Parallel Multi-Model Energy Demand Forecasting with Cloud Redundancy: Leveraging Trend Correction, Feature Selection, and Machine Learning
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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Forecasting |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2571-9394/7/2/25 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849432587613765632 |
|---|---|
| author | Kamran Hassanpouri Baesmat Zeinab Farrokhi Grzegorz Chmaj Emma E. Regentova |
| author_facet | Kamran Hassanpouri Baesmat Zeinab Farrokhi Grzegorz Chmaj Emma E. Regentova |
| author_sort | Kamran Hassanpouri Baesmat |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d35b2a67f9e3475c816befd7bf8b1f1b |
| institution | Kabale University |
| issn | 2571-9394 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forecasting |
| spelling | doaj-art-d35b2a67f9e3475c816befd7bf8b1f1b2025-08-20T03:27:18ZengMDPI AGForecasting2571-93942025-05-01722510.3390/forecast7020025Parallel Multi-Model Energy Demand Forecasting with Cloud Redundancy: Leveraging Trend Correction, Feature Selection, and Machine LearningKamran Hassanpouri Baesmat0Zeinab Farrokhi1Grzegorz Chmaj2Emma E. Regentova3Department of ECE, University of Nevada, Las Vegas, NV 89154, USADepartment of ECE, University of Nevada, Las Vegas, NV 89154, USADepartment of ECE, University of Nevada, Las Vegas, NV 89154, USADepartment of ECE, University of Nevada, Las Vegas, NV 89154, USAIn 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.https://www.mdpi.com/2571-9394/7/2/25IoTcloud computingmachine learningfeature selectionelectrical load predictionpower systems |
| spellingShingle | Kamran Hassanpouri Baesmat Zeinab Farrokhi Grzegorz Chmaj Emma E. Regentova Parallel Multi-Model Energy Demand Forecasting with Cloud Redundancy: Leveraging Trend Correction, Feature Selection, and Machine Learning Forecasting IoT cloud computing machine learning feature selection electrical load prediction power systems |
| title | Parallel Multi-Model Energy Demand Forecasting with Cloud Redundancy: Leveraging Trend Correction, Feature Selection, and Machine Learning |
| title_full | Parallel Multi-Model Energy Demand Forecasting with Cloud Redundancy: Leveraging Trend Correction, Feature Selection, and Machine Learning |
| title_fullStr | Parallel Multi-Model Energy Demand Forecasting with Cloud Redundancy: Leveraging Trend Correction, Feature Selection, and Machine Learning |
| title_full_unstemmed | Parallel Multi-Model Energy Demand Forecasting with Cloud Redundancy: Leveraging Trend Correction, Feature Selection, and Machine Learning |
| title_short | Parallel Multi-Model Energy Demand Forecasting with Cloud Redundancy: Leveraging Trend Correction, Feature Selection, and Machine Learning |
| title_sort | parallel multi model energy demand forecasting with cloud redundancy leveraging trend correction feature selection and machine learning |
| topic | IoT cloud computing machine learning feature selection electrical load prediction power systems |
| url | https://www.mdpi.com/2571-9394/7/2/25 |
| work_keys_str_mv | AT kamranhassanpouribaesmat parallelmultimodelenergydemandforecastingwithcloudredundancyleveragingtrendcorrectionfeatureselectionandmachinelearning AT zeinabfarrokhi parallelmultimodelenergydemandforecastingwithcloudredundancyleveragingtrendcorrectionfeatureselectionandmachinelearning AT grzegorzchmaj parallelmultimodelenergydemandforecastingwithcloudredundancyleveragingtrendcorrectionfeatureselectionandmachinelearning AT emmaeregentova parallelmultimodelenergydemandforecastingwithcloudredundancyleveragingtrendcorrectionfeatureselectionandmachinelearning |