CN112734137B - Short-term building power load prediction method and system based on intelligent electric meter data - Google Patents
Short-term building power load prediction method and system based on intelligent electric meter data Download PDFInfo
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Abstract
The invention discloses a short-term building power load prediction method and a system based on intelligent electric meter data, wherein the method comprises the following steps: 1) Data input: taking total table load data and internal load data as input data, wherein the internal load data comprises each sub table load data and unmeasured load data; 2) Data processing: filling missing data, correcting abnormal values, and reserving internal load data with a correlation coefficient larger than a set value according to a correlation analysis result carried out in advance; 3) And (3) load prediction: inputting the time sequence formed by the internal load data and the total surface load data obtained in the step 2) into a pre-trained stable time convolution network, and performing short-term prediction on the building power load at the target future time. According to the method, the building power load is predicted in a short term by adopting the time convolution neural network, and compared with the traditional method, the time for training to reach stability is obviously shortened, the error is obviously reduced, and the prediction performance is better.
Description
Technical Field
The invention relates to short-term building power load prediction, in particular to a short-term building power load prediction method and system based on intelligent electric meter data.
Background
With the improvement of living standard of ordinary individual residents, the individual building load level is also improved, and the load has great fluctuation due to the behavior of residents. During the peak period of power utilization, when an individual building uses a plurality of high-power electric appliances at the same time, the power supply pressure of a power grid feeder line can be increased, and then the power supply pressure of the whole power distribution network is increased. Therefore, through customer-side demand response, the supplier can realize peak clipping of the power grid by adjusting the price of electricity, and the residents can also reduce the electric bill. Short-term building load prediction is a key technology of customer-side demand response, and win-win between a power distribution department and a user can be realized.
The variability of the electricity utilization behaviors of the customers in the building has obvious correlation with the prediction effect, and the data size is huge, so that great challenges are brought to the learning of a time series model. The traditional prediction method adopts a long-short-term memory recurrent neural network, and learns the habits of residents in using household appliances based on household appliance use data under the condition of considering household appliance consumption, so that the prediction performance superior to that of multiple references is obtained. However, the recurrent neural network such as long-short term memory cannot process the input sequence in parallel, so the overhead of model training increases with the length of the input sequence, and the long-term history information is forgotten, so that the prediction result is inaccurate.
Disclosure of Invention
The invention aims to provide a short-term building power load prediction method and system based on intelligent electric meter data, which have better prediction performance.
In order to achieve the purpose, the short-term building power load prediction method based on the intelligent electric meter data comprises the following steps:
1) Data input: taking total table load data and internal load data as input data, wherein the internal load data comprises each sub table load data and unmeasured load data;
2) Data processing: searching missing data and abnormal data for the total table load data and the internal load data, filling the missing data, correcting an abnormal value, keeping the internal load data with the correlation coefficient larger than a set value according to a correlation analysis result of the internal load data and the total table load data which are performed based on historical load data in advance, and deleting other internal load data;
3) And (3) load prediction: inputting the time sequence formed by the internal load data and the total table load data obtained in the step 2) into a pre-trained stable time convolution network (TCN for short), and performing short-term prediction on the building power load at the target future time (the time when the next group of power utilization data is generated).
Preferably, the step 1) further comprises a data acquisition step, namely acquiring time-series electricity utilization data of the building summary table and each sub-table (such as a washing machine, a plug and the like) in a period of time before the predicted time, and then obtaining unmeasured load data of each group of electricity utilization data according to the difference of the sum of the load data measured by the summary table and the load data measured by each sub-table, thereby obtaining the input data.
Preferably, in the step 1), the length of the acquired time sequence is equal to the length of the time sequence input to the time convolution network each time during training.
Preferably, in the step 3), the convolution layer includes a causal convolution and a hole convolution.
Preferably, the basic structure of the time convolutional network is a residual network composed of 2 to 4 residual layers, and each residual layer includes 1 to 2 base layers, 1 to 2 convolutional layers, and 1 direct-connected layer.
Preferably, in step 3), the convolution layer includes a causal convolution and a void convolution.
Preferably, in the step 3), the number of neurons of the time convolution network is 250-350.
Preferably, in the step 3), the pre-training of the time convolution network includes the following steps: inputting total table load data and internal load data of a past period of time according to training requirements; filling and correcting the load data by adopting the method in the step 2), then carrying out correlation analysis on each internal load data and the total table load data, selecting the internal load data with the correlation coefficient larger than a set value (preferably 0.1) as auxiliary input data, combining the auxiliary input data with the total table load data, and training the time convolution network.
Preferably, the training parameters of the time convolutional network are set as: the batch is set as 60, the learning rate is 0.00001, 10 rounds of training are carried out on the model, the number of times of each round of training is set as 20 epochs, each epoch training is carried out on the whole data set once, 10 rounds of training are carried out to obtain 10 scores in total, and the score average value is obtained to serve as the index of the prediction accuracy rate.
The invention also provides a short-term building power load prediction system designed for realizing the method, which comprises the following steps:
a data acquisition module configured to implement the data acquisition step;
a data processing module configured to implement the data processing steps;
a load prediction module configured to implement the load prediction step.
Preferably, the load prediction module employs a time convolution network, the time convolution network includes 2 residual error layers, and each residual error layer includes 1-2 base layers, 1-2 convolution layers, and 1 direct connection layer.
Compared with the prior art, the invention has the beneficial effects that:
according to the method, the time convolution neural network is adopted, the sub-meter and the total meter of the intelligent electric meter are combined, the total meter load data, each sub-meter load data and the unmeasured load data are used as input data of a prediction task, correlation analysis is conducted on the input data, and internal load data related to the total load serving as a target task are extracted and used as auxiliary input data.
Drawings
Fig. 1 is a block diagram illustrating a short-term building electrical load prediction system according to embodiment 1 of the present invention.
Fig. 2 to 4 are schematic structural diagrams of causal convolution, hole convolution and residual connection of the time convolution network provided in embodiment 1 of the present invention.
Fig. 5 is a bar chart of correlation coefficients of each internal load and total table load data obtained by performing correlation analysis in embodiment 2 of the present invention.
Fig. 6 is an iterative comparison graph of the performance of TCN networks with different structural parameters and the conventional LSTM network in embodiment 2 of the present invention.
Fig. 7 is a comparison graph of the predicted results of TCN networks with different configuration parameters and the conventional algorithm in embodiment 2 of the present invention.
Fig. 8 is an enlarged view of a detail within the box in fig. 7.
Wherein: data acquisition module 1, data processing module 2 and load prediction module 3
Detailed Description
The invention is described in further detail below with reference to the figures and the specific embodiments.
Example 1
The present embodiment describes the construction, training and application of the neural network.
1. Constructing a time convolution network and setting parameters
Selecting the length (n + 1) of a history input sequence, setting the size k of a convolution kernel, setting the step length stride to be 1, and setting and operating the following parameters in order to ensure that the length of an output sequence of a network is the same as the length of an input sequence and cause-effect convolution is required: the convolution parameter zero padding (padding) is determined to be k-1, the output sequence length is (n + k-1), and a tail divide operation is performed to remove the last future output sequence, i.e., the tail divide number is equal to padding, as shown in fig. 2.
Based on the causal convolution, to adaptively adjust the depth of the time convolution network, a hole convolution (scaled convolution) is used. In particular, an input sequence X is given n+1 ＝{x 0 ,x 1 ,...,x n Where f (i) is a convolution function, the hole convolution can be defined as:
d is the hole factor, and m-i.d is the input sequence number of the element selected for convolution. When d =1, the hole convolution is degenerated to a general one-dimensional convolution. As the number of network layers increases, d grows exponentially, and the higher the level, the more historical information is covered by the network sequence. As shown in fig. 3.
Constructing residual connection based on causal convolution and hole convolution, specifically, a first branch, wherein if the length of an output sequence is not equal to that of an input sequence, an input layer is connected to the output layer through 1x1 convolution, otherwise, the input layer is connected to the output layer through identity mapping; and the second branch, namely, the input sequence is connected to a network formed by sequentially forming a plurality of convolution layers, an activation layer and a dropout layer and superposed by a same structure. As shown in fig. 4.
On the basis, the model depth design is determined according to the sequence length, the cavity convolution and the residual error connection mechanism. Specifically, initially, assume that the depth of the network is L, the depth of the input layer is 0, and each layer is a residual layer. The longest history memory information that can be contained in the last output sequence in the n-th layer of neurons can be derived by the following formula:
I L is the longest input sequence information that layer L residual neurons last sequence value can depend on. When load prediction is performed, the output sequence of the neuron in the previous layer is used as the input sequence of the current layer, and only the final value of the output sequence is used as the output of the residual error network after finally passing through all the residual error layers upwards, as shown in fig. 4.
The residual structure shown in fig. 4 is used to superimpose the base layer, or the convolutional layer, or both the base layer and the convolutional layer, as needed.
And finally, taking the output of each parallel residual error network as the input of a feedforward neural network, and outputting the input as the prediction result of the future time step.
2. Training of a time convolutional network
1) Data acquisition and input: collecting time sequence electricity utilization data (the length of a single sequence is n + 1) of a building general table and each branch table (such as a washing machine, a plug and the like) collected in a past period of time before a prediction time according to training needs, obtaining unmeasured load data of each group of electricity utilization data according to the difference of the sum of load data measured by the general table and load data measured by each branch table, and taking the load data of the general table and the internal load data as input data; wherein, each sub-table load data and the unmeasured load data are collectively called as internal load data;
2) Data processing: searching missing data and abnormal data for the load data, filling the missing data, correcting abnormal values, performing correlation analysis on each sub-table and unmeasured load data and total table load data on the basis (after filling and correction), sorting according to the correlation coefficients from large to small, and selecting internal load data and total table load data with the correlation coefficients larger than a set value (selected to be 0.1 in the embodiment) as auxiliary input data of the time convolution network.
3) Model training
And training the time convolution network by combining the total table load data and the internal load data.
The specific training parameters are set as follows: the batch is set to 60, the learning rate is set to 0.00001, 10 rounds of training are performed on the model, the number of times of each round of training is set to 20 epochs, each epoch training is performed once on the whole data set, 10 rounds of training are performed to obtain 10 scores in total, and the score average value is obtained to serve as the prediction accuracy index (namely, each error index in table 2).
Setting model parameters: the hidden layer neuron is set to 300, and since the number of residual layers is set to 2, we select an input sequence length of 7. The original residual layer base layer (B) is 1, the causal void convolution layer (C) in the base layer is 1, and several improved versions are designed simultaneously in this embodiment, which is detailed in table 1.
3. Short-term building electrical load prediction
The steps of adopting the trained time convolution network model to carry out load prediction are as follows:
1) Data acquisition and input: collecting time sequence electricity utilization data (the sequence length is n +1 and is the same as the training stage) of a building general table and each sub-table (such as a washing machine, a plug and the like) in a period of time before the prediction time, obtaining unmeasured load data of each group of electricity utilization data according to the difference value of the sum of the load data measured by the general table and the load data measured by each sub-table, and taking the load data of the general table and the internal load data as input data; wherein, the sub-meter load data and the unmeasured load data are collectively called as internal load data;
2) Data processing: searching missing data and abnormal data for the total table load data and the internal load data, filling the missing data, correcting an abnormal value, reserving the internal load data with the correlation coefficient larger than a set value of 0.1 and deleting other internal load data according to a correlation analysis result (see a training part of a time convolution network) of the internal load data and the total table load data which is performed based on historical load data in advance;
3) And (3) load prediction: inputting the time sequence formed by the internal load data and the total table load data obtained in the step 2) into a pre-trained stable time convolution network (TCN for short), and performing short-term prediction on the building power load at the target future time (the time when the next group of power utilization data is generated).
The embodiment also provides a short-term building power load prediction system based on the method, which comprises a data acquisition module 1, a data storage module and a data processing module, wherein the data acquisition module is configured to realize the data acquisition step; a data processing module 2 configured to implement the data processing steps; a load prediction module 3 configured to implement the load prediction step.
The load prediction module 3 adopts a time convolution network, the time convolution network comprises 1 direct connection layer and 2 residual error layers, each residual error layer comprises 1-2 basic layers and 1-2 convolution layers, and specific parameters are shown in table 1.
Example 2
This example trains the time-time convolutional network given in example 1 based on the AMPds2 dataset and evaluates and compares its predicted performance after the training has stabilized. The method comprises the following specific steps:
1. set of experimental data
This example uses The AMPds2 data set (Stephen makonin. Ampds2: the Almanac of minutely Power dataset (Version 2), 2016) to train and evaluate The time-convolution network disclosed in example 1, as well as several common control algorithms.
From 4/2012 to 3/2014, the AMPds2 data set collected various energy consumption in their authors' homes, including power, water, and natural gas measurements per minute, for a total of 1051200 readings, involving meters including 21 smartmeters, 2 meters, and 2 meters of natural gas (with additional equipment instructions).
The embodiment selects the smart meter data set with granularity per minute as the original data set. The full House meter reading consists of three parts, namely a Master-House meter reading (MHE), a rental housing (RSE) electric sub-meter reading and a Garage (GRE) electric sub-meter reading. The MHE consists of the remaining 18 electrical (AE) meter readings and unmeasured energy consumption (UNE). Thus, the relationship between data set readings can be described as:
WHE＝MHE+RSE+GRE
MHE＝AE+UNE
2. experimental setup
In the experiment, 0 in month 4 of 2012, month 1, to 00 in month 4 of 2012, month 9, and 24 in day 24. After the correlation analysis, as shown in fig. 5, master-House readings (MHE), electric Heat Pump (HPE), clothes Dryer (CDE), oven blower and thermostat (FRE), unmeasured energy consumption (UNE), basement plug and light (BME), kitchen fireplace (WOE), home office electricity (OFE), TV/PVR/AMP (TVE), dishwasher (DWE) were selected as input data for the model.
Each model was set up as follows:
(1) K is adjacent to each other: the kd-Tree is 30, the weights are uniform and consistent, and the neighbor is 15.
(2) Gradient Boost Decision Tree (GBDT): estimators is 100, the maximum depth of the decision tree is 6, the minimum sample size required for the internal node repartitioning is 60, and the rest are default values.
(3) Support Vector Regression (SVR): kernel is RBF, gamma is 0.03, c is 5.66, and others are defaults.
(4) LSTM: and adopting the set optimal parameters, and referring to: weicong Kong, zhao Yang Dong, david J Hill, fengji Luo, and Yan xu. Short-term residual load for evaluating based on residual latent bearing learning. IEEE Transactions on Power Systems,33 (1): 1087-1088,2017.
In addition to LSTM, the other benchmark test is to grid search for the best parameters in the sklern module of python.
In the training of the time-time convolution network, the batch is set to be 60, the learning rate is set to be 0.00001, the epoch times are set to be 20, adam is selected as an optimization method, all models are trained for 10 times, and the average value is taken. At the same time, we have also tested several improved TCN architectures, whose structural parameters are detailed in table 1.
TABLE 1 TCN structural parameters
| Method | Residual layer | Base layer | Convolutional layer | Hidden neuron |
| TCN(B1C1) | 2 | 1 | 1 | 300 |
| TCN(B1C2) | 2 | 1 | 2 | 300 |
| TCN(B2C1) | 2 | 2 | 1 | 300 |
| TCN(B2C2) | 2 | 2 | 2 | 300 |
3. Results and analysis of the experiments
The following error indicators were used to evaluate the predictive performance of the model, where y t Is the sum of observed values
Is an expected value. The smaller their value, the better the model performance.
Percent mean absolute error (MAPE):
mean Absolute Error (MAE):
root Mean Square Error (RMSE):
the predicted performance (MAPE, MAE, RMSE) of each model is shown in table 2 below:
TABLE 2 prediction of building load
As can be seen from the above table, the MAPE of TCN (B1C 2) is lowest and the MAPE is reduced by 11.90% compared with the best reference LSTM; TCN (B2C 2) has the lowest RMSE, the RMSE is reduced by 0.8%; TCN (B1C 2), TCN (B2C 1) and TCN (B2C 2) all have the lowest MAE, and the MAE is reduced by 7.5%; the worst TCN (B1C 1) MAPE decreased by 9.13%, MAE decreased by 7.5%, and RMSE leveled off.
The performance variation of the test set during LSTM and TCN training is shown in FIG. 6. Both reached relatively steady state during training, but TCN reached steady state almost 50% less than LSTM. Thus, the TCN architecture not only enables optimal performance of the network with greater efficiency, but ultimately better network performance than LSTM.
The results of the four network predictions are shown in fig. 7. To better highlight the advantages of TCNs over each benchmark test, some details in fig. 7 are shown in fig. 8. In the case of large residential load fluctuation, the four TCN architectures have stronger capability of tracking residential load than LSTM, which means that TCN architecture has great potential in predicting residential load fluctuation in real time.
Claims (9)
1. A short-term building power load prediction method based on intelligent electric meter data is characterized by comprising the following steps: the method comprises the following steps:
1) Data input: taking total table load data and internal load data as input data, wherein the internal load data comprises each sub table load data and unmeasured load data; the unmeasured load data is equal to the difference between the sum of the load data measured by the general table and the load data measured by each sub-table;
2) Data processing: searching missing data and abnormal data for the total table load data and the internal load data, filling the missing data, correcting abnormal values, keeping the internal load data with the correlation coefficient larger than a set value according to a correlation analysis result of the internal load data and the total table load data which are performed based on historical load data in advance, and deleting other internal load data;
3) And (3) load prediction: inputting a time sequence formed by the internal load data and the total table load data obtained in the step 2) into a pre-trained stable time convolution network, and performing short-term prediction on the building power load at the target future time;
the step 1) further comprises a data acquisition step, namely acquiring time series electricity utilization data of the building general table and each sub-table in a period of time before the prediction time, and obtaining unmeasured load data of each group of electricity utilization data according to the difference value of the sum of the load data measured by the general table and the load data measured by each sub-table, so as to obtain the input data.
2. The short term building electrical load prediction method of claim 1, wherein: in the step 1), the length of the acquired time sequence is equal to the length of the time sequence input into the time convolution network each time during training.
3. The short-term building electrical load prediction method of claim 1, further comprising: in the step 3), the basic structure of the time convolution network is a residual error network formed by 2-4 residual error layers, and each residual error layer comprises 1-2 basic layers, 1-2 convolution layers and 1 direct connection layer.
4. The short term building electrical load prediction method of claim 3, wherein: in the step 3), the time convolution network comprises a causal convolution and a cavity convolution, and the number of neurons in each layer is 250-350.
5. The short term building electrical load prediction method of claim 1, wherein: in the step 3), the pre-training of the time convolution network includes the following steps: inputting total table load data and internal load data of a past period of time according to training requirements; filling and correcting the load data by adopting the method in the step 2), then carrying out correlation analysis on each internal load data and the total table load data, selecting the internal load data with the correlation coefficient larger than a set value as auxiliary input data, combining the auxiliary input data with the total table load data, and training the time convolution network.
6. The short term building electrical load prediction method of claim 5, wherein: the set value of the correlation coefficient is 0.1.
7. The short-term building electrical load prediction method of claim 5, further comprising: the training parameters of the time convolution network are set as follows: the batch is set as 60, the learning rate is set as 0.00001, 10 rounds of training are carried out on the model, the training frequency of each round is set as 20 epochs, each epoch training is carried out once on the whole data set, 10 rounds of training are carried out to obtain 10 scores in total, and the score average value is obtained to serve as the prediction accuracy index.
8. A short term building electrical load prediction system designed to implement the method of any one of claims 1 to 7, comprising:
a data acquisition module (1) configured to implement said data acquisition step;
a data processing module (2) configured to implement the data processing steps;
a load prediction module (3) configured to implement the load prediction step.
9. The short term building electrical load prediction system of claim 8, wherein: the load prediction module (3) adopts a time convolution network, the time convolution network comprises 2 residual error layers, and each residual error layer comprises 1-2 base layers, 1-2 convolution layers and 1 direct connection layer.
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