Nearest Neighbors Time Series Forecaster Based on Phase Space Reconstruction for Short-Term Load Forecasting

Abstract

Load forecasting provides essential information for engineers and operators of an electric system. Using the forecast information, an electric utility company’s engineers make informed decisions in critical scenarios. The deregulation of energy industries makes load forecasting even more critical. In this article, the work we present, called Nearest Neighbors Load Forecasting (NNLF), was applied to very short-term load forecasting of electricity consumption at the national level in Mexico. The Energy Control National Center (CENACE—Spanish acronym) manages the National Interconnected System, working in a Real-Time Market system. The forecasting methodology we propose provides the information needed to solve the problem known as Economic Dispatch with Security Constraints for Multiple Intervals (MISCED). NNLF produces forecasts with a 15-min horizon to support decisions in the following four electric dispatch intervals. The hyperparameters used by Nearest Neighbors are tuned using Differential Evolution (DE), and the forecaster model inputs are determined using phase-space reconstruction. The developed models also use exogenous variables; we append a timestamp to each input (i.e., delay vector). The article presents a comparison between NNLF and other Machine Learning techniques: Artificial Neural Networks and Support Vector Regressors. NNLF outperformed those other techniques and the forecasting system they currently use.