Produção Nacional
Revisado por pares
Forecasting of individual electricity consumption using Optimized Gradient Boosting Regression with Modified Particle Swarm Optimization
2021; Elsevier BV; Volume: 105; Linguagem: Inglês
10.1016/j.engappai.2021.104440
ISSN1873-6769
AutoresLuis Fernando Marín Sepulveda, Petterson S. Diniz, João Otávio Bandeira Diniz, Stelmo Magalhães Barros Netto, Carolina L. S. Cipriano, Alexandre de Carvalho Araújo, Victor Henrique Bezerra de Lemos, Alexandre Pessoa, Darlan B. P. Quintanilha, João Dallyson Sousa de Almeida, Aristófanes Corrêa Silva, Anselmo Cardoso de Paiva, Geraldo Bráz, Marcia I. A. Silva, Eliana Márcia Garros Monteiro, Italo Fernandes S. Silva, Eduardo Camacho Fernandes,
Tópico(s)Grey System Theory Applications
ResumoThe task of forecasting consumers' energy consumption is currently a trend in energy supply companies. An accurate prediction of energy consumption is a powerful tool to check for inconsistencies between what is recorded and the actual amount consumed. In practice, Brazilian energy companies verify inconsistencies in the manual reading of consumption, using a consumption range based on the predicted consumption. This consumption forecast is currently realized by the average of previous consumptions and, therefore, can be improved by the use of machine learning techniques. For this purpose, an Optimized Gradient Boosting Regressor (OGBR) was proposed, which has been optimized by a modified version of the Particle Swarm Optimization (PSO) for fast parameter optimization. The OGBR prediction results on a dataset of over 2 million consumers were compared with its unmodified version and with the Seasonal and Trend decomposition using Loess (STL). In addition, the forecast stability of the OGBR over 12 months was evaluated. Therefore, the energy consumption forecasting performance was improved by using the OGBR and this performance was better than its unmodified version, in all validation metrics, and better than STL, in most classes of consumption.
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