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A New Two-Stage Approach to Short Term Electrical Load Forecasting

Author

Listed:
  • Miloš Božić

    (Faculty of Electronic Engineering, University of Niš, Aleksandra Medvedeva 14, Niš 18000, Serbia)

  • Miloš Stojanović

    (School of Higher Technical Professional Education, Aleksandra Medvedevа 20, Niš 18000, Serbia)

  • Zoran Stajić

    (Faculty of Electronic Engineering, University of Niš, Aleksandra Medvedeva 14, Niš 18000, Serbia)

  • Dragan Tasić

    (Faculty of Electronic Engineering, University of Niš, Aleksandra Medvedeva 14, Niš 18000, Serbia)

Abstract

In the deregulated energy market, the accuracy of load forecasting has a significant effect on the planning and operational decision making of utility companies. Electric load is a random non-stationary process influenced by a number of factors which make it difficult to model. To achieve better forecasting accuracy, a wide variety of models have been proposed. These models are based on different mathematical methods and offer different features. This paper presents a new two-stage approach for short-term electrical load forecasting based on least-squares support vector machines. With the aim of improving forecasting accuracy, one more feature was added to the model feature set, the next day average load demand. As this feature is unknown for one day ahead, in the first stage, forecasting of the next day average load demand is done and then used in the model in the second stage for next day hourly load forecasting. The effectiveness of the presented model is shown on the real data of the ISO New England electricity market. The obtained results confirm the validity advantage of the proposed approach.

Suggested Citation

  • Miloš Božić & Miloš Stojanović & Zoran Stajić & Dragan Tasić, 2013. "A New Two-Stage Approach to Short Term Electrical Load Forecasting," Energies, MDPI, vol. 6(4), pages 1-19, April.
    • Handle: RePEc:gam:jeners:v:6:y:2013:i:4:p:2130-2148:d:25086
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    References listed on IDEAS

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    Cited by:

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    3. Xie, Guangrui & Chen, Xi & Weng, Yang, 2021. "Enhance load forecastability: Optimize data sampling policy by reinforcing user behaviors," European Journal of Operational Research, Elsevier, vol. 295(3), pages 924-934.
    4. Cheng-Ming Lee & Chia-Nan Ko, 2016. "Short-Term Load Forecasting Using Adaptive Annealing Learning Algorithm Based Reinforcement Neural Network," Energies, MDPI, vol. 9(12), pages 1-15, November.

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