Smart Buildings Energy Consumption Forecasting using Adaptive Evolutionary Ensemble Learning Models
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918057973121024 |
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| author | Neshat, Mehdi Thilakaratne, Menasha El-Abd, Mohammed Mirjalili, Seyedali Gandomi, Amir H. Boland, John |
| author_facet | Neshat, Mehdi Thilakaratne, Menasha El-Abd, Mohammed Mirjalili, Seyedali Gandomi, Amir H. Boland, John |
| contents | Smart buildings are gaining popularity because they can enhance energy efficiency, lower costs, improve security, and provide a more comfortable and convenient environment for building occupants. A considerable portion of the global energy supply is consumed in the building sector and plays a pivotal role in future decarbonization pathways. To manage energy consumption and improve energy efficiency in smart buildings, developing reliable and accurate energy demand forecasting is crucial and meaningful. However, extending an effective predictive model for the total energy use of appliances at the building level is challenging because of temporal oscillations and complex linear and non-linear patterns. This paper proposes three hybrid ensemble predictive models, incorporating Bagging, Stacking, and Voting mechanisms combined with a fast and effective evolutionary hyper-parameters tuner. The performance of the proposed energy forecasting model was evaluated using a hybrid dataset comprising meteorological parameters, appliance energy use, temperature, humidity, and lighting energy consumption from various sections of a building, collected by 18 sensors located in Stambroek, Mons, Belgium. To provide a comparative framework and investigate the efficiency of the proposed predictive model, 15 popular machine learning (ML) models, including two classic ML models, three NNs, a Decision Tree (DT), a Random Forest (RF), two Deep Learning (DL) and six Ensemble models, were compared. The prediction results indicate that the adaptive evolutionary bagging model surpassed other predictive models in both accuracy and learning error. Notably, it achieved accuracy gains of 12.6%, 13.7%, 12.9%, 27.04%, and 17.4% compared to Extreme Gradient Boosting (XGB), Categorical Boosting (CatBoost), GBM, LGBM, and Random Forest (RF). |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_11864 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Smart Buildings Energy Consumption Forecasting using Adaptive Evolutionary Ensemble Learning Models Neshat, Mehdi Thilakaratne, Menasha El-Abd, Mohammed Mirjalili, Seyedali Gandomi, Amir H. Boland, John Neural and Evolutionary Computing Smart buildings are gaining popularity because they can enhance energy efficiency, lower costs, improve security, and provide a more comfortable and convenient environment for building occupants. A considerable portion of the global energy supply is consumed in the building sector and plays a pivotal role in future decarbonization pathways. To manage energy consumption and improve energy efficiency in smart buildings, developing reliable and accurate energy demand forecasting is crucial and meaningful. However, extending an effective predictive model for the total energy use of appliances at the building level is challenging because of temporal oscillations and complex linear and non-linear patterns. This paper proposes three hybrid ensemble predictive models, incorporating Bagging, Stacking, and Voting mechanisms combined with a fast and effective evolutionary hyper-parameters tuner. The performance of the proposed energy forecasting model was evaluated using a hybrid dataset comprising meteorological parameters, appliance energy use, temperature, humidity, and lighting energy consumption from various sections of a building, collected by 18 sensors located in Stambroek, Mons, Belgium. To provide a comparative framework and investigate the efficiency of the proposed predictive model, 15 popular machine learning (ML) models, including two classic ML models, three NNs, a Decision Tree (DT), a Random Forest (RF), two Deep Learning (DL) and six Ensemble models, were compared. The prediction results indicate that the adaptive evolutionary bagging model surpassed other predictive models in both accuracy and learning error. Notably, it achieved accuracy gains of 12.6%, 13.7%, 12.9%, 27.04%, and 17.4% compared to Extreme Gradient Boosting (XGB), Categorical Boosting (CatBoost), GBM, LGBM, and Random Forest (RF). |
| title | Smart Buildings Energy Consumption Forecasting using Adaptive Evolutionary Ensemble Learning Models |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2506.11864 |