Volume 24, Issue 4 (Winter 2020)
JPBUD 2020, 24(4): 57-73 |
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Fatemi Ardestani S F, Barakchian S M, Shokoohian H. (2020). Short-term Forecast of Hourly Electricity Demand in Iran Using a Forecast Combination Method. JPBUD. 24(4), 57-73. doi:10.29252/jpbud.24.4.57
URL: http://jpbud.ir/article-1-1668-en.html
URL: http://jpbud.ir/article-1-1668-en.html
1- Faculty of Management and Economics, Sharif University of Technology, Tehran, Iran. , ffatemi@sharif.ir
2- Institute for Management and Planning Studies, Tehran, Iran.
3- Faculty of Management and Economics, Sharif University of Technology, Tehran, Iran.
2- Institute for Management and Planning Studies, Tehran, Iran.
3- Faculty of Management and Economics, Sharif University of Technology, Tehran, Iran.
Abstract: (2563 Views)
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.
Type of Study: Research |
Subject:
public economics
Received: Aug 01 2018 | Accepted: Sep 09 2020 | ePublished: Nov 08 2020
Received: Aug 01 2018 | Accepted: Sep 09 2020 | ePublished: Nov 08 2020
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