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

Jose R. Cedeño González (), Juan J. Flores (), Claudio R. Fuerte-Esquivel () and Boris A. Moreno-Alcaide ()
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Jose R. Cedeño González: División de Estudios de Posgrado, Facultad de Ingenierá Eléctrica, Universidad Michoacana de San Nicolás de Hidalgo, Morelia 58000, Mexico
Juan J. Flores: División de Estudios de Posgrado, Facultad de Ingenierá Eléctrica, Universidad Michoacana de San Nicolás de Hidalgo, Morelia 58000, Mexico
Claudio R. Fuerte-Esquivel: División de Estudios de Posgrado, Facultad de Ingenierá Eléctrica, Universidad Michoacana de San Nicolás de Hidalgo, Morelia 58000, Mexico
Boris A. Moreno-Alcaide: National Center for Energy Control, Secretary of Energy, Don Manuelito 32, Olivar de los Padres, Álvaro Obregón, Ciudad de México 01780, Mexico

Energies, 2020, vol. 13, issue 20, 1-24

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.

Keywords: short-term load forecasting; machine learning; time series forecasting; nearest neighbors algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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