A Multifactorial Framework for Short-Term Load Forecasting System as Well as the Jinan’s Case Study

Abstract

Accurate and reliable short-term electric load forecasting (STLF) plays a critical role in power system to enhance its routine management efficiency and reduce operational costs. However, most of the existing STLF methods suffer from lack of appropriate feature selection procedure. In this paper, a multifactorial framework (MF) possessing the potential to contribute more satisfactory forecasting results and computational speed is proposed. Moreover, a graphical tool for easy and accurate computation of day-ahead load forecast is implemented via MATLAB App Designer. Firstly, we choose the candidate feature set by analyzing the raw electricity consumption data. Then, partial mutual information is adopted as criterion to eliminate these irrelevant and redundant ones among candidate features for the purpose of reducing the input subset and retaining these most relevant. At last, the selected features are used as the input of the well-established artificial neural network (ANN) model optimized by genetic algorithm and cross validation to implement prediction. The MF is applied for the load data measured from 2016 to 2018 in Jinan, and then some competitive experiments and extensive simulations are carried out and results indicates that the ANN-based model with selected features significantly outperforms other alternative models with single features or a few of features regarding mean absolute percent error. In addition, the parallel structure of ANN and the lower dimension of the input space enable the model to achieve faster calculation speed.

Keywords

• Short-term load forecasting
• feature selection
• partial mutual information
• artificial neural network