Equipping Seasonal Exponential Smoothing Models with Particle Swarm Optimization Algorithm for Electricity Consumption Forecasting

• Published in: Energies (Basel) Vol. 14; no. 13; p. 4036
• Main Authors: Deng, Changrui, Zhang, Xiaoyuan, Huang, Yanmei, Bao, Yukun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2021
• Subjects: Accuracy; Artificial intelligence; Electricity; electricity consumption forecasting; Forecasting; genetic algorithm; grid search method; Methods; Optimization algorithms; particle swarm optimization algorithm; Random variables; seasonal exponential smoothing models; Seasonal variations; Support vector machines; Time series
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en14134036

Electricity consumption forecasting plays an important role in investment planning of electricity infrastructure, and in electricity production/generation and distribution. Accurate electricity consumption prediction over the mid/long term is of great interest to both practitioners and academics. Considering that monthly electricity consumption series usually show an obvious seasonal variation due to their inherent nature subject to temperature during the year, in this paper, seasonal exponential smoothing (SES) models were employed as the modeling technique, and the particle swarm optimization (PSO) algorithm was applied to find a set of near-optimal smoothing parameters. Quantitative and comprehensive assessments were performed with two real-world electricity consumption datasets on the basis of prediction accuracy and computational cost. The experimental results indicated that (1) whether the accuracy measure or the elapsed time was considered, the PSO performed better than grid search (GS) or genetic algorithm (GA); (2) the proposed PSO-based SES model with a non-trend component and additive seasonality term significantly outperformed other competitors for the majority of prediction horizons, which indicates that the model could be a promising alternative for electricity consumption forecasting.