Day-Ahead Electricity Demand Forecasting Using a Novel Decomposition Combination Method

Hasnain Iftikhar, Josue E. Turpo-Chaparro (), Paulo Canas Rodrigues and Javier Linkolk López-Gonzales ()
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Hasnain Iftikhar: Department of Statistics, Quaid-i-Azam University, Islamabad 45320, Pakistan
Josue E. Turpo-Chaparro: Escuela de Posgrado, Universidad Peruana Unión, Lima 15468, Peru
Paulo Canas Rodrigues: Department of Statistics, Federal University of Bahia, Salvador 40170-110, Brazil
Javier Linkolk López-Gonzales: Vicerrectorado de Investigación, Universidad Privada Norbert Wiener, Lima 15046, Peru

Energies, 2023, vol. 16, issue 18, 1-22

Abstract: In the present liberalized energy markets, electricity demand forecasting is critical for planning of generation capacity and required resources. An accurate and efficient electricity demand forecast can reduce the risk of power outages and excessive power generation. Avoiding blackouts is crucial for economic growth, and electricity is an essential energy source for industry. Considering these facts, this study presents a detailed analysis of the forecast of hourly electricity demand by comparing novel decomposition methods with several univariate and multivariate time series models. To that end, we use the three proposed decomposition methods to divide the electricity demand time series into the following subseries: a long-run linear trend, a seasonal trend, and a stochastic trend. Next, each subseries is forecast using all conceivable combinations of univariate and multivariate time series models. Finally, the multiple forecasting models are immediately integrated to provide a final one-day-ahead electricity demand forecast. The presented modeling and forecasting technique is implemented for the Nord Pool electricity market’s hourly electricity demand. Three accuracy indicators, a statistical test, and a graphical analysis are used to assess the performance of the proposed decomposition combination forecasting technique. Hence, the forecasting results demonstrate the efficiency and precision of the proposed decomposition combination forecasting technique. In addition, the final best combination model within the proposed forecasting framework is comparatively better than the best models proposed in the literature and standard benchmark models. Finally, we suggest that the decomposition combination forecasting approach developed in this study be employed to handle additional complicated power market forecasting challenges.

Keywords: Nord Pool electricity market; day-ahead electricity demand forecasting; decomposition combination method; univariate and multivariate times series 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: 2023
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