Short-Term Power Load Prediction Based on VMD-SG-LSTM

Abstract

Power load prediction plays an important role in the safety and stability of national power system. However, due to the nonlinear and multi-frequency characteristics of the power system itself, power load prediction is difficult. To address this problem, we propose a short-term power load prediction model based on variational mode decomposition (VMD). First, original data are decomposed into intrinsic mode function (IMF) of different frequencies using the VMD algorithm, and the decomposed sub-functions are reconstructed. After smoothing the reconstructed data by Savitzky-Golay (S-G) filtering algorithm, the change trend of raw data (CTRD) is obtained. Then, IMF, CTRD and raw data are used as inputs to predict short-term power load by long short-term memory network (LSTM). Finally, the proposed prediction model is compared with the other two groups of prediction models. The results show that the proposed VMD-SG-LSTM prediction model has high fitting ability and high prediction accuracy, and is an effective method for short-term power load prediction.

A New Combined Prediction Model for Ultra-Short-Term Wind Power Based on Variational Mode Decomposition and Gradient Boosting Regression Tree

“…Methods Application [11] BCD, SVR Forecasting wind generation [12] ARFIMA, LSSVM Wind power prediction [13] ISSO, MLP Forecasting wind power [14] CNN, LSTM Wind power prediction [15] EMEMA Wind power prediction error in the multi-objective environmental economics problem [16] VMD, SG, LSTM Short-term power load prediction [17] VMD, NN Wind speed prediction [18] EMD, LSTM Short-term wind power prediction [19] EMD, DLSTM Ultra-short-term prediction of wind power [21] VMD, LSTM Load forecasting [22] VMD, RBF Wind power prediction…”

“…To further enhance their ability to extract temporal information and improve their resistance to interference and generalization, combined algorithms of machine learning and load decomposition are often employed [16,17]. In order to fully utilize the effective information in historical data and further improve the prediction accuracy of wind power generation, reference [18] proposed an ensemble empirical mode decomposition (EMD) and LSTM neural network.…”

Short-Term Power Load Forecasting Based on VMD-Pyraformer-Adan

“…The load data are first modally decomposed using the VMD algorithm, and then all subsequences and residuals of the VMD are predicted using the ISSA-optimized GRU network, and this method can effectively avoid the modal confounding phenomenon that occurs in EMD decomposition. Sun et al [25] combined VMD with SG filter (Savitzky-Golay Filter) and proposed a combined VMD-SG-LSTM prediction model, where the data were noise reduced using SG filter after VMD decomposition, and then the reconstructed data were input to LSTM network for prediction to improve the model prediction accuracy.…”