Effective RNN-Based Forecasting Methodology Design for Improving Short-Term Power Load Forecasts: Application to Large-Scale Power-Grid Time Series

Abstract

This article introduces a carefully-engineered forecasting methodology for day-ahead electric power load forecasts evaluated using the European Network of Transmission System Operators for Electricity (ENTSO-E). Two steps were employed to configure the desired forecasting methodology: First, a straightforward processing pipeline is proposed to enable systematic preprocessing of raw multivariate time-discrete power data extracted from the ENTSO-E repository, including a stride-based sliding window approach to generate time series-based batches ready for the supervised learning procedure. Second, the lightweight type of recurrent neural network method, namely gated recurrent units (GRU), is selected and carefully calibrated to yield accurate multi-step forecasts, which was trained using the preprocessed multivariate time series data to render day-ahead power load forecasts. The forecasting estimates generated by the proposed GRU model are evaluated using a set of regression-based metrics to assess the models’ precisions. The empirical results show that the proposed forecasting methodology yields outstanding day-ahead power load forecasting performance regarding the enterprise-class measured data compared to a statistical model, namely autoregressive integrated moving average with exogenous variables (ARIMAX), as well as the actual day-ahead forecasts generated by the ENTSO-E platform.