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An Ensemble Stochastic Forecasting Framework for Variable Distributed Demand Loads

Author

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  • Kofi Afrifa Agyeman

    (Electrical Engineering Department, Kyungpook National University, Daegu 41566, Korea)

  • Gyeonggak Kim

    (Electrical Engineering Department, Kyungpook National University, Daegu 41566, Korea)

  • Hoonyeon Jo

    (Electrical Engineering Department, Kyungpook National University, Daegu 41566, Korea)

  • Seunghyeon Park

    (Electrical Engineering Department, Kyungpook National University, Daegu 41566, Korea)

  • Sekyung Han

    (Electrical Engineering Department, Kyungpook National University, Daegu 41566, Korea)

Abstract

Accurate forecasting of demand load is momentous for the efficient economic dispatch of generating units with enormous economic and reliability implications. However, with the high integration levels of grid-tie generations, the precariousness in demand load forecasts is unreliable. This paper proposes a data-driven stochastic ensemble model framework for short-term and long-term demand load forecasts. Our proposed framework reduces uncertainties in the load forecast by fusing homogenous models that capture the dynamics in load state characteristics and exploit model diversities for accurate prediction. The ensemble model caters for factors such as meteorological and exogenous variables that affect load prediction accuracy with adaptable, scalable algorithms that consider weather conditions, load features, and state characteristics of the load. We defined a heuristic trained combiner model and an error correction model to estimate the contributions and compensate for forecast errors of each prediction model, respectively. Acquired data from the Korean Electric Power Company (KEPCO), and building data from the Korea Research Institute, together with testbed datasets, were used to evaluate the developed framework. The results obtained prove the efficacy of the proposed model for demand load forecasting.

Suggested Citation

  • Kofi Afrifa Agyeman & Gyeonggak Kim & Hoonyeon Jo & Seunghyeon Park & Sekyung Han, 2020. "An Ensemble Stochastic Forecasting Framework for Variable Distributed Demand Loads," Energies, MDPI, vol. 13(10), pages 1-20, May.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:10:p:2658-:d:362784
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    References listed on IDEAS

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    1. Jain, Rishee K. & Smith, Kevin M. & Culligan, Patricia J. & Taylor, John E., 2014. "Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy," Applied Energy, Elsevier, vol. 123(C), pages 168-178.
    2. Xenos, Dionysios P. & Mohd Noor, Izzati & Matloubi, Mitra & Cicciotti, Matteo & Haugen, Trond & Thornhill, Nina F., 2016. "Demand-side management and optimal operation of industrial electricity consumers: An example of an energy-intensive chemical plant," Applied Energy, Elsevier, vol. 182(C), pages 418-433.
    3. Spiliotis, Konstantinos & Ramos Gutierrez, Ariana Isabel & Belmans, Ronnie, 2016. "Demand flexibility versus physical network expansions in distribution grids," Applied Energy, Elsevier, vol. 182(C), pages 613-624.
    4. Wang, Chi-hsiang & Grozev, George & Seo, Seongwon, 2012. "Decomposition and statistical analysis for regional electricity demand forecasting," Energy, Elsevier, vol. 41(1), pages 313-325.
    5. Raza, Muhammad Qamar & Khosravi, Abbas, 2015. "A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1352-1372.
    6. Nosratabadi, Seyyed Mostafa & Hooshmand, Rahmat-Allah & Gholipour, Eskandar, 2017. "A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 341-363.
    7. Fan, Cheng & Xiao, Fu & Wang, Shengwei, 2014. "Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques," Applied Energy, Elsevier, vol. 127(C), pages 1-10.
    8. Deihimi, Ali & Orang, Omid & Showkati, Hemen, 2013. "Short-term electric load and temperature forecasting using wavelet echo state networks with neural reconstruction," Energy, Elsevier, vol. 57(C), pages 382-401.
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    2. Daisuke Kodaira & Kazuki Tsukazaki & Taiki Kure & Junji Kondoh, 2021. "Improving Forecast Reliability for Geographically Distributed Photovoltaic Generations," Energies, MDPI, vol. 14(21), pages 1-15, November.

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