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A hybrid machine learning model for forecasting a billing period’s peak electric load days

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  • Saxena, Harshit
  • Aponte, Omar
  • McConky, Katie T.

Abstract

Many models have been studied for forecasting the peak electric load, but studies focusing on forecasting peak electric load days for a billing period are scarce. This focus is highly relevant to consumers, as their electricity costs are determined based not only on total consumption, but also on the peak load required during a period. Forecasting these peak days accurately allows demand response actions to be planned and executed efficiently in order to mitigate these peaks and their associated costs. We propose a hybrid model based on ARIMA, logistic regression and artificial neural networks models. This hybrid model evaluates the individual results of these statistical and machine learning models in order to forecast whether a given day will be a peak load day for the billing period. The proposed model predicted 70% (40/57) of actual peak load days accurately and revealed potential savings of approximately USD $80,000 for an American university during a one-year testing period.

Suggested Citation

  • Saxena, Harshit & Aponte, Omar & McConky, Katie T., 2019. "A hybrid machine learning model for forecasting a billing period’s peak electric load days," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1288-1303.
    • Handle: RePEc:eee:intfor:v:35:y:2019:i:4:p:1288-1303
    DOI: 10.1016/j.ijforecast.2019.03.025
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    2. Ghafoori, Mahdi & Abdallah, Moatassem & Kim, Serena, 2023. "Electricity peak shaving for commercial buildings using machine learning and vehicle to building (V2B) system," Applied Energy, Elsevier, vol. 340(C).
    3. Yang, Dongchuan & Guo, Ju-e & Sun, Shaolong & Han, Jing & Wang, Shouyang, 2022. "An interval decomposition-ensemble approach with data-characteristic-driven reconstruction for short-term load forecasting," Applied Energy, Elsevier, vol. 306(PA).
    4. Boza, Pal & Evgeniou, Theodoros, 2021. "Artificial intelligence to support the integration of variable renewable energy sources to the power system," Applied Energy, Elsevier, vol. 290(C).
    5. 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.
    6. Hao, Jun & Feng, Qianqian & Yuan, Jiaxin & Sun, Xiaolei & Li, Jianping, 2022. "A dynamic ensemble learning with multi-objective optimization for oil prices prediction," Resources Policy, Elsevier, vol. 79(C).
    7. Warut Pannakkong & Vu Thanh Vinh & Nguyen Ngoc Minh Tuyen & Jirachai Buddhakulsomsiri, 2023. "A Reinforcement Learning Approach for Ensemble Machine Learning Models in Peak Electricity Forecasting," Energies, MDPI, vol. 16(13), pages 1-20, July.

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