Evolutionary Hybrid System for Energy Consumption Forecasting for Smart Meters

Diogo M. F. Izidio, Paulo S. G. de Mattos Neto, Luciano Barbosa, João F. L. de Oliveira, Manoel Henrique da Nóbrega Marinho and Guilherme Ferretti Rissi
Additional contact information
Diogo M. F. Izidio: Centro de Informática, Universidade Federal de Pernambuco, Recife 50740-560, Brazil
Paulo S. G. de Mattos Neto: Centro de Informática, Universidade Federal de Pernambuco, Recife 50740-560, Brazil
Luciano Barbosa: Centro de Informática, Universidade Federal de Pernambuco, Recife 50740-560, Brazil
João F. L. de Oliveira: Escola Politécnica de Pernambuco, Universidade de Pernambuco, Recife 50720-001, Brazil
Manoel Henrique da Nóbrega Marinho: Escola Politécnica de Pernambuco, Universidade de Pernambuco, Recife 50720-001, Brazil
Guilherme Ferretti Rissi: CPFL Energia, Campinas, São Paulo 13088-900, Brazil

Energies, 2021, vol. 14, issue 7, 1-19

Abstract: The usage of smart grids is growing steadily around the world. This technology has been proposed as a promising solution to enhance energy efficiency and improve consumption management in buildings. Such benefits are usually associated with the ability of accurately forecasting energy demand. However, the energy consumption series forecasting is a challenge for statistical linear and Machine Learning (ML) techniques due to temporal fluctuations and the presence of linear and non-linear patterns. Traditional statistical techniques are able to model linear patterns, while obtaining poor results in forecasting the non-linear component of the time series. ML techniques are data-driven and can model non-linear patterns, but their feature selection process and parameter specification are a complex task. This paper proposes an Evolutionary Hybrid System (EvoHyS) which combines statistical and ML techniques through error series modeling. EvoHyS is composed of three phases: (i) forecast of the linear and seasonal component of the time series using a Seasonal Autoregressive Integrated Moving Average (SARIMA) model, (ii) forecast of the error series using an ML technique, and (iii) combination of both linear and non-linear forecasts from (i) and (ii) using a a secondary ML model. EvoHyS employs a Genetic Algorithm (GA) for feature selection and hyperparameter optimization in phases (ii) and (iii) aiming to improve its accuracy. An experimental evaluation was conducted using consumption energy data of a smart grid in a one-step-ahead scenario. The proposed hybrid system reaches statistically significant improvements when compared to other statistical, hybrid, and ML approaches from the literature utilizing well known metrics, such as Mean Squared Error (MSE).

Keywords: smart metering; energy consumption; forecasting; time series; machine learning; hybrid systems; statistical models (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 (10)

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