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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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