Short-Term Load Forecasting with an Ensemble Model Based on 1D-UCNN and Bi-LSTM

Abstract

Short-term load forecasting (STLF), especially for regional aggregate load forecasting, is essential in smart grid operation and control. However, the existing CNN-based methods cannot efficiently extract the essential features from the electricity load. The reason is that the basic requirement of using CNNs is space invariance, which is not satisfied by the actual electricity data. In addition, the existing models cannot extract the multi-scale input features by representing the tendency of the electricity load, resulting in a reduction in the forecasting performance. As a solution, this paper proposes a novel ensemble model, which is a four-stage framework composed of a feature extraction module, a densely connected residual block (DCRB), a bidirectional long short-term memory layer (Bi-LSTM), and ensemble thinking. The model first extracts the basic and derived features from raw data using the feature extraction module. The derived features comprise hourly average temperature and electricity load features, which can capture huge randomness and trend characteristics in electricity load. The DCRB can effectively extract the essential features from the above multi-scale input data compared with CNN-based models. The experiment results show that the proposed method can provide higher forecasting performance than the existing models, by almost 0.9–3.5%.