Short-Term Load Monitoring of a Power System Based on Neural Network

Abstract

In order to improve the accuracy of power load forecasting, this paper proposes a neural network-based short-term monitoring method. First, the original energy load signal is decomposed by the CEEMDAN algorithm to obtain several eigenmode function components and residual components; several eigenmode function components and residual functions are fed into the NARX neural network for computational purposes. The partial hypothesis is superimposed in the following part to obtain the final short-term forecast. According to the test results, the MAPE of the CEEMDAN-NARX model is 4.753%, 3.540%, and 0.343% lower than the SVM, RNN, and NARX models, respectively, and 3.741% and 2.682% lower than CEEMDAN-SVM and CEEMDAN-RNN, respectively. The MAPE and RMSE of the CEEMDAN-NARX model are 0.765% and 101.7 MW, respectively, which are 0.468% and 45.2 MW lower than NARX models, respectively. Compared to CEEMDAN-SVM, the MAPE of CEEMDAN-NARX and CEEMDAN-RNN decreased by 0.986% and 0.692%, respectively, and the RMSE of CEEMDAN-NARX decreased by 111.5 and 65.7 MW, respectively, compared to CEEMDAN-SVM. Conclusion is that the load forecasting model based on the combination of CEEMDAN algorithm and NARX neural network can effectively connect, reduce the negative impact of noise on forecasting results, and improve forecasting accuracy.

Short-Term Electric Load Forecasting Based on Signal Decomposition and Improved TCN Algorithm

“…CEEMDAN represents an advancement over empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) [20,21]. EMD is a method for extracting singular and symmetric components from nonlinear and non-stationary signals, dividing the signal into a set of intrinsic mode functions (IMFs), each representing the signal's oscillation patterns at different frequencies [22].…”

Multifeature Short-Term Power Load Forecasting Based on GCN-LSTM

“…In order to comprehensively evaluate the GCN-LSTM model in terms of load forecasting accuracy, characteristic dimensions, training time, and prediction duration, three existing methods, LSTM [21], CNN-LSTM [22], and TCN-LSTM [23], are selected for comparison.…”

Retracted: Short-Term Load Monitoring of a Power System Based on Neural Network

“…Tis article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of systematic manipulation of the publication and peer-review process.…”