CN113902183B - BERT-based non-invasive transformer area charging pile state monitoring and electricity price adjusting method - Google Patents
BERT-based non-invasive transformer area charging pile state monitoring and electricity price adjusting method Download PDFInfo
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Abstract
The invention relates to the electric vehicle charging management technology and aims to provide a BERT-based non-invasive distribution room charging pile state monitoring and electricity price adjusting method. The method comprises the following steps: acquiring total power historical data and charging pile state historical data of a transformer substation area as training samples; building a BERT model, which sequentially comprises an embedding layer, a transform layer and an output layer from front to back; determining a loss function for training; training a BERT model by using a gradient descent algorithm; historical total power data of the intelligent electric meters in the transformer substation area are input into the trained BERT model, historical working state data and historical charging power data of each charging pile are obtained, and state monitoring of the charging piles in the transformer area is achieved. The method can be directly applied to the existing charging pile without updating the hardware equipment of the charging pile; the design and production cost of the charging pile is reduced, and the method is more economical and efficient than the traditional method. The charging price can be adjusted in real time to guide a user to change electricity utilization habits, so that the peak clipping and valley filling of a power grid are facilitated, and the electric energy utilization rate is improved.
Description
Technical Field
The invention relates to an electric vehicle charging management technology, in particular to a non-invasive load detection and charging capacity prediction method based on BERT (Bidirectional Encoder recovery from converters), and a method for adjusting charging price of an electric vehicle in real time by using the method.
Technical Field
In recent years, with the rapid development of electric vehicles and related technologies, more and more electric vehicles are connected to a power grid, and the complexity of power grid load is increased. Through the electric vehicle charging capacity prediction algorithm, data support is provided for formulation of the real-time charging electricity price of the electric vehicle, and charging scheduling of the electric vehicle is facilitated. Most of the existing charge capacity prediction methods are realized by electric vehicle charge information acquired by related electrical data detection equipment arranged on a charge pile, so that certain requirements are configured on the hardware of the charge pile, and the existing charge capacity prediction methods are not economical enough.
Disclosure of Invention
The technical problem to be solved by the invention is to overcome the defects in the prior art and provide a non-invasive method for monitoring the charging pile state and adjusting the electricity price of a distribution room based on BERT.
In order to solve the technical problem, the solution of the invention is as follows:
the method for monitoring the state of the charging pile in the non-invasive distribution area based on the BERT comprises the following steps:
(1) Acquiring total power historical data and charging pile state historical data of a transformer substation area as training samples;
(2) Building a BERT model, which sequentially comprises an embedding layer, a transform layer and an output layer from front to back;
(3) Determining a loss function for training;
(4) Training the BERT model by using a gradient descent algorithm, which specifically comprises the following steps:
(4.1) randomly initializing model parameters;
(4.2) transmitting the training samples into a BERT model to obtain output;
(4.3) calculating the loss according to the loss function;
(4.4) for each neuron generating an error, adjusting model parameters to reduce the error;
(4.5) repeating steps (4.2) to (4.4) until the loss converges;
(5) Historical total power data of the intelligent electric meters in the transformer substation area are input into the trained BERT model, historical working state data and historical charging power data (curves) of the charging piles are obtained, and monitoring of the charging pile state in the transformer area is achieved.
The invention further provides a method for further predicting the residual charging capacity of a transformer substation area by using the BERT-based non-invasive transformer substation charging pile state monitoring method, which comprises the following steps:
(1) Building and training a fully-connected feedforward neural network FFN;
(2) Deploying the trained BERT model and FFN model to an intelligent electric meter, reading total power data of a transformer substation area in real time by the intelligent electric meter, inputting the total power data into the trained BERT model, and acquiring working state data of a charging pile in real time to form historical charging power data (curve);
(3) Calculating historical residual capacity data of the transformer area according to the historical total power of the transformer area and the inherent capacity of the transformer area read by the intelligent electric meter; calculating to obtain the historical residual charging capacity of the transformer substation area by combining the historical charging power data obtained in the step (2); and inputting the result into the trained FFN model to obtain the predicted future residual charging capacity.
The invention also provides a method for further adjusting the real-time charging price by using the method for predicting the residual charging capacity of the transformer substation area, which comprises the following steps:
(1) Calculating the ratio of the historical/future residual charging capacity of the transformer area to the total transformation capacity of the transformer area within a certain time step according to the historical residual charging capacity and the predicted future residual charging capacity of the transformer area, and recording the ratio as { H } t-T ,H t-T+1 ,...,H t-1 ,H t And { F } t ,F t+1 ,...,F t+T-1 ,F t+T }; wherein H t ＝F t The current time is the ratio of the remaining charging capacity;
(2) Calculate electric automobile according to the following formula and use real-time electrovalence P that charges that fills electric pile t ：
Wherein rho is an electricity price adjustment constant; k is the number of the time points taken into account, and K time points are considered in the historical and future residual charging capacities; h k The remaining charge capacity ratio of the kth time point before the current time; f k The predicted remaining charge capacity ratio at the kth time point after the current time is obtained; alpha belongs to (0, 1) as a historical discount factor, and gamma belongs to (0, 1) as a future discount factor, which represents how much the remaining charge capacity at the previous/next moment is considered at the current moment;
(3) And taking the calculated real-time charging electricity price as a charging calculation basis for the electric automobile to use the charging pile.
Description of the inventive concept:
under the normal condition, the state detection of the charging pile of the transformer substation requires that related sensors and metering equipment are additionally arranged on the charging pile, and more hardware cost is required.
The non-invasive load detection is a load detection method for obtaining the internal load power data by analyzing the electrical information data of the power load inlet, and has the advantages of economy and cleanness. Nowadays, the energy internet is more and more popular, and the non-invasive load detection is more and more favored by manufacturers and researchers. However, in the related research on non-intrusive load detection, the technical scheme of the related research is to estimate the activities and energy consumption conditions of various other electric devices in the facility by measuring the total electric energy information of the accessed facility, so that the application scene of the technology is basically limited to providing an auxiliary detection means for the research on power consumption control of the electric devices in the facility.
The invention breaks through the inertial thought of the technical research and development in the industry, abandons the traditional method of hardware modification of the charging pile, and uses the non-intrusive load detection technology to obtain the electric energy information of the charging pile, so that the data metering equipment and the sensor are not required to be additionally arranged for monitoring the state of the existing charging pile, and the economy is improved; meanwhile, the extracted data is used for capacity prediction and electricity price adjustment, and instantaneity and prospect of electricity price adjustment are improved. The non-intrusive load detection method and the electric vehicle charging capacity prediction are introduced into the real-time adjustment of the charging electricity price of the electric vehicle, and compared with the traditional technical improvement scheme, the method is more prospective and more economic.
The BERT (Bidirectional Encoder retrieval from transforms) model is generally used for natural language processing tasks and has a good recognition capability for time series data such as language characters. However, compared with language and character data, the transformer substation area data is more fluctuating and diverse, and the task of extracting the charging pile data from the transformer substation area data cannot be well completed by directly using the BERT model. The invention improves the loss function of the model to better fulfill the requirements.
Meanwhile, the traditional electricity price making method generally only considers historical factors (such as historical power generation amount and historical load) and does not have foresight. The invention provides a charging price adjustment method considering historical and future residual charging capacity at the same time, and designs a related price adjustment formula, so that the method is more prospective and more reasonable than the traditional method. The future residual charging capacity is obtained by predicting the non-intrusive historical residual charging capacity through the full-connection feedforward neural network FFN.
Based on the improvement, according to total power data of the intelligent electric meters in the transformer substation area, historical working state information of the charging pile of the electric automobile in the transformer substation area is extracted by using a BERT-based non-invasive load detection method; predicting future residual charging capacity by using a fully-connected feed-forward neural network (FFN) according to the historical residual capacity of the transformer substation and the extracted historical charging load information; and finally, formulating a real-time charging electricity price mechanism of the electric automobile according to the historical residual charging capacity and the predicted future residual charging capacity of the charging pile.
Compared with the prior art, the invention has the beneficial effects that:
(1) The non-invasive load detection method is introduced into the electric vehicle charging capacity prediction algorithm, so that the algorithm for obtaining the electric vehicle charging information does not depend on the charging pile to install the electrical data acquisition equipment, can be directly applied to the existing charging pile, and does not need to update the charging pile hardware equipment; the design and production cost of the charging pile is reduced, and the method is more economical and efficient than the traditional method.
(2) After offline training and deployment are carried out, charging power information of the charging pile can be directly obtained online in real time by reading total power data of a transformer substation area, and residual charging capacity information of a predicted future time is updated in real time, so that the method is more real-time and efficient than the traditional algorithm.
(3) The real-time electricity price mechanism of the electric automobile simultaneously considers the historical residual charging capacity and the future residual charging capacity within a certain time step length, and compared with the traditional method, the real-time electricity price mechanism of the electric automobile is more prospective and the established electricity price is more reasonable. The charging price can be adjusted in real time to guide a user to change electricity utilization habits, so that the peak clipping and valley filling of a power grid are facilitated, and the electric energy utilization rate is improved.
Drawings
FIG. 1 is a block diagram of a BERT-based non-intrusive load detection model;
FIG. 2 is a diagram of a fully connected feed forward neural network (FFN) prediction model.
Detailed Description
The invention is further described below with reference to the accompanying drawings and specific embodiments, where an implementation scenario is a substation area where electric vehicle charging piles are built, and the substation area is provided with an intelligent summary table. The invention comprises three phases: firstly, extracting historical working state information of a charging pile of an electric vehicle in a transformer area by using a BERT-based non-invasive load detection method according to total power data of intelligent electric meters in the transformer area; secondly, predicting future residual charging capacity by using a fully-connected feed-forward neural network (FFN) according to the historical residual capacity of the transformer substation and the extracted historical charging load information; and finally, formulating a real-time charging electricity price mechanism of the electric automobile according to the historical residual charging capacity and the predicted future residual charging capacity of the charging pile.
The specific operation of each stage is as follows:
a first part: BERT-based non-invasive load detection method
1. Acquiring total power data of a transformer substation area and charging pile state data as training samples for model input and output;
2. building a BERT model (as shown in figure 1), and sequentially comprising an embedding layer, a transform layer and an output layer from front to back;
(2.1) Embedded layer
The embedding layer firstly extracts the characteristics of input data through a convolutional neural network and inputs the characteristics into the hiding layer, reduces the length of an input sequence by half through a square average pooling operation, and finally adds the input sequence with a learnable position embedding matrix (capturing sequence position coding) and outputs the input sequence to the next layer. The calculation formula of the embedding layer is:
Embedding(X)＝LPPooling(Conv(X))+E pose
where X is the input matrix, LPPooling (. Degree.) is the square mean pooling operation, conv (. Degree.) is the convolutional neural network, E pose Representing a learnable position embedding matrix.
(2.2) Transformer layer
Embedding the layer output matrix into the bidirectional Transformer layer, which is composed of multiple layers of transformers and multiple self-attentions in each layer. A single self-entry can be represented by a linear transformation of the input matrices Q (Query), K (Key) and V (Value), with the formula:
wherein, d k Dimensions of Q and K, i.e., hidden layer size; softmax (.) is a normalized exponential function.
Integrating multi-head attentions by a plurality of self-attentions, namely, making the process of calculating self-attentions for a plurality of times, and splicing output matrixes, wherein the formula is as follows:
MultiHead(Q,K,V)＝Concat(head 1 ,head 2 ,…,head h )W O
wherein, concat () is a matrix splicing function; w is a group of O Is a weight matrix; w i Q ,W i K ,W i V To linearly map the matrix, the inputs are mapped to different spaces.
(2.3) output layer
The output of the transform layer is first input to a position full-connection feed forward network (PFFN) of the output layer, which has the formula: PFFN (X) = GELU (0, xw) 1 +b 1 )W 2 +b 2
Wherein GELU (. Eta.) is activation function, W i ,b i And X is a network parameter matrix and a network input matrix.
And then obtaining the final output through a multilayer perceptron (MLP), wherein the final output comprises an deconvolution layer and two linear layers, and the formula is as follows:
Out(X)＝Tanh(Deconv(X)W 1 +b 1 )W 2 +b 2
where Tanh (. Eta.) is the activation function, deconv (. Eta.) is the deconvolution network, W i ,b i Is a network parameter matrix and X is a network input matrix.
3. Determining a loss function for training, which is as follows:
wherein,
x∈[0,1]respectively representing a model prediction output value and a normalization value of the real charging pile power;
s i e { -1,1} is the predicted on-off state and the actual on-off state of the charging pile; t is the total time step; o is the time step length which meets the condition that the actual on-off state of the charging pile is on or the model state is wrong in prediction; both tau and lambda are hyper-parameters of the model, so as to reduce absolute errors; d KL () is a relative entropy function; softmax (.) is a normalized exponential function; log (.) is a log-based function of 10; exp (.) is an exponential function with a constant e as the base.
4. Training a model by using a gradient descent algorithm, and specifically comprising the following steps of:
(4.1) random initialization of model parameters, i.e. weights w i And deviation b i ；
(4.2) transmitting the input data into the model to obtain output;
(4.3) calculating the loss L according to the loss function;
(4.4) for each neuron that produces an error, adjusting the model parameters to reduce the error according to:
(4.5) repeating steps (4.2) to (4.4) until the loss converges.
5. Historical total power data of the intelligent electric meters in the transformer substation area are input into the trained BERT model in real time, historical working state data and historical charging power data (curves) of all charging piles are obtained, and non-invasive load detection is achieved.
A second part: method for predicting residual charging capacity of transformer substation area
1. Building and training a fully connected feed forward neural network (FFN) for predicting future residual charge capacity;
(1) Acquiring historical residual capacity of a transformer substation area and historical charging power data of a charging pile, and calculating to obtain the historical residual charging capacity of the transformer substation area; segmenting the data samples to form training data for the FFN model;
(2) An FFN model is built, as shown in fig. 2, which includes a convolution layer and two linear layers, and the calculation formula is:
Out(X)＝Tanh(conv(X)W 1 +b 1 )W 2 +b 2
where Tanh (. Eta.) is the activation function, conv (. Eta.) is the convolutional network, W i ,b i X is the network input, is the network parameter.
(3) Determining a loss function for training, which is as follows:
wherein,
x i respectively representing the model output value and the true remaining charge capacity, T being the time step.
(4) Training a model by using a gradient descent algorithm, and specifically comprising the following steps of:
(4.1) random initialization of model parameters, i.e. weights w i And deviation b i ；
(4.2) transmitting the input data into the model to obtain output;
(4.3) calculating the loss L according to the loss function;
(4.4) for each neuron that produces an error, adjusting the model parameters to reduce the error according to:
(4.5) repeating steps (4.2) to (4.4) until the loss converges.
2. And deploying the trained BERT model and FFN model to an intelligent electric meter, reading the total power data of the transformer substation area in real time by the intelligent electric meter, inputting the total power data into the trained BERT model, and acquiring the working state data of the charging pile in real time to form historical charging power data (curve).
3. Calculating historical residual capacity data of the transformer area according to the historical total power of the transformer area and the inherent capacity of the transformer area read by the intelligent electric meter, and calculating the historical residual charging capacity of the transformer area according to the historical charging power data obtained in the step 2; inputting the current data into a trained FFN model to obtain the predicted future residual charge capacity.
And a third part: mechanism for formulating charging real-time electricity price of electric automobile
1. Calculating the ratio of the historical/future residual charging capacity of the transformer area to the total transformation capacity of the transformer area within a certain time step according to the historical residual charging capacity and the predicted future residual charging capacity of the transformer area, and recording the ratio as { H } t-T ,H t-T+1 ,...,H t-1 ,H t And { F } t ,F t+1 ,...,F t+T-1 ,F t+T }; wherein H t ＝F t The remaining charge capacity is a ratio of the remaining charge capacity at the present moment;
2. calculate electric automobile according to the following formula and use real-time electrovalence P that charges that fills electric pile t ：
Wherein rho is an electricity price adjustment constant; h k Is the ratio of the remaining charge capacity at the kth time point before the current time, F k The predicted remaining charge capacity ratio of the kth time point after the current time; k is the number of the time points taken into account, and K time points are considered for history and future residual charging capacity; alpha belongs to (0, 1) as a historical discount factor, and gamma belongs to (0, 1) as a future discount factor, which represents how much the remaining charge capacity at the previous/next moment is considered at the current moment;
(3) And taking the real-time charging electricity price obtained by calculation as a charging calculation basis for the electric automobile to use the charging pile.
The historical surplus charging capacity and the future surplus charging capacity within a certain time step length are considered simultaneously by the electricity price mechanism, real-time electricity prices are formulated according to the historical surplus charging capacity and the future surplus charging capacity, electricity prices in peak periods are increased, electricity prices in valley periods are reduced, the electricity price mechanism is beneficial to guiding users to reduce electricity consumption in peak periods, electricity consumption in valley periods is increased, peak clipping and valley filling of a power grid are facilitated, and the electric energy utilization rate is improved.
Claims (4)
1. A BERT-based non-invasive method for monitoring the charging pile state in a distribution room is characterized by comprising the following steps:
(1) Acquiring total power historical data and charging pile state historical data of a transformer substation area as training samples;
(2) Building a BERT model, which sequentially comprises an embedding layer, a transform layer and an output layer from front to back;
the BERT model specifically comprises:
(2.1) embedding layer
The embedding layer firstly extracts the characteristics of input data through a convolutional neural network and inputs the characteristics into the hiding layer, then the length of an input sequence is halved by adopting square average pooling operation, and finally the input sequence is added with a learnable position embedding matrix and output to the next layer;
(2.2) Transformer layer
Embedding the layer output matrix into a bidirectional Transformer layer, wherein the bidirectional Transformer layer consists of a plurality of layers of transformers and a plurality of self-attentions in each layer; a single self-event is represented by a linear transformation of the input matrices Q (Query), K (Key) and V (Value); integrating multi-head attentions by a plurality of self-attentions, namely, performing a self-attention calculation process for a plurality of times, splicing output matrixes, and mapping input to different spaces;
(2.3) output layer
The output of the Transformer layer is firstly input to a position full-connection feedforward network PFFN of an output layer; then, a final output is obtained through a multilayer perceptron MLP comprising an deconvolution layer and two linear layers;
(3) Determining a loss function for training, specifically:
wherein,
representing the model predicted output value and the normalized value of the real charging pile power,
x i respectively representing the model output value and the real residual charging capacity;
the predicted on-off state and the actual on-off state of the charging pile are obtained; t is the total time step; o is the time step length which meets the condition that the actual on-off state of the charging pile is on or the model state is wrong in prediction; both tau and lambda are hyper-parameters of the model, so as to reduce absolute errors; d KL () is a relative entropy function; softmax (.) is a normalized exponential function; log (.) is a logarithmic function with a base 10; exp (.) is an exponential function with a constant e as the base;
(4) Training the BERT model by using a gradient descent algorithm, which specifically comprises the following steps:
(4.1) randomly initializing model parameters;
(4.2) transmitting the training samples into a BERT model to obtain output;
(4.3) calculating the loss according to the loss function;
(4.4) for each neuron generating an error, adjusting model parameters to reduce the error;
(4.5) repeating steps (4.2) to (4.4) until the loss converges;
(5) Historical total power data of the intelligent electric meters in the transformer substation area are input into the trained BERT model, historical working state data and historical charging power data of each charging pile are obtained, and state monitoring of the charging piles in the transformer area is achieved.
2. The method of claim 1, further predicting a substation area remaining charging capacity, comprising the steps of:
(S1) building and training a fully-connected feedforward neural network (FFN); the method specifically comprises the following steps:
(S1.1) acquiring historical residual capacity of a transformer substation area and historical charging power data of a charging pile, and calculating to obtain the historical residual charging capacity of the transformer substation area; segmenting the data sample to form training data;
(S1.2) building an FFN model, wherein the FFN model comprises a convolution layer and two linear layers;
(S1.3) determining a loss function for training;
(S1.4) training the FFN model by using a gradient descent algorithm, which specifically comprises the following steps:
(S1.4.1) randomly initializing model parameters;
(S1.4.2) transmitting the input data into the model to obtain output;
(S1.4.3) calculating loss according to the loss function;
(S1.4.4) for each neuron generating an error, adjusting model parameters to reduce the error:
(s1.4.5) repeating steps (s1.4.2) to (s1.4.4) until the loss converges;
(S2) deploying the trained BERT model and the FFN model to an intelligent electric meter, reading total power data of a transformer substation area in real time by the intelligent electric meter, inputting the total power data into the trained BERT model, and acquiring working state data of a charging pile in real time to form historical charging power data;
(S3) calculating historical residual capacity data of the transformer area according to the historical total power and the inherent capacity of the transformer area read by the intelligent electric meter; calculating to obtain the historical residual charging capacity of the transformer substation area by combining the historical charging power data obtained in the step (S2); and inputting the result into the trained FFN model to obtain the predicted future residual charging capacity.
3. Method according to claim 2, characterized in that the loss function for training in step (S1.3) is in particular:
4. the method of claim 2, wherein the real-time charging electricity price is further adjusted, comprising the steps of:
(A1) Calculating the ratio of the historical/future residual charging capacity of the transformer area to the total transformation capacity of the transformer area within a certain time step according to the historical residual charging capacity and the predicted future residual charging capacity of the transformer area, and recording the ratio as { H } t-T ，H t-T+1 ，...，H t-1 ，H t And { F } t ，F t+1 ，...，F t+T-1 ，F t+T }; wherein H t ＝F t The remaining charge capacity is a ratio of the remaining charge capacity at the present moment;
(A2) Calculate real-time electricity price P that charges that electric automobile used charging pile according to following formula t ：
Wherein rho is an electricity price adjusting constant; k is the number of the time points taken into account, and K time points are considered in the historical and future residual charging capacities; h k The remaining charge capacity ratio of the kth time point before the current time; f k The predicted remaining charge capacity ratio at the kth time point after the current time is obtained; alpha belongs to (0, 1) as a historical discount factor, and gamma belongs to (0, 1) as a future discount factor, which represents how much the remaining charge capacity at the previous/next moment is considered at the current moment;
(A3) And taking the real-time charging electricity price obtained by calculation as a charging calculation basis for the electric automobile to use the charging pile.
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