CN115907136B - Electric automobile dispatching method, device, equipment and computer readable medium - Google Patents
Electric automobile dispatching method, device, equipment and computer readable medium Download PDFInfo
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- CN115907136B CN115907136B CN202211442677.1A CN202211442677A CN115907136B CN 115907136 B CN115907136 B CN 115907136B CN 202211442677 A CN202211442677 A CN 202211442677A CN 115907136 B CN115907136 B CN 115907136B
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Abstract
The embodiment of the disclosure discloses an electric automobile dispatching method, an electric automobile dispatching device, electric automobile dispatching equipment and a computer readable medium. One embodiment of the method comprises the following steps: acquiring a photovoltaic output power information sequence of a photovoltaic power station in a preset time period; acquiring an initial electricity load information sequence of an electric automobile sequence in a preset time period; inputting the photovoltaic output power information sequence into a photovoltaic output power prediction model trained in advance to obtain predicted photovoltaic output power information; inputting the predicted photovoltaic output power information into a pre-trained electricity load information prediction model to obtain predicted electricity load information; inputting the predicted power load information and the predicted photovoltaic output power information into a pre-trained electric vehicle quantity prediction model to obtain the predicted quantity of the electric vehicles; and controlling the associated electric automobile dispatching equipment to dispatch the electric automobiles according to the predicted quantity of the electric automobiles. This embodiment may avoid wasting transportation resources.
Description
Technical Field
Embodiments of the present disclosure relate to the field of computer technology, and in particular, to an electric vehicle dispatching method, apparatus, device, and computer readable medium.
Background
The electric automobile is scheduled to charge the photovoltaic power station, redundant photovoltaic output power of the photovoltaic power station can be received, and the photovoltaic utilization rate can be improved. At present, the electric automobile is scheduled in the following general modes: and taking the historical number of electric vehicles as the predicted number of the electric vehicles, or manually calculating the predicted number of the electric vehicles only according to the initial power load information, and then dispatching the electric vehicle dispatching equipment to transport the electric vehicles with the predicted number of the electric vehicles to the photovoltaic power station.
However, the following technical problems generally exist in the above manner:
firstly, the predicted quantity of the electric vehicles is manually calculated, the accuracy of the obtained predicted quantity of the electric vehicles is low, the electric vehicles are required to be repeatedly scheduled by the electric vehicle scheduling equipment to be transported, and transportation resources are wasted;
secondly, only the initial power load information is considered, the relation between the initial power load information and the photovoltaic output power is not considered, the number accuracy of the scheduled electric vehicles is easy to be low, and when the number of the scheduled electric vehicles is large, the electric vehicle scheduling equipment needs to be scheduled repeatedly to transport the electric vehicles, so that transport resources are wasted;
thirdly, the historical number of electric vehicles is used as the predicted number of the electric vehicles, so that the accuracy of the predicted number of the electric vehicles is low, and when the number of the scheduled electric vehicles is low, the waste of photovoltaic output power is easy to cause.
The above information disclosed in this background section is only for enhancement of understanding of the background of the inventive concept and, therefore, may contain information that does not form the prior art that is already known to those of ordinary skill in the art in this country.
Disclosure of Invention
The disclosure is in part intended to introduce concepts in a simplified form that are further described below in the detailed description. The disclosure is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Some embodiments of the present disclosure propose an electric vehicle scheduling method, apparatus, electronic device, and computer readable medium to solve one or more of the technical problems mentioned in the background section above.
In a first aspect, some embodiments of the present disclosure provide an electric vehicle scheduling method, the method including: acquiring a photovoltaic output power information sequence of a photovoltaic power station in a preset time period; acquiring an initial electricity load information sequence of the electric automobile sequence in the preset time period, wherein initial electricity load information in the initial electricity load information sequence corresponds to photovoltaic output power information in the photovoltaic output power information sequence, and one initial electricity load information represents total electricity load corresponding to the electric automobile sequence; inputting the photovoltaic output power information sequence into a photovoltaic output power prediction model trained in advance to obtain predicted photovoltaic output power information; inputting the predicted photovoltaic output power information into a pre-trained electricity load information prediction model to obtain predicted electricity load information; inputting the predicted power load information and the predicted photovoltaic output power information into a pre-trained electric vehicle quantity prediction model to obtain the predicted quantity of the electric vehicles; and controlling the associated electric automobile dispatching equipment to dispatch the electric automobiles according to the predicted quantity of the electric automobiles.
In a second aspect, some embodiments of the present disclosure provide an electric vehicle dispatching apparatus, the apparatus including: the photovoltaic power generation system comprises a first acquisition unit, a second acquisition unit and a control unit, wherein the first acquisition unit is configured to acquire a photovoltaic output power information sequence of a photovoltaic power station in a preset time period; the second acquisition unit is configured to acquire an initial electricity load information sequence of the electric automobile sequence within the preset time period, wherein initial electricity load information in the initial electricity load information sequence corresponds to photovoltaic output power information in the photovoltaic output power information sequence, and one initial electricity load information represents total electricity load corresponding to the electric automobile sequence; the first input unit is configured to input the photovoltaic output power information sequence into a photovoltaic output power prediction model trained in advance to obtain predicted photovoltaic output power information; the second input unit is configured to input the predicted photovoltaic output power information into a pre-trained electricity load information prediction model to obtain predicted electricity load information; the third input unit is configured to input the predicted electricity load information and the predicted photovoltaic output power information into a pre-trained electric automobile quantity prediction model to obtain the predicted quantity of the electric automobiles; and the control unit is configured to control the associated electric automobile dispatching equipment to dispatch the electric automobiles according to the predicted quantity of the electric automobiles.
In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors causes the one or more processors to implement the method described in any of the implementations of the first aspect above.
In a fourth aspect, some embodiments of the present disclosure provide a computer readable medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.
The above embodiments of the present disclosure have the following advantageous effects: by the electric automobile scheduling method, waste of transportation resources can be avoided. Specifically, the reason for wasting transportation resources is that: the prediction quantity of the electric vehicles is manually calculated, the accuracy of the obtained prediction quantity of the electric vehicles is low, and the electric vehicles are required to be transported by the electric vehicle dispatching equipment in a repeated dispatching mode. Based on this, in the electric vehicle scheduling method of some embodiments of the present disclosure, first, a photovoltaic output power information sequence of a photovoltaic power station in a preset time period is obtained. And secondly, acquiring an initial electricity load information sequence of the electric automobile sequence in the preset time period. The initial power consumption load information in the initial power consumption load information sequence corresponds to the photovoltaic output power information in the photovoltaic output power information sequence, and one initial power consumption load information represents the total power consumption load corresponding to the electric automobile sequence. And then, inputting the photovoltaic output power information sequence into a photovoltaic output power prediction model trained in advance to obtain predicted photovoltaic output power information. Therefore, accurate predicted photovoltaic output power information can be obtained according to the photovoltaic output power prediction model, so that the number of electric vehicles can be predicted subsequently. And then, inputting the predicted photovoltaic output power information into a pre-trained electricity load information prediction model to obtain predicted electricity load information. Therefore, more accurate prediction electricity load information can be obtained according to the electricity load information prediction model, so that the number of electric vehicles can be predicted later. And then, inputting the predicted electric load information and the predicted photovoltaic output power information into a pre-trained electric vehicle quantity prediction model to obtain the predicted quantity of the electric vehicles. Therefore, more accurate predicted photovoltaic output power information and more accurate predicted electricity load information can be input into the electric automobile quantity prediction model, and more accurate electric automobile prediction quantity is obtained. And finally, controlling the associated electric automobile dispatching equipment to dispatch the electric automobiles according to the predicted quantity of the electric automobiles. Thus, the associated electric vehicle dispatching equipment can be controlled to dispatch the accurate number of electric vehicles. Therefore, the repeated dispatching of the electric automobile dispatching equipment to transport the electric automobile can be avoided. Thus, waste of transportation resources can be avoided.
Drawings
The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale.
FIG. 1 is a flow chart of some embodiments of an electric vehicle scheduling method according to the present disclosure;
FIG. 2 is a schematic structural view of some embodiments of an electric vehicle dispatching device according to the present disclosure;
fig. 3 is a schematic structural diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.
It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings. Embodiments of the present disclosure and features of embodiments may be combined with each other without conflict.
It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.
The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Referring to fig. 1, a flow 100 of some embodiments of an electric vehicle scheduling method according to the present disclosure is shown. The electric automobile dispatching method comprises the following steps:
Step 101, acquiring a photovoltaic output power information sequence of a photovoltaic power station in a preset time period.
In some embodiments, an execution body (for example, a computing device) of the electric automobile scheduling method may acquire a photovoltaic output power information sequence of the photovoltaic power station in a preset time period from the terminal device in a wired connection or wireless connection manner. The photovoltaic power plant may be a power plant that directly converts solar radiation into electrical energy. The photovoltaic output power information in the photovoltaic output power information sequence may be photovoltaic output power information corresponding to a time granularity within a preset time period. The predetermined period of time may be, but is not limited to, one day, one week, one month, three months. The above time granularity may be, but is not limited to, fifteen minutes, one hour, one day, one week. The photovoltaic output power information can represent the output power of the photovoltaic power station.
Step 102, acquiring an initial electricity load information sequence of the electric automobile sequence in the preset time period.
In some embodiments, the executing body may acquire the initial power load information sequence of the electric automobile sequence in the preset time period from the terminal device through a wired connection or a wireless connection. The initial power consumption load information in the initial power consumption load information sequence corresponds to the photovoltaic output power information in the photovoltaic output power information sequence, and one initial power consumption load information can represent the total power consumption load corresponding to the electric automobile sequence. The initial electricity load information sequence may be initial electricity load information corresponding to a time granularity within the preset time period. The above-described electric car sequence may include individual electric cars charged at a photovoltaic power plant.
And step 103, inputting the photovoltaic output power information sequence into a photovoltaic output power prediction model trained in advance to obtain predicted photovoltaic output power information.
In some embodiments, the executing entity may input the photovoltaic output power information sequence into a photovoltaic output power prediction model trained in advance, so as to obtain predicted photovoltaic output power information. The pre-trained photovoltaic output power prediction model may be a time series model (e.g., ARIMA model, RNN recurrent neural network model) based on federal learning technology, which is pre-trained with a sequence of photovoltaic output power information as input and with the predicted photovoltaic output power information as output. Here, the federal learning technique may be a distributed machine learning technique. Federal learning techniques can achieve a balance of data privacy protection and data sharing computation.
Alternatively, the pre-trained photovoltaic output power prediction model may be trained by:
first, a training sample set is obtained.
In some embodiments, the executing entity may obtain the training sample set from the terminal device through a wired connection or a wireless connection. Wherein, the training samples in the training sample set include: a sample photovoltaic output power information sequence and sample predicted photovoltaic output power information. The sample photovoltaic output power information in the sample photovoltaic output power information sequence may be photovoltaic output power information corresponding to a time granularity within a preset time period. The sample predicted photovoltaic output power information may be photovoltaic output power information corresponding to a next time granularity of the preset time granularity. Here, the preset time granularity may be a time granularity corresponding to the last sample photovoltaic output power information in the sample photovoltaic output power information sequence. For example, when the preset time period is 2022, 1-2022, 2-1, and the time granularity is one day, the sample predicted photovoltaic output power information may be photovoltaic output power information corresponding to 2022, 2. When the preset time period is 2022, 1, 10 points, 2022, 1, 2, 10 points, and the time granularity is one hour, the sample predicted photovoltaic output power information may be photovoltaic output power information corresponding to 2022, 1, 2, 11 points.
And secondly, selecting training samples from the training sample set.
In some embodiments, the executing entity may select a training sample from the training sample set. In practice, the executing entity may randomly select training samples from the training sample set.
And thirdly, inputting a sample photovoltaic output power information sequence included in the training sample into an initial photovoltaic output power prediction model to obtain initial predicted photovoltaic output power information.
In some embodiments, the executing body may input a sample photovoltaic output power information sequence included in the training sample into an initial photovoltaic output power prediction model to obtain initial predicted photovoltaic output power information. The initial photovoltaic output power prediction model may be an untrained federal learning technique-based time series model (e.g., ARIMA model, RNN recurrent neural network model), among others.
Fourth, based on a preset first loss function, determining a predicted photovoltaic output power difference value between the initial predicted photovoltaic output power information and sample predicted photovoltaic output power information included in the training sample.
In some embodiments, the executing entity may determine a predicted photovoltaic output power difference value between the initial predicted photovoltaic output power information and sample predicted photovoltaic output power information included in the training sample based on a preset first loss function. The preset first loss function may be, but is not limited to: mean square error loss function (MSE), hinge loss function (SVM), cross entropy loss function (CrossEntropy), 0-1 loss function, absolute value loss function, log loss function, square loss function, exponential loss function, etc.
And seventhly, adjusting network parameters of the initial photovoltaic output power prediction model based on the predicted photovoltaic output power difference value.
In some embodiments, the executing entity may adjust network parameters of the initial photovoltaic output power prediction model based on the predicted photovoltaic output power difference value. In practice, the executing entity may adjust the network parameters of the initial photovoltaic output power prediction model in response to determining that the predicted photovoltaic output power difference value does not satisfy a preset predicted photovoltaic output power condition. The preset predicted photovoltaic output power condition may be that the predicted photovoltaic output power difference value is less than or equal to a preset predicted photovoltaic output power difference value. For example, the above-described predicted photovoltaic output power difference value and the preset predicted photovoltaic output power difference value may be differenced. On this basis, the error value is transmitted forward from the last layer of the model by using back propagation, random gradient descent and the like to adjust the parameters of each layer. Of course, a network freezing (dropout) method may be used as needed, and network parameters of some layers therein may be kept unchanged and not adjusted, which is not limited in any way. The setting of the preset predicted photovoltaic output power difference value is not limited, and for example, the preset predicted photovoltaic output power difference value may be 0.1.
Optionally, in response to determining that the predicted photovoltaic output power difference value meets a preset predicted photovoltaic output power condition, determining the initial photovoltaic output power prediction model as a trained photovoltaic output power prediction model.
In some embodiments, the executing entity may determine the initial photovoltaic output power prediction model as a trained photovoltaic output power prediction model in response to determining that the predicted photovoltaic output power difference value satisfies a preset predicted photovoltaic output power condition.
And 104, inputting the predicted photovoltaic output power information into a pre-trained electricity load information prediction model to obtain predicted electricity load information.
In some embodiments, the executing entity may input the predicted photovoltaic output power information into a pre-trained electricity load information prediction model to obtain predicted electricity load information. The pre-trained electricity load information prediction model can be a linear regression model taking photovoltaic output power information as input and electricity load information as output. For example, the pre-trained electrical load information prediction model may be a unitary linear regression model.
Optionally, inputting each piece of photovoltaic output power information in the photovoltaic output power information sequence into the electricity load information prediction model to generate electricity load information, so as to obtain an electricity load information sequence.
In some embodiments, the executing entity may input each photovoltaic output power information in the photovoltaic output power information sequence into the electrical load information prediction model to generate electrical load information, so as to obtain an electrical load information sequence. One photovoltaic output power information corresponds to one electric load information.
Alternatively, the pre-trained electrical load information prediction model may be trained by:
and step one, selecting photovoltaic output power information from the photovoltaic output power information sequence.
In some embodiments, the executing entity may select the photovoltaic output power information from the sequence of photovoltaic output power information. In practice, the executing entity may randomly select the photovoltaic output power information from the photovoltaic output power information sequence.
And secondly, inputting the photovoltaic output power information into an initial electricity load information prediction model to obtain initial predicted electricity load information.
In some embodiments, the executing entity may input the photovoltaic output power information into an initial electrical load information prediction model to obtain initial predicted electrical load information. The initial photovoltaic output power prediction model may be an untrained linear regression model, among other things. For example, the initial photovoltaic output power prediction model may be a unitary linear regression model.
And thirdly, determining an electricity load difference value of the initial electricity load information corresponding to the initial predicted electricity load information and the photovoltaic output power information based on a preset second loss function.
In some embodiments, the executing entity may determine a power consumption load difference value of the initial power consumption load information corresponding to the photovoltaic output power information based on a preset second loss function. The preset second loss function may be, but is not limited to: mean square error loss function (MSE), hinge loss function (SVM), cross entropy loss function (CrossEntropy), 0-1 loss function, absolute value loss function, log loss function, square loss function, exponential loss function, etc.
And step four, adjusting parameters of the initial electricity load information prediction model based on the electricity load difference value.
In some embodiments, the execution body may adjust parameters of the initial electrical load information prediction model based on the electrical load difference value. In practice, the executing entity may adjust parameters of the initial electrical load information prediction model in response to determining that the electrical load difference value does not satisfy a preset electrical load condition. The preset electrical load condition may be that the electrical load difference value is less than or equal to a preset electrical load difference value. For example, the above-described predicted photovoltaic output power difference value and the preset predicted photovoltaic output power difference value may be differenced. On the basis, the parameters of the initial electricity load information prediction model are adjusted by using methods such as back propagation, gradient descent and the like. It should be noted that the back propagation algorithm and the gradient descent method are well known techniques widely studied and applied at present, and will not be described herein. The setting of the preset electrical load difference value is not limited, and the preset electrical load difference value may be 0.1, for example.
The optional technical content in step 104 is taken as an invention point of the embodiment of the present disclosure, and solves the second technical problem mentioned in the background art, namely "transportation resources are wasted". Factors that waste transportation resources are often as follows: only the initial power load information is considered, the relation between the initial power load information and the photovoltaic output power is not considered, the number accuracy of the scheduled electric vehicles is low easily, and when the number of the scheduled electric vehicles is large, the electric vehicle scheduling equipment needs to be scheduled repeatedly to transport the electric vehicles. If the above factors are solved, an effect that waste of transportation resources can be avoided can be achieved. To achieve this, first, photovoltaic output power information is selected from the sequence of photovoltaic output power information described above. And secondly, inputting the photovoltaic output power information into an initial electricity load information prediction model to obtain initial predicted electricity load information. Thus, the initial predicted electricity load information output by the initial electricity load information prediction model can be obtained, so that the initial electricity load information prediction model is optimized later. And then, determining a power consumption load difference value of the initial power consumption load information corresponding to the initial predicted power consumption load information and the photovoltaic output power information based on a preset second loss function. Therefore, the power consumption load difference value can be obtained according to the preset second loss function, so that the initial power consumption load information prediction model can be optimized later. Finally, based on the power consumption load difference value, parameters of the initial power consumption load information prediction model are adjusted. Therefore, parameters of the initial electricity load information prediction model can be continuously adjusted according to the electricity load difference value, so that accurate electricity load information can be obtained. Therefore, the number of the electric vehicles can be more accurate, and the electric vehicles can be prevented from being transported by repeatedly dispatching the electric vehicle dispatching equipment. Thus, waste of transportation resources can be avoided.
Optionally, in response to determining that the electrical load difference value meets a preset electrical load condition, determining the initial electrical load information prediction model as a trained electrical load information prediction model.
In some embodiments, the executing entity may determine the initial electrical load information prediction model as a trained electrical load information prediction model in response to determining that the electrical load difference value satisfies a preset electrical load condition.
And 105, inputting the predicted electric load information and the predicted photovoltaic output power information into a pre-trained electric vehicle quantity prediction model to obtain the predicted quantity of the electric vehicles.
In some embodiments, the execution subject may input the predicted electrical load information and the predicted photovoltaic output power information into a pre-trained electric vehicle number prediction model to obtain a predicted number of electric vehicles. The pre-trained electric vehicle number prediction model may be a predefined model with predicted electric load information and predicted photovoltaic output power information as inputs and the predicted electric vehicle number as output. The predefined model can be divided into three layers:
The first layer may be an input layer for passing the predicted electrical load information and the predicted photovoltaic output power information to the second layer.
The second layer may include: a first sub-model and a second sub-model. The first sub-model may be a multi-layer feedforward neural network model trained according to an error back propagation algorithm with the predicted electrical load information and the predicted photovoltaic output power information as inputs and the predicted number of the first electric vehicles as outputs. The second sub-model may be a differential integration moving average autoregressive model with the predicted electrical load information and the predicted photovoltaic output power information as inputs and the predicted quantity of the second electric vehicle as outputs.
The third layer may be an output layer for receiving the outputs of the first sub-model and the second sub-model, respectively, and taking weighted results of the outputs of the first sub-model and the second sub-model as the output of the entire predefined model. For example, first, the execution body may determine a product of the first predicted number of electric vehicles and the first weight as the first weight predicted number of electric vehicles. The first weight may be a preset weight corresponding to the predicted number of the first electric automobile. For example, the first weight may be 0.5. Then, the executing body may determine a product of the second predicted number of electric vehicles and the second weight as the second weight predicted number of electric vehicles. The second weight may be a preset weight corresponding to the predicted number of the second electric automobile. For example, the second weight may be 0.5. And then, the execution body can determine the sum of the predicted number of the electric vehicles with the first weight and the predicted number of the electric vehicles with the second weight as the predicted number of the electric vehicles. Finally, the execution subject may take the predicted number of electric vehicles as the output of the entire predefined model.
Alternatively, the pre-trained electric vehicle number prediction model may be trained by:
firstly, acquiring a sample electric vehicle number sequence in the preset time period.
In some embodiments, the execution body may acquire the sample electric vehicle number sequence in the preset time period from the terminal device through a wired connection or a wireless connection. The number of the sample electric vehicles in the sample electric vehicle number sequence corresponds to photovoltaic output power information corresponding to the photovoltaic output power information sequence. The number of the sample electric vehicles in the sample electric vehicle number sequence may be the number of each electric vehicle included in the corresponding electric vehicle sequence.
And secondly, selecting photovoltaic output power information from the photovoltaic output power information sequence.
In some embodiments, the executing entity may select the photovoltaic output power information from the sequence of photovoltaic output power information. In practice, the executing entity may randomly select the photovoltaic output power information from the photovoltaic output power information sequence.
And thirdly, inputting the photovoltaic output power information and the power load information corresponding to the photovoltaic output power information into an initial electric vehicle quantity prediction model to obtain an initial electric vehicle quantity.
In some embodiments, the executing body may input the photovoltaic output power information and the power load information corresponding to the photovoltaic output power information into an initial electric vehicle number prediction model to obtain an initial electric vehicle predicted number. The initial electric vehicle number prediction model may be an untrained predefined model with predicted electric load information and predicted photovoltaic output power information as inputs and the predicted electric vehicle number as output.
And step four, determining an electric vehicle predicted quantity difference value between the initial electric vehicle predicted quantity and the sample electric vehicle quantity corresponding to the photovoltaic output power information based on a preset third loss function.
In some embodiments, based on a third predetermined loss function, the executing body may determine an electric vehicle predicted quantity difference value between the initial electric vehicle predicted quantity and the sample electric vehicle quantity corresponding to the photovoltaic output power information. The third loss function may be, but is not limited to: mean square error loss function (MSE), hinge loss function (SVM), cross entropy loss function (CrossEntropy), 0-1 loss function, absolute value loss function, log loss function, square loss function, exponential loss function, etc.
And fifthly, adjusting network parameters of the initial electric vehicle quantity prediction model based on the electric vehicle quantity prediction difference value.
In some embodiments, the executing entity may adjust the network parameters of the initial electric vehicle quantity prediction model based on the electric vehicle quantity prediction difference value. In practice, the executing body may adjust the network parameters of the initial electric vehicle quantity prediction model in response to determining that the electric vehicle quantity prediction difference value does not satisfy a preset electric vehicle quantity prediction condition. The preset electric vehicle predicted quantity condition may be that the electric vehicle predicted quantity difference value is less than or equal to a preset electric vehicle predicted quantity difference value. For example, the difference value may be obtained between the predicted number difference value of the electric vehicle and the predicted number difference value of the preset electric vehicle. On this basis, the error value is transmitted forward from the last layer of the model by using back propagation, random gradient descent and the like to adjust the parameters of each layer. Of course, a network freezing (dropout) method may be used as needed, and network parameters of some layers therein may be kept unchanged and not adjusted, which is not limited in any way. The setting of the preset electric vehicle predicted quantity difference value is not limited, and for example, the preset electric vehicle predicted quantity difference value may be 0.1.
The optional technical content in step 105 is taken as an invention point of the embodiment of the present disclosure, which solves the third technical problem mentioned in the background art, namely that when the number of the scheduled electric vehicles is small, the waste of the photovoltaic output power is easy to be caused. Factors that easily cause waste of photovoltaic output power tend to be as follows: the historical number of electric vehicles is used as the predicted number of the electric vehicles, so that the accuracy of the predicted number of the electric vehicles is low. If the above factors are solved, the effect of avoiding the waste of the output power of the photovoltaic can be achieved. To achieve this effect, first, a series of the number of sample electric vehicles within the above-described preset period of time is acquired. Therefore, the number of the electric steam which is scheduled in the history can be obtained. And selecting photovoltaic output power information from the photovoltaic output power information sequence. And then, inputting the photovoltaic output power information and the power load information corresponding to the photovoltaic output power information into an initial electric vehicle quantity prediction model to obtain an initial electric vehicle quantity. Therefore, an electric vehicle quantity prediction model can be established according to the relation among the photovoltaic output power information, the electricity load information and the electric vehicle quantity prediction, so that the accurate electric vehicle quantity prediction can be obtained later. And then, based on a preset third loss function, determining an electric vehicle predicted quantity difference value between the initial electric vehicle predicted quantity and the sample electric vehicle quantity corresponding to the photovoltaic output power information. Therefore, the difference value of the predicted quantity of the electric vehicles can be calculated according to the preset third loss function, so that the network parameters of the predicted quantity of the electric vehicles can be adjusted subsequently. And finally, based on the electric vehicle predicted quantity difference value, adjusting network parameters of the initial electric vehicle quantity prediction model. Therefore, the network parameters of the electric vehicle quantity prediction model can be adjusted based on the electric vehicle quantity prediction difference value, so that more accurate electric vehicle quantity predictions can be output. Therefore, the accurate number of electric vehicles can be scheduled, and the waste of photovoltaic output power can be avoided.
Optionally, in response to determining that the predicted quantity difference value of the electric vehicles meets a preset predicted quantity condition of the electric vehicles, determining the initial electric vehicle quantity prediction model as a trained electric vehicle quantity prediction model.
In some embodiments, the executing body may determine the initial electric vehicle number prediction model as the trained electric vehicle number prediction model in response to determining that the electric vehicle number prediction difference value satisfies a preset electric vehicle number prediction condition.
And step 106, controlling the associated electric automobile dispatching equipment to dispatch the electric automobiles according to the predicted quantity of the electric automobiles.
In some embodiments, the execution body may control the associated electric vehicle dispatching device to perform electric vehicle dispatching according to the predicted number of electric vehicles. The associated electric vehicle dispatching device may be a transport device for transporting the electric vehicle sequence. For example, the electric car dispatching device may be a car. In practice, the execution body may control the associated electric vehicle dispatching device to transport the predicted number of electric vehicles to the photovoltaic power plant.
The above embodiments of the present disclosure have the following advantageous effects: by the electric automobile scheduling method, waste of transportation resources can be avoided. Specifically, the reason for wasting transportation resources is that: the prediction quantity of the electric vehicles is manually calculated, the accuracy of the obtained prediction quantity of the electric vehicles is low, and the electric vehicles are required to be transported by the electric vehicle dispatching equipment in a repeated dispatching mode. Based on this, in the electric vehicle scheduling method of some embodiments of the present disclosure, first, a photovoltaic output power information sequence of a photovoltaic power station in a preset time period is obtained. And secondly, acquiring an initial electricity load information sequence of the electric automobile sequence in the preset time period. The initial power consumption load information in the initial power consumption load information sequence corresponds to the photovoltaic output power information in the photovoltaic output power information sequence, and one initial power consumption load information represents the total power consumption load corresponding to the electric automobile sequence. And then, inputting the photovoltaic output power information sequence into a photovoltaic output power prediction model trained in advance to obtain predicted photovoltaic output power information. Therefore, accurate predicted photovoltaic output power information can be obtained according to the photovoltaic output power prediction model, so that the number of electric vehicles can be predicted subsequently. And then, inputting the predicted photovoltaic output power information into a pre-trained electricity load information prediction model to obtain predicted electricity load information. Therefore, more accurate prediction electricity load information can be obtained according to the electricity load information prediction model, so that the number of electric vehicles can be predicted later. And then, inputting the predicted electric load information and the predicted photovoltaic output power information into a pre-trained electric vehicle quantity prediction model to obtain the predicted quantity of the electric vehicles. Therefore, more accurate predicted photovoltaic output power information and more accurate predicted electricity load information can be input into the electric automobile quantity prediction model, and more accurate electric automobile prediction quantity is obtained. And finally, controlling the associated electric automobile dispatching equipment to dispatch the electric automobiles according to the predicted quantity of the electric automobiles. Thus, the associated electric vehicle dispatching equipment can be controlled to dispatch the accurate number of electric vehicles. Therefore, the repeated dispatching of the electric automobile dispatching equipment to transport the electric automobile can be avoided. Thus, waste of transportation resources can be avoided.
With further reference to fig. 2, as an implementation of the method shown in the foregoing figures, the present disclosure provides some embodiments of an electric vehicle dispatching device, which correspond to those method embodiments shown in fig. 1, and which may be applied to various electronic devices in particular.
As shown in fig. 2, the electric automobile dispatch device 200 of some embodiments includes: a first acquisition unit 201, a second acquisition unit 202, a first input unit 203, a second input unit 204, a third input unit 205, and a control unit 206. Wherein, the first obtaining unit 201 is configured to obtain a photovoltaic output power information sequence of the photovoltaic power plant in a preset time period; a second obtaining unit 202, configured to obtain an initial power consumption load information sequence of the electric vehicle sequence within the preset time period, where initial power consumption load information in the initial power consumption load information sequence corresponds to photovoltaic output power information in the photovoltaic output power information sequence, and one initial power consumption load information represents a total power consumption load corresponding to the electric vehicle sequence; a first input unit 203 configured to input the above-mentioned photovoltaic output power information sequence into a photovoltaic output power prediction model trained in advance, to obtain predicted photovoltaic output power information; a second input unit 204 configured to input the predicted photovoltaic output power information into a pre-trained electricity load information prediction model to obtain predicted electricity load information; a third input unit 205 configured to input the predicted electric load information and the predicted photovoltaic output power information into a pre-trained electric vehicle number prediction model to obtain a predicted number of electric vehicles; and the control unit 206 is configured to control the associated electric automobile dispatching equipment to dispatch the electric automobiles according to the predicted quantity of the electric automobiles.
It will be appreciated that the elements described in the electric vehicle dispatching device 200 correspond to the various steps in the method described with reference to fig. 1. Thus, the operations, features, and beneficial effects described above with respect to the method are equally applicable to the electric vehicle dispatching device 200 and the units contained therein, and are not described herein.
Referring now to FIG. 3, a schematic diagram of an electronic device (e.g., computing device) 300 suitable for use in implementing some embodiments of the present disclosure is shown. The electronic devices in some embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), car terminals (e.g., car navigation terminals), and the like, as well as stationary terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 3 is merely an example and should not impose any limitations on the functionality and scope of use of embodiments of the present disclosure.
As shown in fig. 3, the electronic device 300 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 301 that may perform various suitable actions and processes in accordance with a program stored in a Read Only Memory (ROM) 302 or a program loaded from a storage means 308 into a Random Access Memory (RAM) 303. In the RAM303, various programs and data required for the operation of the electronic apparatus 300 are also stored. The processing device 301, the ROM302, and the RAM303 are connected to each other via a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.
In general, the following devices may be connected to the I/O interface 305: input devices 306 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 307 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 308 including, for example, magnetic tape, hard disk, etc.; and communication means 309. The communication means 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. While fig. 3 shows an electronic device 300 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead. Each block shown in fig. 3 may represent one device or a plurality of devices as needed.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via communications device 309, or from storage device 308, or from ROM 302. The above-described functions defined in the methods of some embodiments of the present disclosure are performed when the computer program is executed by the processing means 301.
It should be noted that, the computer readable medium described in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, the computer-readable signal medium may comprise a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
In some implementations, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring a photovoltaic output power information sequence of a photovoltaic power station in a preset time period; acquiring an initial electricity load information sequence of the electric automobile sequence in the preset time period, wherein initial electricity load information in the initial electricity load information sequence corresponds to photovoltaic output power information in the photovoltaic output power information sequence, and one initial electricity load information represents total electricity load corresponding to the electric automobile sequence; inputting the photovoltaic output power information sequence into a photovoltaic output power prediction model trained in advance to obtain predicted photovoltaic output power information; inputting the predicted photovoltaic output power information into a pre-trained electricity load information prediction model to obtain predicted electricity load information; inputting the predicted power load information and the predicted photovoltaic output power information into a pre-trained electric vehicle quantity prediction model to obtain the predicted quantity of the electric vehicles; and controlling the associated electric automobile dispatching equipment to dispatch the electric automobiles according to the predicted quantity of the electric automobiles.
Computer program code for carrying out operations for some embodiments of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in some embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. The described units may also be provided in a processor, for example, described as: a processor includes a first acquisition unit, a second acquisition unit, a first input unit, a second input unit, a third input unit, and a control unit. The names of these units do not in any way constitute a limitation of the unit itself, for example the first acquisition unit may also be described as "acquisition of a sequence of photovoltaic output power information of a photovoltaic power plant over a preset period of time".
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above technical features, but encompasses other technical features formed by any combination of the above technical features or their equivalents without departing from the spirit of the invention. Such as the above-described features, are mutually substituted with (but not limited to) the features having similar functions disclosed in the embodiments of the present disclosure.
Claims (4)
1. An electric automobile dispatching method, comprising:
acquiring a photovoltaic output power information sequence of a photovoltaic power station in a preset time period;
acquiring an initial electricity load information sequence of an electric automobile sequence in the preset time period, wherein initial electricity load information in the initial electricity load information sequence corresponds to photovoltaic output power information in the photovoltaic output power information sequence, one initial electricity load information represents total electricity load corresponding to the electric automobile sequence, the initial electricity load information sequence is initial electricity load information corresponding to time granularity in the preset time period, and the electric automobile sequence comprises electric automobiles charged in a photovoltaic power station;
inputting the photovoltaic output power information sequence into a photovoltaic output power prediction model trained in advance to obtain predicted photovoltaic output power information;
inputting the predicted photovoltaic output power information into a pre-trained electricity load information prediction model to obtain predicted electricity load information;
inputting the predicted power load information and the predicted photovoltaic output power information into a pre-trained electric vehicle quantity prediction model to obtain the predicted quantity of the electric vehicles;
Controlling associated electric automobile dispatching equipment to dispatch electric automobiles according to the predicted quantity of the electric automobiles;
inputting each piece of photovoltaic output power information in the photovoltaic output power information sequence into the electricity load information prediction model to generate electricity load information, and obtaining an electricity load information sequence, wherein one piece of photovoltaic output power information corresponds to one piece of electricity load information;
the pre-trained photovoltaic output power prediction model is obtained through training by the following steps:
obtaining a training sample set, wherein training samples in the training sample set comprise: the method comprises the steps of a sample photovoltaic output power information sequence and sample prediction photovoltaic output power information;
selecting a training sample from the training sample set;
inputting a sample photovoltaic output power information sequence included in the training sample into an initial photovoltaic output power prediction model to obtain initial predicted photovoltaic output power information;
determining a predicted photovoltaic output power difference value between the initial predicted photovoltaic output power information and sample predicted photovoltaic output power information included in the training sample based on a preset first loss function;
Based on the predicted photovoltaic output power difference value, adjusting network parameters of the initial photovoltaic output power prediction model;
and in response to determining that the predicted photovoltaic output power difference value meets a preset predicted photovoltaic output power condition, determining the initial photovoltaic output power prediction model as a trained photovoltaic output power prediction model.
2. An electric car dispatching device, comprising:
the photovoltaic power generation system comprises a first acquisition unit, a second acquisition unit and a control unit, wherein the first acquisition unit is configured to acquire a photovoltaic output power information sequence of a photovoltaic power station in a preset time period;
the second acquisition unit is configured to acquire an initial electricity load information sequence of the electric automobile sequence in the preset time period, wherein initial electricity load information in the initial electricity load information sequence corresponds to photovoltaic output power information in the photovoltaic output power information sequence, one initial electricity load information represents total electricity load corresponding to the electric automobile sequence, the initial electricity load information sequence is initial electricity load information corresponding to time granularity in the preset time period, and the electric automobile sequence comprises electric automobiles charged in a photovoltaic power station;
the first input unit is configured to input the photovoltaic output power information sequence into a photovoltaic output power prediction model trained in advance to obtain predicted photovoltaic output power information;
The second input unit is configured to input the predicted photovoltaic output power information into a pre-trained electricity load information prediction model to obtain predicted electricity load information;
the third input unit is configured to input the predicted electricity load information and the predicted photovoltaic output power information into a pre-trained electric automobile quantity prediction model to obtain the predicted quantity of the electric automobiles;
the control unit is configured to control the associated electric automobile dispatching equipment to dispatch the electric automobiles according to the predicted quantity of the electric automobiles;
wherein the electric vehicle dispatching device is further configured to:
inputting each piece of photovoltaic output power information in the photovoltaic output power information sequence into the electricity load information prediction model to generate electricity load information, and obtaining an electricity load information sequence, wherein one piece of photovoltaic output power information corresponds to one piece of electricity load information;
the pre-trained photovoltaic output power prediction model is obtained through training by the following steps:
obtaining a training sample set, wherein training samples in the training sample set comprise: the method comprises the steps of a sample photovoltaic output power information sequence and sample prediction photovoltaic output power information;
Selecting a training sample from the training sample set;
inputting a sample photovoltaic output power information sequence included in the training sample into an initial photovoltaic output power prediction model to obtain initial predicted photovoltaic output power information;
determining a predicted photovoltaic output power difference value between the initial predicted photovoltaic output power information and sample predicted photovoltaic output power information included in the training sample based on a preset first loss function;
based on the predicted photovoltaic output power difference value, adjusting network parameters of the initial photovoltaic output power prediction model;
wherein the electric vehicle dispatching device is further configured to:
and in response to determining that the predicted photovoltaic output power difference value meets a preset predicted photovoltaic output power condition, determining the initial photovoltaic output power prediction model as a trained photovoltaic output power prediction model.
3. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of claim 1.
4. A computer readable medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the method of claim 1.
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