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Parallel Multi-Model Energy Demand Forecasting with Cloud Redundancy: Leveraging Trend Correction, Feature Selection, and Machine Learning

Author

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  • Kamran Hassanpouri Baesmat

    (Department of ECE, University of Nevada, Las Vegas, NV 89154, USA)

  • Zeinab Farrokhi

    (Department of ECE, University of Nevada, Las Vegas, NV 89154, USA)

  • Grzegorz Chmaj

    (Department of ECE, University of Nevada, Las Vegas, NV 89154, USA)

  • Emma E. Regentova

    (Department of ECE, University of Nevada, Las Vegas, NV 89154, USA)

Abstract

In this work, we present a novel approach for predicting short-term electrical energy consumption. Most energy consumption methods work well for their case study datasets. The proposed method utilizes a cloud computing platform that allows for integrating information from different sources, such as weather data and historical energy consumption, while employing machine learning techniques to achieve higher accuracy in forecasting. We collected detailed weather data from the “Weather Underground Company” website, known for its accurate records. Then, we studied past energy consumption data provided by PJM (focusing on DEO&K, which serves Cincinnati and northern Kentucky) and identified features that significantly impact energy consumption. We also introduced a processing step to ensure accurate predictions for holidays. Our goal is to predict the next 24 h of load consumption. We developed a hybrid, generalizable forecasting methodology with deviation correction. The methodology is characterized by fault tolerance due to distributed cloud deployment and an introduced voting mechanism. The proposed approach improved the accuracy of LSTM, SARIMAX, and SARIMAX + SVM, with MAPE values of 5.17%, 4.21%, and 2.21% reduced to 1.65%, 1.00%, and 0.88%, respectively, using our CM-LSTM-DC, CM-SARIMAX-DC, and CM-SARIMAX + SVM-DC models.

Suggested Citation

  • Kamran Hassanpouri Baesmat & Zeinab Farrokhi & Grzegorz Chmaj & Emma E. Regentova, 2025. "Parallel Multi-Model Energy Demand Forecasting with Cloud Redundancy: Leveraging Trend Correction, Feature Selection, and Machine Learning," Forecasting, MDPI, vol. 7(2), pages 1-18, May.
    • Handle: RePEc:gam:jforec:v:7:y:2025:i:2:p:25-:d:1669567
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    References listed on IDEAS

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    1. Abdel-Aal, R.E. & Al-Garni, A.Z. & Al-Nassar, Y.N., 1997. "Modelling and forecasting monthly electric energy consumption in eastern Saudi Arabia using abductive networks," Energy, Elsevier, vol. 22(9), pages 911-921.
    2. 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.
    3. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
    4. Sun-Youn Shin & Han-Gyun Woo, 2022. "Energy Consumption Forecasting in Korea Using Machine Learning Algorithms," Energies, MDPI, vol. 15(13), pages 1-20, July.
    5. Prince Waqas Khan & Yung-Cheol Byun & Sang-Joon Lee & Namje Park, 2020. "Machine Learning Based Hybrid System for Imputation and Efficient Energy Demand Forecasting," Energies, MDPI, vol. 13(11), pages 1-23, May.
    6. Monika Zielińska-Sitkiewicz & Mariola Chrzanowska & Konrad Furmańczyk & Kacper Paczutkowski, 2021. "Analysis of Electricity Consumption in Poland Using Prediction Models and Neural Networks," Energies, MDPI, vol. 14(20), pages 1-21, October.
    7. Javeed Nizami, SSAK & Al-Garni, Ahmed Z, 1995. "Forecasting electric energy consumption using neural networks," Energy Policy, Elsevier, vol. 23(12), pages 1097-1104, December.
    8. Konrad Bogner & Florian Pappenberger & Massimiliano Zappa, 2019. "Machine Learning Techniques for Predicting the Energy Consumption/Production and Its Uncertainties Driven by Meteorological Observations and Forecasts," Sustainability, MDPI, vol. 11(12), pages 1-22, June.
    9. Federico Divina & Miguel García Torres & Francisco A. Goméz Vela & José Luis Vázquez Noguera, 2019. "A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings," Energies, MDPI, vol. 12(10), pages 1-23, May.
    10. Prince Waqas Khan & Yongjun Kim & Yung-Cheol Byun & Sang-Joon Lee, 2021. "Influencing Factors Evaluation of Machine Learning-Based Energy Consumption Prediction," Energies, MDPI, vol. 14(21), pages 1-22, November.
    11. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    12. Jie Ma & Amos Oppong & Kingsley Nketia Acheampong & Lucille Aba Abruquah, 2018. "Forecasting Renewable Energy Consumption under Zero Assumptions," Sustainability, MDPI, vol. 10(3), pages 1-17, February.
    13. Bożena Gajdzik & Radosław Wolniak & Rafał Nagaj & Brigita Žuromskaitė-Nagaj & Wieslaw Wes Grebski, 2024. "The Influence of the Global Energy Crisis on Energy Efficiency: A Comprehensive Analysis," Energies, MDPI, vol. 17(4), pages 1-51, February.
    14. Hafeez, Ghulam & Alimgeer, Khurram Saleem & Khan, Imran, 2020. "Electric load forecasting based on deep learning and optimized by heuristic algorithm in smart grid," Applied Energy, Elsevier, vol. 269(C).
    15. Chou, Jui-Sheng & Ngo, Ngoc-Tri, 2016. "Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns," Applied Energy, Elsevier, vol. 177(C), pages 751-770.
    16. Upma Singh & Mohammad Rizwan & Muhannad Alaraj & Ibrahim Alsaidan, 2021. "A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments," Energies, MDPI, vol. 14(16), pages 1-21, August.
    17. Ahmad, Tanveer & Huanxin, Chen & Zhang, Dongdong & Zhang, Hongcai, 2020. "Smart energy forecasting strategy with four machine learning models for climate-sensitive and non-climate sensitive conditions," Energy, Elsevier, vol. 198(C).
    18. Beyca, Omer Faruk & Ervural, Beyzanur Cayir & Tatoglu, Ekrem & Ozuyar, Pinar Gokcin & Zaim, Selim, 2019. "Using machine learning tools for forecasting natural gas consumption in the province of Istanbul," Energy Economics, Elsevier, vol. 80(C), pages 937-949.
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