Electrical Load Demand Forecasting Using Feed-Forward Neural Networks

Eduardo Machado, Tiago Pinto, Vanessa Guedes and Hugo Morais
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Eduardo Machado: Instituto Superior Técnico-IST, Universidade de Lisboa, 1049-001 Lisbon, Portugal
Tiago Pinto: GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Rua DR. Antonio Bernardino de Almeida, 431, 4200-072 Porto, Portugal
Vanessa Guedes: Department of Materials, Energy Efficiency and Complementary Generation, Electrical Energy Research Center (Cepel), University City, Fundão Island, Rio de Janeiro 21941-911, Brazil
Hugo Morais: Instituto Superior Técnico-IST, Universidade de Lisboa, 1049-001 Lisbon, Portugal

Energies, 2021, vol. 14, issue 22, 1-24

Abstract: The higher share of renewable energy sources in the electrical grid and the electrification of significant sectors, such as transport and heating, are imposing a tremendous challenge on the operation of the energy system due to the increase in the complexity, variability and uncertainties associated with these changes. The recent advances of computational technologies and the ever-growing data availability allowed the development of sophisticated and efficient algorithms that can process information at a very fast pace. In this sense, the use of machine learning models has been gaining increased attention from the electricity sector as it can provide accurate forecasts of system behaviour from energy generation to consumption, helping all the stakeholders to optimize their activities. This work develops and proposes a methodology to enhance load demand forecasts using a machine learning model, namely a feed-forward neural network (FFNN), by incorporating an error correction step that involves the prediction of the initial forecast errors by another FFNN. The results showed that the proposed methodology was able to significantly improve the quality of load demand forecasts, demonstrating a better performance than the benchmark models.

Keywords: error correction; load demand forecast; feed-forward neural network (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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