Short-term Forecast of Hourly Electricity Demand in Iran Using a Forecast Combination Method (in Persian)

Seyed Farshad Fatemi Ardestani (), Seyed Mahdi Barakchian () and Hamideh Shokoohian
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Seyed Farshad Fatemi Ardestani: Faculty of Management and Economics, Sharif University of Technology, Tehran, Iran.
Seyed Mahdi Barakchian: Institute for Management and Planning Studies, Tehran, Iran.
Hamideh Shokoohian: Faculty of Management and Economics, Sharif University of Technology, Tehran, Iran.

The Journal of Planning and Budgeting (٠صلنامه برنامه ریزی و بودجه), 2020, vol. 24, issue 4, 57-73

Abstract: The aim of this study is to present two time-series forecasting models and combine these models to provide a short-term prediction for hourly electricity demand, using daily electricity consumption data for the period 2006-2011. The first model is based on the decomposition of the electricity load into deterministic and stochastic components and the second model is based on the assumption that the electricity load is a stochastic time series. Once the hourly demand for electricity load is predicted using the above-mentioned models, the performance of the combined model is compared with the two time-series models and also with the dispatching unit model (a multi-variable model in which the weather variable is also included). The results show that the use of the combined model leads to an increase in prediction accuracy over the two time-series models. Moreover, the accuracy of the combined model is as good as the dispatching unit model for most of the time during the day, and even better during the consumption peak hours.

Keywords: Electricity Demand; Short-term Forecast; Forecast Combination; Time-series Modeling; Time-series Decomposition. (search for similar items in EconPapers)
JEL-codes: C32 C53 L94 (search for similar items in EconPapers)
Date: 2020
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