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

Author

Listed:
  • Fatemi Ardestani, Seyed Farshad

    (Faculty of Management and Economics, Sharif University of Technology, Tehran, Iran.)

  • Barakchian, Seyed Mahdi

    (Institute for Management and Planning Studies, Tehran, Iran.)

  • Shokoohian, Hamideh

    (Faculty of Management and Economics, Sharif University of Technology, Tehran, Iran.)

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.

Suggested Citation

  • Fatemi Ardestani, Seyed Farshad & Barakchian, Seyed Mahdi & Shokoohian, Hamideh, 2020. "Short-term Forecast of Hourly Electricity Demand in Iran Using a Forecast Combination Method (in Persian)," The Journal of Planning and Budgeting (٠صلنامه برنامه ریزی Ùˆ بودجه), Institute for Management and Planning studies, vol. 24(4), pages 57-73, February.
    • Handle: RePEc:auv:jipbud:v:24:y:2020:i:4:p:57-73
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    More about this item

    Keywords

    Electricity Demand; Short-term Forecast; Forecast Combination; Time-series Modeling; Time-series Decomposition.;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

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