Construction of short-term electricity demand streaming forecasting model in demand-side dynamic response

Abstract

In the context of demand-side dynamic response, the electricity supply-demand relationship undergoes constant changes, and short-term electricity load exhibits strong randomness and volatility, making load conditions challenging to predict. Hence, this paper proposes a short-term electricity demand streaming forecasting model that combines wavelet decomposition with Random Forest to enhance the accuracy of short-term electricity load forecasting. This model establishes a load feature system, utilizing a three-scale wavelet decomposition algorithm to break down the load sequence into several sub-sequences of different frequency bands. Subsequently, Random Forest load forecasting models are separately established for these sub-sequences. The final load prediction is obtained through reconstruction. This approach enables quasi-real-time short-term forecasting analysis of demand-side electricity demand.