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Sectoral Energy Demand Forecasting under an Assumption-Free Data-Driven Technique

Author

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  • Bismark Ameyaw

    (School of Management and Economics, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China)

  • Li Yao

    (School of Management and Economics, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China)

Abstract

In order to implement sustainable economic policies, realistic and high accuracy demand projections are key to drawing and implementing realizable environmentally-friendly energy policies. However, some core energy models projections depict considerably high forecast inaccuracies in their previous projections. The inaccuracies are due to the massive assumption-driven variables whose assumptions and scenarios typically deviate from their realized levels. Here, we propose a high-accuracy assumption-free own-data-driven technique that utilizes zero of the traditional determinants as well as assumptions or scenarios for sectorial energy demand forecasting; and implement it in the United States (U.S.). The results show that the forecast accuracy of our gated recurrent network presents an enormous improvement on Annual Energy Outlook 2008 forecast projections. With evidence that our proposed sequential algorithm outperformed Annual Energy Outlook 2008 forecast projections, our proposed algorithm will guide policymakers in making sustainable energy-related policies in the near future. Although future realized consumption levels are unknown, we present our estimated projections along with Annual Energy Outlook 2018 projections to inform policymakers on future energy demands for the commercial sector, industrial sector, residential sector, and transportation.

Suggested Citation

  • Bismark Ameyaw & Li Yao, 2018. "Sectoral Energy Demand Forecasting under an Assumption-Free Data-Driven Technique," Sustainability, MDPI, vol. 10(7), pages 1-20, July.
  • Handle: RePEc:gam:jsusta:v:10:y:2018:i:7:p:2348-:d:156555
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    References listed on IDEAS

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    Cited by:

    1. Ameyaw, Bismark & Yao, Li & Oppong, Amos & Agyeman, Joy Korang, 2019. "Investigating, forecasting and proposing emission mitigation pathways for CO2 emissions from fossil fuel combustion only: A case study of selected countries," Energy Policy, Elsevier, vol. 130(C), pages 7-21.
    2. João Vitor Leme & Wallace Casaca & Marilaine Colnago & Maurício Araújo Dias, 2020. "Towards Assessing the Electricity Demand in Brazil: Data-Driven Analysis and Ensemble Learning Models," Energies, MDPI, vol. 13(6), pages 1-20, March.
    3. Bismark Ameyaw & Li Yao, 2018. "Analyzing the Impact of GDP on CO 2 Emissions and Forecasting Africa’s Total CO 2 Emissions with Non-Assumption Driven Bidirectional Long Short-Term Memory," Sustainability, MDPI, vol. 10(9), pages 1-23, August.
    4. Mustapha Mukhtar & Bismark Ameyaw & Nasser Yimen & Quixin Zhang & Olusola Bamisile & Humphrey Adun & Mustafa Dagbasi, 2021. "Building Retrofit and Energy Conservation/Efficiency Review: A Techno-Environ-Economic Assessment of Heat Pump System Retrofit in Housing Stock," Sustainability, MDPI, vol. 13(2), pages 1-23, January.

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