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

Kamran Hassanpouri Baesmat (), Zeinab Farrokhi, Grzegorz Chmaj and Emma E. Regentova
Additional contact information
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

Forecasting, 2025, vol. 7, issue 2, 1-18

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.

Keywords: IoT; cloud computing; machine learning; feature selection; electrical load prediction; power systems; statistical methods; deep learning; artificial intelligence; deviation correction (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2571-9394/7/2/25/pdf (application/pdf)
https://www.mdpi.com/2571-9394/7/2/25/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jforec:v:7:y:2025:i:2:p:25-:d:1669567

Access Statistics for this article

Forecasting is currently edited by Ms. Joss Chen

More articles in Forecasting from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().