A hybrid approach based on autoregressive integrated moving average and least-square support vector machine for long-term forecasting of net electricity consumption

Fazil Kaytez

Energy, 2020, vol. 197, issue C

Abstract: Electricity consumption is on the rise in developing countries. Most of the research studies in energy demand forecasting aim to provide that sufficient electricity is produced to meet future needs. A reliable forecasting model is necessary for accurate investment planning of electricity generation and distribution. The main goal of this study is to develop effective and realistic solutions for electricity consumption forecasting in Turkey. This paper proposes a hybrid model based on least-square support vector machine and an autoregressive integrated moving average. This hybrid approach’s forecast results are compared with multiple linear regression approach, a single autoregressive integrated moving average model, official forecasts and similar studies in literature. Also, it is applied to forecast the future net electricity consumption for Turkey until 2022. The study results indicate that the proposed model can generate more realistic and reliable forecasts. It can also be stated that it responds better to some unexpected reactions in the time series.

Keywords: Energy modeling; Least square support vector machines; Autoregressive integrated moving average; Multiple regression; Prediction; Electricity consumption (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (31)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:197:y:2020:i:c:s0360544220303078

DOI: 10.1016/j.energy.2020.117200

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