A power material demand forecasting method based on parameter optimization variational mode decomposition and LSTM
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Abstract:
The State Grid has continuously improved its material procurement management level and refined its online procurement processes. However, inaccurate estimation of procurement plans, has led suppliers to engage in price games using the general bidding and tendering mechanism during the bidding process. This has resulted in increased procurement costs of the power grid company. Therefore, it is of great significance to establish an accurate and effective electricity material demand forecasting model. In respose to the instability, volatility and intermittency of power material sequences, this paper proposes a forecasting method for power material demand based on parameter-optimized variational mode decomposition (VMD) and long short-term memory neural network (LSTM). Typical power materials from the State Grid e-commerce zone platform were selected. VMD, optimized by using the whale optimization algorithm(WOA) parameters, was adopted to perform modal decomposition on the original sequence. LSTM models were then constructed for each modal component obtained from the decomposition. Finally, the predicted values of each mode were superimposed and reconstructed into the predicted value of power materials. Experimental results show that the proposed method achieves higher prediction accuracy compared to LSTM, EMD-LSTM,VMD-LSTM, PSO-VMD-LSTM and SSA-VMD-LSTM. This approach holds practical significance for the forecast of power grid material purchase.
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Supported by State Grid Corporation of China Science and Technology Project(SGCQWZ00ZBJS2313256).
