Short Term Power Load Forecasting Based on PSVMD-CGA Model

Su, Jingming; Han, Xuguang; Hong, Yan

doi:10.3390/su15042941

Cited by 9 publications

(5 citation statements)

References 39 publications

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“…Decomposing the original load data before forecasting [3,[24][25][26][27][28][29][30][31][32][33][34] This type of method reduces the volatility of the original load data, and the different components obtained from the decomposition are forecasted individually, with the various methods forming a complementary advantage.…”

Section: References Advantages Improvement Requirement/disadvantagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Variational mode decomposition (VMD) is also a commonly used decomposition approach for short-term load forecasting [32][33][34], which is a completely non-recursive model that avoids the mode-confounding problems present in EMD, and has the advantage that the number of mode decompositions can be artificially determined. In reference [32], VMD decomposes the original sequence into multiple subsequences, which reduces the volatility of the raw load. CNN is used to solve the problem whereby it is difficult for GRU to extract high-dimensional characteristics of the power load, and an attention mechanism is introduced to solve the problem whereby important information cannot be emphatically weighted when the series is too long.…”

Section: Introductionmentioning

confidence: 99%

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Two-Stage Short-Term Power Load Forecasting Based on SSA–VMD and Feature Selection

2023

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Short-term power load forecasting is of great significance for the reliable and safe operation of power systems. In order to improve the accuracy of short-term load forecasting, for the problems of random fluctuation in load and the complexity of load-influencing factors, this paper proposes a two-stage short-term load forecasting method, SSA–VMD-LSTM-MLR-FE (SVLM–FE) based on sparrow search algorithm (SSA), to optimize variational mode decomposition (VMD) and feature engineering (FE). Firstly, an evaluation criterion on the loss of VMD decomposition is proposed, and SSA is used to find the optimal combination of parameters for VMD under this criterion. Secondly, the first stage of forecasting is carried out, and the different components obtained from SSA–VMD are predicted separately, with the high-frequency components input to a long short-term memory network (LSTM) for forecasting and the low-frequency components input to a multiple linear regression model (MLR) for forecasting. Finally, the forecasting values of the components obtained in the first stage are input to the second stage for error correction; factors with a high degree of influence on the load are selected using the Pearson correlation coefficient (PCC) and maximal information coefficient (MIC), and the load value at the moment that has a great influence on the load value at the time to be predicted is selected using autocorrelation function (ACF). The forecasting values of the components are fused with the selected feature values to construct a vector, which is fed into the fully connected layer for forecasting. In this paper, the performance of SVLM–FE is evaluated experimentally on two datasets from two places in China. In Place 1, the RMSE, MAE, and MAPE are 128.169 MW, 102.525 MW, and 1.562%, respectively; in Place 2, the RMSE, MAE, and MAPE are 111.636 MW, 92.291 MW, and 1.426%, respectively. The experimental results show that SVLM–FE has high accuracy and stability.

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Section: References Advantages Improvement Requirement/disadvantagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Two-Stage Short-Term Power Load Forecasting Based on SSA–VMD and Feature Selection

2023

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“…Compared to other types of time-series data, electricity loads are more random and volatile and are influenced by many factors [7,8], such as weather conditions, geographical location, holidays, and time-of-day tariffs, which can all interfere with electricity consumption [9]. By introducing feature quantities to the forecasting model and training the features and load together, the fit of the data to the model can be improved to a certain extent, thus enabling the forecasting model to gain stronger predictive power [10].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Power Load Forecasting Based on Feature Filtering and Error Compensation under Imbalanced Samples

Wan

2023

Energies

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There are many influencing factors present in different situations of power load. There is also a strong imbalance in the number of load samples. In addition to examining the problem of low training efficiency of existing algorithms, this paper proposes a short-term power load prediction method based on feature selection and error compensation under imbalanced samples. After clustering the load data, we expand some sample data to balance the sample categories and input the load data and filtered feature sequences into the improved GRU for prediction. At the same time, the errors generated during the training process are used as training data. An error correction model is constructed and trained, and the results are used for error compensation to further improve prediction accuracy. The experimental results show that the overall prediction accuracy of the model has increased by 80.24%. After expanding a few samples, the prediction accuracy of the region where the samples are located increased by 59.41%. Meanwhile, due to the improvement of the algorithms, the running time was reduced by approximately 14.92%.

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“…Some researchers have also attempted to incorporate other algorithms into models that have already combined multiple neural networks. Su et al [23] proposed a variational mode decomposition optimization algorithm (PSVMD-CGA) that combines CNN, GRU, attention mechanism, and mahjong algorithm. Cai et al [24] introduced a short-term load forecasting approach that integrates variational mode decomposition (VMD), gated recurrent unit (GRU), and temporal convolutional network (TCN) into a hybrid network.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Review of Machine Learning-Based Methods for Electricity Load Forecasting

Zhao

2023

E3S Web Conf.

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With the improvement of data processing power and the continuous development of modern power grids, there is an increasing demand for accuracy in predicting power load. To study the field of power load forecasting, this article summarizes and categorizes different models into three types: traditional models, single machine learning models, and hybrid models, based on previous literature. Firstly, a general overview is provided of the application of different models in power load forecasting. Secondly, typical models from three categories are selected for a detailed introduction. In traditional models, the ARIMA model is chosen, while in single machine learning models, CNN, and LSTM are chosen. For the hybrid model, the ResNet-LSTM mixed neural network is selected for the introduction. Finally, four different datasets were used to test different models. The differences and patterns of the models were summarized, and suggestions were proposed for future research directions in the field of power load forecasting.

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Short Term Power Load Forecasting Based on PSVMD-CGA Model

Cited by 9 publications

References 39 publications

Two-Stage Short-Term Power Load Forecasting Based on SSA–VMD and Feature Selection

Two-Stage Short-Term Power Load Forecasting Based on SSA–VMD and Feature Selection

Short-Term Power Load Forecasting Based on Feature Filtering and Error Compensation under Imbalanced Samples

Comprehensive Review of Machine Learning-Based Methods for Electricity Load Forecasting

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