Short-Term Power Load Forecasting Based on Cross Multi-Model and Second Decision Mechanism

Abstract

Short-term load forecasting (STLF) plays a vital role in the reliable, secure, and efficient operation of power systems. Since electric load variation results from diverse factors, accurate and stable load forecasting remains a challenging task. To increase the forecasting accuracy and stability, in this paper, we newly propose a short-term load forecasting method based on the cross multi-model and second decision mechanism. First, we combine horizontal and longitudinal training set selection method to construct the cross training sets, which acquire both the horizontal and longitudinal characteristics of the load variation. Second, to improve the generalization ability and extend the application scope, we construct forecasting multi-models by training multiple forecasting algorithms with cross training sets. Finally, to aggregate the forecasting outputs obtained by the forecasting multi-models, we propose a second decision mechanism based on a decision multi-model and adaptive weight allocation strategy, which overcomes the limited learning ability shortcoming of single decision models and further improves the forecasting accuracy. Case studies based on electrical load data from the state of Maine, the region of New England, Singapore, and New South Wales of Australia show that both the accuracy and the stability of the proposed method are superior to the compared models.

Keywords

• Short-term load forecasting
• multi-model
• cross training set, second decision mechanism
• model aggregation