An Overview of Short-Term Load Forecasting for Electricity Systems Operational Planning: Machine Learning Methods and the Brazilian Experience

Giancarlo Aquila, Lucas Barros Scianni Morais, Victor Augusto Durães de Faria, José Wanderley Marangon Lima, Luana Medeiros Marangon Lima and Anderson Rodrigo de Queiroz ()
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Giancarlo Aquila: Institute of Production Engineering and Management, Federal University of Itajubá, Itajubá 37500-903, MG, Brazil
Lucas Barros Scianni Morais: Institute of Electrical and Energy Systems, Federal University of Itajubá, Itajubá 37500-903, MG, Brazil
Victor Augusto Durães de Faria: Graduate Program on Operations Research, NC State University, Raleigh, NC 27606, USA
José Wanderley Marangon Lima: Institute of Electrical and Energy Systems, Federal University of Itajubá, Itajubá 37500-903, MG, Brazil
Luana Medeiros Marangon Lima: Nicholas School of Environment, Duke University, Durham, NC 27708, USA
Anderson Rodrigo de Queiroz: Graduate Program on Operations Research, NC State University, Raleigh, NC 27606, USA

Energies, 2023, vol. 16, issue 21, 1-35

Abstract: The advent of smart grid technologies has facilitated the integration of new and intermittent renewable forms of electricity generation in power systems. Advancements are driving transformations in the context of energy planning and operations in many countries around the world, particularly impacting short-term horizons. Therefore, one of the primary challenges in this environment is to accurately provide forecasting of the short-term load demand. This is a critical task for creating supply strategies, system reliability decisions, and price formation in electricity power markets. In this context, nonlinear models, such as Neural Networks and Support Vector Machines, have gained popularity over the years due to advancements in mathematical techniques as well as improved computational capacity. The academic literature highlights various approaches to improve the accuracy of these machine learning models, including data segmentation by similar patterns, input variable selection, forecasting from hierarchical data, and net load forecasts. In Brazil, the national independent system operator improved the operation planning in the short term through the DESSEM model, which uses short-term load forecast models for planning the day-ahead operation of the system. Consequently, this study provides a comprehensive review of various methods used for short-term load forecasting, with a particular focus on those based on machine learning strategies, and discusses the Brazilian Experience.

Keywords: short-term load forecasting; day-ahead operational planning; time series forecasting; machine learning methods; electricity power systems (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: 2023
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