Abstract:

With the high proportion of renewable energy and the rapid development of the electricity market,the uncertainty of the power system increases.In order to improve the accuracy of load forecasting in the market environment,a short-term load forecasting method based on causal relationship analysis is proposed.First,the grey correlation analysis method is used to quantify the correlation between meteorological factors and market factors and the load.Then,the optimal modal decomposition method is used to decompose the load mode,and the Granger causal analysis method is used to match the influencing factors with the modal subsequences.Finally,the subsequence is predicted using the differential autoregressive moving average (ARIMA) model and the bidirectional long-short-term memory (Bi-LSTM) neural network model.The prediction results are superimposed to obtain the short-term load prediction results.The simulation results show that the prediction accuracy of the proposed method can reach 92%,which can verifie the accuracy and effectiveness of the proposed method.

Key words: short-term load forecasting, optimal modal decomposition, Granger causal analysis, ARIMA, Bi-LSTM