CN112526888A - Regional concentrated tail end load prediction method - Google Patents
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Abstract
The invention provides a regional concentrated tail end load prediction method, which comprises the following steps: acquiring historical load data of a user; establishing a load model of a typical building according to historical load data; acquiring weather data in a target area, inputting the weather data into a load model, and predicting a load at preset time by using the load model to obtain a load predicted value at preset time; acquiring water supply equipment information currently applied to the target area; calculating a target gap according to the water supply equipment information and the load predicted value of the preset time; increasing or decreasing the capacity of the water supply equipment according to the target gap; and obtaining the optimization result of the equipment type selection and the capacity configuration. By adopting the prediction method provided by the invention, the load model can be verified by utilizing the predicted value of the preset time and the actual value of the preset time, the accurate value is gradually approached, and the more the running time of the load model provided by the scheme is longer, the more accurate the predicted value is.
Description
Technical Field
The invention relates to the technical field of load prediction, in particular to a method for predicting regional concentrated tail end load.
Background
With the increasing global environmental pollution and the rapid maturation of renewable energy power generation technology, the comprehensive energy system has the advantages of gradient utilization of energy, efficient consumption of renewable energy and the like, and is receiving more and more attention.
The comprehensive energy system is characterized in that advanced physical information technology and innovative management modes are utilized in a certain area, multiple energy sources such as coal, petroleum, natural gas, electric energy and heat energy in the area are integrated, and coordinated planning, optimized operation, cooperative management, interactive response and complementary mutual assistance among multiple heterogeneous energy subsystems are achieved. The energy utilization efficiency is effectively improved and the sustainable development of energy is promoted while the diversified energy utilization requirements in the system are met.
The comprehensive energy system is a system capable of providing various energy services for energy users at the same time, and is an actual physical carrier of an energy Internet. But the coupling of the energy system is enhanced due to the incorporation of various energy sources such as cold, heat, electricity and the like; secondly, energy production and consumption are rapidly marketed, the real-time performance requirements of the comprehensive energy system on energy scheduling are higher and higher, and the requirements on the accuracy and the reliability of the multi-element load prediction of the comprehensive energy system are higher, so that the accurate multi-element load prediction becomes the key for realizing the economic operation and the optimized scheduling of the comprehensive energy system.
The multi-element load prediction precision of the existing load prediction mode comprehensive energy system is low. Therefore, how to accurately predict the multi-element load of the comprehensive energy system becomes an urgent problem to be solved.
Disclosure of Invention
In order to solve the technical problem, the invention provides a method for predicting the load of the concentrated tail end of a region, which has the following specific technical scheme:
the embodiment of the invention provides a method for predicting regional concentrated tail end load, which comprises the following steps:
acquiring historical load data and historical weather data of a user;
creating a load model of a typical building according to historical load data and historical weather data;
determining a target area, acquiring predicted weather data of the target area within a preset time period, inputting the weather data into a load model, and predicting a load within the preset time period by using the load model to obtain a predicted load value within the preset time period;
acquiring water supply equipment information currently applied to the target area; the water supply equipment information comprises the number of equipment, the model of the equipment, the running load of the equipment and the energy consumption of the equipment;
calculating a target gap according to the water supply equipment information and the load predicted value of the preset time; increasing or decreasing the capacity of the water supply equipment according to the target gap; and obtaining the optimization result of the equipment type selection and the capacity configuration.
Further, the building of the load model of the typical building based on the historical load data comprises:
the load model supports vector machine training; and loading the historical load data into the load model in groups, optimizing the load model, inputting each group of the historical data into the load model in groups, and finally obtaining optimized design parameters to finish the training of the load model.
Further, weather data of the target area in the preset time are input into the trained load model, and the load predicted value in the preset time is obtained through prediction by using the obtained design parameters.
Further, the historical data includes: the total usage amount of the predetermined time period, the load data at different time points, and the average value of the load data at different time points in the predetermined time period.
Further, the water supply equipment comprises a first chilled water pump, a second chilled water pump, a control system, a first refrigeration host, a second refrigeration host, a first water pipeline, a second water pipeline, an external water pipeline, an electromagnetic valve and a water flow sensor; the first refrigeration water pump is used for pumping water in a water supply pipeline to the first refrigeration host, one end of the first water pipeline is communicated with a cold water outlet of the first refrigeration host, the other end of the first water pipeline is communicated with the outward water pipeline, the second refrigeration water pump is used for pumping water in the water supply pipeline to the second refrigeration host, one end of the second water pipeline is communicated with a cold water outlet of the second refrigeration host, the other end of the second water pipeline is communicated with the outward water pipeline, the water flow sensor is used for detecting a water flow value in the outward water pipeline and sending the water flow value to the control system, the control system is respectively connected with the first refrigeration water pump, the second refrigeration water pump and the electromagnetic valve in a control mode, and the electromagnetic valve is used for controlling the on-off of the second water pipeline.
Further, acquiring weather data in the target area, inputting the weather data into a load model, predicting the load at the preset time by using the load model, and obtaining a predicted value of the load at the preset time, the method further comprises:
and comparing the predicted value of the preset time with the actual load value of the preset time, inputting the actual value into the load model, and calibrating the load model.
The embodiment of the invention provides a method for predicting regional concentrated tail end load, which comprises the following steps: acquiring historical load data and historical weather data of a user; creating a load model of a typical building according to historical load data and historical weather data; determining a target area, acquiring predicted weather data of the target area within a preset time period, inputting the weather data into a load model, and predicting a load within the preset time period by using the load model to obtain a predicted load value within the preset time period; acquiring water supply equipment information currently applied to the target area; the water supply equipment information comprises the number of equipment, the model of the equipment, the running load of the equipment and the energy consumption of the equipment; calculating a target gap according to the water supply equipment information and the load predicted value of the preset time; increasing or decreasing the capacity of the water supply equipment according to the target gap; and obtaining the optimization result of the equipment type selection and the capacity configuration. By adopting the prediction method provided by the invention, the load model is created according to the historical load data of the user in a specific time period and the weather prediction data in the preset time in the target area, the load model is combined with the two data, the weather data in the preset time is acquired and input into the load model, the load in the preset time is predicted to obtain a predicted value, and the water supply equipment is equipped by using the predicted value.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below.
Fig. 1 is a flowchart of a method for predicting a load at an end of a regional concentration according to an embodiment of the present invention.
Fig. 2 is a connection diagram of a water supply apparatus provided in an embodiment of the present invention.
Reference numerals:
the system comprises a first chilled water pump 1, a second chilled water pump 2, a control system 3, a first refrigeration host machine 4, a second refrigeration host machine 5, a first water delivery pipeline 6, a second water delivery pipeline 7, an external water delivery pipeline 8, an electromagnetic valve 9, a water flow sensor 10 and a water supply pipeline 11.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below. It is to be understood that the described embodiments are merely a few embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
Referring to fig. 1, an embodiment of the present invention provides a method for predicting a load at an end of a regional concentration, including:
s110, acquiring historical load data and historical weather data of a user; specifically, the user may obtain a historical load data statistics list in advance, and intercept a load value of a predetermined period in the load data statistics list, for example, the user intercepts load data of 2020.1 months, and then, the user respectively counts load data of 31 days, every minute or every hour in the month, and obtains an average value of data of 31 data centers, where the average value of data of every minute or every hour is shown in the following table:
specifically, the historical weather data includes weather conditions corresponding to each day of the predetermined period.
S120, creating a load model of a typical building according to historical load data and historical weather data; and establishing a load model of the typical building according to the acquired load data, specifically, establishing the load model of the typical building with the load value corresponding to each time point by taking time as a coordinate axis and weather factors as vectors.
S130, determining a target area, acquiring predicted weather data of the target area within a preset time period, inputting the weather data into a load model, and predicting a load within the preset time period by using the load model to obtain a predicted load value within the preset time period; in this embodiment, the load data is associated with the weather data and time to create an adaptive learning model, a mathematical relationship is established between the load data and the weather data, then, the weather condition and the cooling load data in a predetermined time for a specific area are input into the adaptive learning model for prediction, and after the prediction is completed and after the preset time is finished, a predicted value of the preset time is obtained, the load prediction of the preset time can adopt the following method to obtain the cooling load data of a required cooling time period and the weather condition corresponding to the cooling time period in the specific area, the specific area can be a certain cell, the change condition of the weather data is counted, the prediction is made according to the change condition of the time and the load data, the change curve of the cooling load in the current specific area is analyzed, and the load condition of the next cooling time period, that is the preset time, is made according to the curve, specifically, the weather condition can be corresponded to the model according to the weather condition published by the national weather station in the future month, and the cooling load of the future month can be predicted. Similarly, the difference exists between the predicted weather data and the actual weather, in the process of continuously prolonging the time, the data of the previous time can be continuously supplemented and set, and the prediction model provided by the scheme is more and more accurate by utilizing a big data statistical means.
S140, acquiring information of water supply equipment currently applied to the target area; the water supply equipment information comprises the number of equipment, the model of the equipment, the running load of the equipment and the energy consumption of the equipment; in this embodiment, the water supply capacity in the target area can be grasped by acquiring the information of the water supply equipment.
S150, calculating a target gap according to the water supply equipment information and the load predicted value of the preset time; increasing or decreasing the capacity of the water supply equipment according to the target gap; and obtaining the optimization result of the equipment type selection and the capacity configuration. In this embodiment, the number or capacity of the water supply apparatuses is increased or decreased to match the predicted value.
The embodiment of the invention provides a method for predicting regional concentrated tail end load, which comprises the following steps: acquiring historical load data of a user; establishing a load model of a typical building according to historical load data; acquiring weather data in a target area, inputting the weather data into a load model, and predicting a load at preset time by using the load model to obtain a load predicted value at preset time; acquiring water supply equipment information currently applied to the target area; the water supply equipment information comprises the number of equipment, the model of the equipment, the running load of the equipment and the energy consumption of the equipment; calculating a target gap according to the water supply equipment information and the load predicted value of the preset time; increasing or decreasing the capacity of the water supply equipment according to the target gap; and obtaining the optimization result of the equipment type selection and the capacity configuration. By adopting the prediction method provided by the invention, according to the historical load data of the user in a specific time period and in combination with a community system, the weather data in a preset time is obtained, the load model is created in combination with the two data, the weather data in the preset time is obtained, the data is input into the load model, the load in the preset time is predicted to obtain a predicted value, and the water supply equipment is equipped by using the predicted value.
In one embodiment, building a load model of a typical building from historical load data comprises: the load model supports vector machine training; and loading the historical load data into the load model in groups, optimizing the load model, inputting each group of the historical data into the load model in groups, and finally obtaining optimized design parameters to finish the training of the load model.
In a specific embodiment, the weather data of the target area at the preset time is input into the trained load model, and the load predicted value at the preset time is predicted by using the obtained design parameters.
In one embodiment, the historical data includes: predicted weather data of a predetermined time period, load data at different time points, and an average value of the load data at different time points within the predetermined time period.
In a specific embodiment, please refer to fig. 2, the water supply device includes a first chilled water pump 1, a second chilled water pump 2, a control system 3, a first refrigeration host 4, a second refrigeration host 5, a first water pipe 6, a second water pipe 7, an external water pipe 8, an electromagnetic valve 9, and a water flow sensor 10; the first chilled water pump 1 is used for pumping water in a water supply pipeline 11 to the first refrigeration main machine 4, one end of the first water conveying pipeline 6 is communicated with a cold water outlet of the first refrigeration main machine 4, the other end is communicated with the external water conveying pipeline, the second chilled water pump 2 is used for pumping water in a water supply pipeline 11 to the second refrigeration main unit 5, one end of the second water conveying pipeline is communicated with a cold water outlet of the second refrigeration host 5, the other end of the second water conveying pipeline is communicated with the external water conveying pipeline, the water flow sensor 10 is used for detecting a water flow value in the external water conveying pipeline, and sending the water flow value to the control system 3, wherein the control system 3 is respectively connected with the first chilled water pump 1, the second chilled water pump 2 and the electromagnetic valve 9 in a control manner, and the electromagnetic valve 9 is used for controlling the on-off of the second water conveying pipeline 7.
In the using process, the first refrigeration host 4 is controlled to start to work, the first refrigeration water pump 1 is synchronously started, at the moment, the first refrigeration water pump 1 pumps water in the water supply pipeline 11 to the first refrigeration host 4, the first refrigeration host 4 starts to refrigerate, cold water is conveyed to the first water conveying pipeline from a cold water outlet of the first refrigeration host 4 and conveyed to the home of a user through an external water conveying pipeline, if the current cold supply demand is larger than the limit cold supply capacities of the first refrigeration host 4 and the first refrigeration water pump 1, the control system 3 controls the second refrigeration water pump 2, the second refrigeration equipment and the electromagnetic valve 9 to be synchronously started, at the moment, the second refrigeration water pump 2 starts to work, the second refrigeration water pump 2 pumps water in the water supply pipeline 11 to the second refrigeration host 5 for refrigeration, and conveys the water into the second water conveying pipeline 7 through a cold water outlet of the second refrigeration host 5, and the cold water output with the first conduit is assembled in the external conduit, deliver to the user's family via the external conduit, in this scheme, the above-mentioned first, two chilled water pumps can be purchased and obtained directly, the above-mentioned first refrigeration host 4 and the above-mentioned second refrigeration host 5 can be the water-cooling cold water equipment, the above-mentioned first, two refrigeration hosts can be purchased and obtained, place in information such as its concrete model, etc., this embodiment does not limit it. It should be noted that the relationship between the demand of the current user and the water supply amount can be determined by the water flow sensor 10, when the water flow increases and increases to the cold supply limit of the first refrigeration host 4 and the first refrigeration water pump 1, at this time, the control system 3 makes a determination that the water flow exceeds the limit, at this time, the control system 3 controls the second refrigeration water pump 2 and the second refrigeration host 5 to work to supplement the shortage of the water supply capacity of the first refrigeration water pump 1 and the first refrigeration host 4, in this embodiment, the pipe diameter of the external water conveying pipe is greater than the pipe diameter of the first water conveying pipe, and the pipe diameter of the external water conveying pipe is greater than the pipe diameter of the second water conveying pipe. The water supply equipment that this scheme of adoption provided can be under the prerequisite that does not increase water supply equipment's quantity, better do the matching with the predicted value to practice thrift the equipment fixing cost. When the expected water supply amount is larger than the maximum water supply amount of the water supply equipment, a plurality of groups of water supply equipment provided by the scheme can be arranged or arranged in advance to ensure that the water supply capacity is larger than the requirement.
In a specific embodiment, the method for predicting the load in the preset time by using the load model includes the following steps: and comparing the predicted value of the preset time with the actual load value of the preset time, inputting the actual value into the load model, and calibrating the load model.
The embodiment of the invention provides a method for predicting regional concentrated tail end load, which comprises the following steps: acquiring historical load data of a user; establishing a load model of a typical building according to historical load data; acquiring weather data in a target area, inputting the weather data into a load model, and predicting a load at preset time by using the load model to obtain a load predicted value at preset time; acquiring water supply equipment information currently applied to the target area; the water supply equipment information comprises the number of equipment, the model of the equipment, the running load of the equipment and the energy consumption of the equipment; calculating a target gap according to the water supply equipment information and the load predicted value of the preset time; increasing or decreasing the capacity of the water supply equipment according to the target gap; and obtaining the optimization result of the equipment type selection and the capacity configuration. By adopting the prediction method provided by the invention, according to the historical load data of the user in a specific time period and in combination with a community system, the weather data in a preset time is obtained, the load model is created in combination with the two data, the weather data in the preset time is obtained, the data is input into the load model, the load in the preset time is predicted to obtain a predicted value, and the water supply equipment is equipped by using the predicted value.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (6)
1. A method for predicting regional concentrated end loads, comprising:
acquiring historical load data and historical weather data of a user;
creating a load model of a typical building according to historical load data and historical weather data;
determining a target area, acquiring predicted weather data of the target area within a preset time period, inputting the weather data into a load model, and predicting a load within the preset time period by using the load model to obtain a predicted load value within the preset time period;
acquiring water supply equipment information currently applied to the target area; the water supply equipment information comprises the number of equipment, the model of the equipment, the running load of the equipment and the energy consumption of the equipment;
calculating a target gap according to the water supply equipment information and the load predicted value of the preset time; increasing or decreasing the capacity of the water supply equipment according to the target gap; and obtaining the optimization result of the equipment type selection and the capacity configuration.
2. The method of claim 1, wherein building a load model of a typical building from historical load data comprises:
the load model supports vector machine training; and loading the historical load data into the load model in groups, optimizing the load model, inputting each group of the historical load data into the load model in groups, and finally obtaining optimized design parameters to finish the training of the load model.
3. The method for predicting the load at the concentrated tail end of the area according to claim 2, wherein the weather data of the target area at the preset time is input into the trained load model, and the load predicted value at the preset time is predicted by using the obtained design parameters.
4. The method of regional intensive end load prediction according to claim 1, wherein the historical load data comprises: the total usage amount of the predetermined time period, the load data at different time points, and the average value of the load data at different time points in the predetermined time period.
5. The regional concentration tail end load prediction method according to claim 1, wherein the water supply equipment comprises a first chilled water pump, a second chilled water pump, a control system, a first refrigeration host, a second refrigeration host, a first water pipe, a second water pipe, an external water pipe, an electromagnetic valve and a water flow sensor; the first refrigeration water pump is used for pumping water in a water supply pipeline to the first refrigeration host, one end of the first water pipeline is communicated with a cold water outlet of the first refrigeration host, the other end of the first water pipeline is communicated with the outward water pipeline, the second refrigeration water pump is used for pumping water in the water supply pipeline to the second refrigeration host, one end of the second water pipeline is communicated with a cold water outlet of the second refrigeration host, the other end of the second water pipeline is communicated with the outward water pipeline, the water flow sensor is used for detecting a water flow value in the outward water pipeline and sending the water flow value to the control system, the control system is respectively connected with the first refrigeration water pump, the second refrigeration water pump and the electromagnetic valve in a control mode, and the electromagnetic valve is used for controlling the on-off of the second water pipeline.
6. The method for predicting the load of the concentrated terminal in the area according to claim 1, wherein the method for predicting the load of the concentrated terminal in the area includes the steps of obtaining weather data in a target area, inputting the weather data into a load model, predicting the load of a preset time by using the load model, and obtaining a predicted value of the load of the preset time, and further includes:
and comparing the predicted value of the preset time with the actual load value of the preset time, inputting the actual value into the load model, and calibrating the load model.
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| CN114021294A (en) * | 2021-11-01 | 2022-02-08 | 武汉荣方科技有限公司 | Energy operation load prediction and early warning method |
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| CN110222398A (en) * | 2019-05-29 | 2019-09-10 | 广州汇电云联互联网科技有限公司 | Water cooler Artificial Intelligence Control, device, storage medium and terminal device |
| CN111256294A (en) * | 2020-01-17 | 2020-06-09 | 深圳市得益节能科技股份有限公司 | Model prediction-based optimization control method for combined operation of water chilling unit |
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| CN114021294A (en) * | 2021-11-01 | 2022-02-08 | 武汉荣方科技有限公司 | Energy operation load prediction and early warning method |
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