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Peak-Load Forecasting for Small Industries: A Machine Learning Approach

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  • Dong-Hoon Kim

    (Department of Information and Telecommunication Engineering, Incheon Nat’l University, Incheon 22012, Korea)

  • Eun-Kyu Lee

    (Department of Information and Telecommunication Engineering, Incheon Nat’l University, Incheon 22012, Korea)

  • Naik Bakht Sania Qureshi

    (Department of Information and Telecommunication Engineering, Incheon Nat’l University, Incheon 22012, Korea)

Abstract

Peak-load forecasting prevents energy waste and helps with environmental issues by establishing plans for the use of renewable energy. For that reason, the subject is still actively studied. Most of these studies are focused on improving predictive performance by using varying feature information, but most small industrial facilities cannot provide such information because of a lack of infrastructure. Therefore, we introduce a series of studies to implement a generalized prediction model that is applicable to these small industrial facilities. On the basis of the pattern of load information of most industrial facilities, new features were selected, and a generalized model was developed through the aggregation of ensemble models. In addition, a new method is proposed to improve prediction performance by providing additional compensation to the prediction results by reflecting the fewest opinions among the prediction results of each model. Actual data from two small industrial facilities were applied to our process, and the results proved the effectiveness of our proposed method.

Suggested Citation

  • Dong-Hoon Kim & Eun-Kyu Lee & Naik Bakht Sania Qureshi, 2020. "Peak-Load Forecasting for Small Industries: A Machine Learning Approach," Sustainability, MDPI, vol. 12(16), pages 1-19, August.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:16:p:6539-:d:398335
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    References listed on IDEAS

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    1. Amara-Ouali, Yvenn & Fasiolo, Matteo & Goude, Yannig & Yan, Hui, 2023. "Daily peak electrical load forecasting with a multi-resolution approach," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1272-1286.
    2. Joohyun Jang & Woonyoung Jeong & Sangmin Kim & Byeongcheon Lee & Miyoung Lee & Jihoon Moon, 2023. "RAID: Robust and Interpretable Daily Peak Load Forecasting via Multiple Deep Neural Networks and Shapley Values," Sustainability, MDPI, vol. 15(8), pages 1-27, April.
    3. Ming-Fong Tsai & Yen-Ching Chu & Min-Hao Li & Lien-Wu Chen, 2020. "Smart Machinery Monitoring System with Reduced Information Transmission and Fault Prediction Methods Using Industrial Internet of Things," Mathematics, MDPI, vol. 9(1), pages 1-14, December.
    4. Bibi Ibrahim & Luis Rabelo, 2021. "A Deep Learning Approach for Peak Load Forecasting: A Case Study on Panama," Energies, MDPI, vol. 14(11), pages 1-26, May.

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