An Efficient Short-Term Electricity Forecasting Approach Based on EEMD-LSTM Model with Feature Factors

Abstract

The forecasting of electricity consumption data plays an important role in the operation, planning, and security of the power grid. However, electricity data is affected by multiple factors and large fluctuations, which makes it difficult to accurately forecast. Traditionally, ARIMA and SVM are widely used for electricity forecasting based on historical consumption data. However, for non-stationary multi-feature data, traditional schemes cannot achieve deep feature mining of them, and the forecast results are inaccurate. To address this problem, this paper proposes an efficient short-term electricity forecasting approach based on EEMD-LSTM model. Firstly, we perform Savitzky-golay (SG) smoothing on the original data, and then introduce feature factors to the feature analysis. In particular, the proposed approach can reduce the random noise in data, as well as reduce the impact of data fluctuations, and effectively learn the long-term characteristics of the data. The simulation results show that, compared with ARIMA, LSTM, EMD-SVM, EMD-LSTM, the proposed approach can achieve better accuracy in the electricity forecasting.