Adaptive Ensemble Learning for Enhancing Building Energy Consumption Prediction: Insights from COVID-19 Pandemic Energy Consumption Dynamics

Putu Handre Kertha Utama; Edi Leksono; Irsyad Nashirul Haq; Rachmadi Indrapraja; Rezky Mahesa Nanda; Koko Friansa; Reza Fauzi Iskandar; Justin Pradipta

doi:10.5614/j.eng.technol.sci.2025.57.2.2

Adaptive Ensemble Learning for Enhancing Building Energy Consumption Prediction: Insights from COVID-19 Pandemic Energy Consumption Dynamics

Published: 2025-01-01 | DOI: 10.5614/j.eng.technol.sci.2025.57.2.2 | Vol 57, Issue 2 (2025)

Authors

Putu Handre Kertha Utama

OpenAlex: A5071468074

Edi Leksono

ORCID: 0000-0002-8129-5685 · OpenAlex: A5068485094

Irsyad Nashirul Haq

ORCID: 0000-0002-2241-5755 · OpenAlex: A5050832703

Rachmadi Indrapraja

OpenAlex: A5116161316

Rezky Mahesa Nanda

ORCID: 0000-0002-1366-4397 · OpenAlex: A5110976842

Koko Friansa

ORCID: 0000-0002-2503-7753 · OpenAlex: A5022960108

Reza Fauzi Iskandar

ORCID: 0009-0001-0572-4521 · OpenAlex: A5075975163

Justin Pradipta

ORCID: 0000-0001-6419-3823 · OpenAlex: A5011861503

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Abstract

Buildings account for approximately 40% of the total global energy consumption. Therefore, accurate prediction of building energy consumption is necessary to optimize resource allocation and promote sustainable energy usage. A key challenge in developing building energy consumption models is their adaptability to abrupt changes in consumption patterns owing to extraordinary events, such as the COVID-19 pandemic. Therefore, a two-layer ensemble-learning (EL) model incorporating sliding windows as input features is proposed. The model is a two-layer stacking EL consisting of two base learning methods: (1) support vector regression (SVR), and (2) random forest (RF). Temperature and humidity are included to account for the influence of weather conditions on energy consumption. The proposed model is deployed to forecast building energy consumption both before (November 2019) and during (May – October 2020) the COVID-19 pandemic and is compared with a single machine learning model. The results demonstrate that the EL model outperforms the SVR and RF methods, providing excellent prediction accuracy even during the pandemic when significant changes in energy consumption patterns occurred. The findings also highlight the effectiveness of sliding windows as input features for improving model adaptability. Additionally, the analysis reveals that temperature is more prominent than humidity for improving prediction accuracy.

Keywords

building energy consumption; energy consumptions prediction; ensemble learning; random forest; sliding windows; support vector regression

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Energy Load and Power Forecasting Primary

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Electrical and Electronic Engineering › Engineering › Physical Sciences

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Consumption (sociology) 0.64

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Energy (signal processing) 0.51 Environmental science 0.49 Computer science 0.38 Mathematics 0.22 Statistics 0.21

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Bandung Institute of Technology ID
Putu Handre Kertha Utama · Edi Leksono · Irsyad Nashirul Haq · Rachmadi Indrapraja · Rezky Mahesa Nanda · Koko Friansa · Justin Pradipta
Telkom University ID
Reza Fauzi Iskandar

References

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[ref-1] Ahmad, M. W., Mourshed, M., & Rezgui, Y. (2017). Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption. Energy and Buildings, 147, 77–89. https://doi.org/https://doi.org/10.1016/j.enbuild.2017.04.038 DOI: 10.1016/j.enbuild.2017.04.038

[ref-2] Arjomandi-Nezhad, A., Ahmadi, A., Taheri, S., Fotuhi-Firuzabad, M., Moeini-Aghtaie, M., & Lehtonen, M. (2022). Pandemic-Aware Day-Ahead Demand Forecasting Using Ensemble Learning. IEEE Access, 10, 7098–7106. https://doi.org/10.1109/ACCESS.2022.3142351 DOI: 10.1109/access.2022.3142351

[ref-3] Asim, N., Badiei, M., Mohammad, M., Razali, H., Rajabi, A., Chin Haw, L., & Jameelah Ghazali, M. (2022). Sustainability of Heating, Ventilation and Air-Conditioning (HVAC) Systems in Buildings—An Overview. International Journal of Environmental Research and Public Health, 19(2). https://doi.org/10.3390/ijerph19021016 DOI: 10.3390/ijerph19021016

[ref-4] Ayele, W. Y. (2020). Adapting CRISP-DM for idea mining a data mining process for generating ideas using a textual dataset. International Journal of Advanced Computer Science and Applications, 11(6), 20–32. https://doi.org/10.14569/IJACSA.2020.0110603 DOI: 10.14569/ijacsa.2020.0110603

[ref-5] Bošnjak, Z., Grljević, O., & Bošnjak, S. (2009). CRISP-DM as a framework for discovering knowledge in small and medium sized enterprises’ data. Proceedings - 2009 5th International Symposium on Applied Computational Intelligence and Informatics, SACI 2009, June, 509–514. https://doi.org/10.1109/SACI.2009.5136302 DOI: 10.1109/saci.2009.5136302

[ref-6] Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324 DOI: 10.1023/a:1010933404324

[ref-7] Cao, L., Li, Y., Zhang, J., Jiang, Y., Han, Y., & Wei, J. (2020). Electrical load prediction of healthcare buildings through single and ensemble learning. Energy Reports, 6, 2751–2767. https://doi.org/10.1016/j.egyr.2020.10.005 DOI: 10.1016/j.egyr.2020.10.005

[ref-8] Cha, G.-W., Moon, H.-J., & Kim, Y.-C. (2021). Comparison of Random Forest and Gradient Boosting Machine Models for Predicting Demolition Waste Based on Small Datasets and Categorical Variables. International Journal of Environmental Research and Public Health, 18(16). https://doi.org/10.3390/ijerph18168530 DOI: 10.3390/ijerph18168530

[ref-9] Chou, J.-S., Telaga, A. S., Chong, W. K., & Gibson, G. E. (2017). Early-warning application for real-time detection of energy consumption anomalies in buildings. Journal of Cleaner Production, 149, 711–722. https://doi.org/https://doi.org/10.1016/j.jclepro.2017.02.028 DOI: 10.1016/j.jclepro.2017.02.028

[ref-10] Chung, Y. W., Khaki, B., Li, T., Chu, C., & Gadh, R. (2019). Ensemble machine learning-based algorithm for electric vehicle user behavior prediction. In Applied Energy (Vol. 254). Elsevier Ltd. https://doi.org/10.1016/j.apenergy.2019.113732 DOI: 10.1016/j.apenergy.2019.113732

[ref-11] Divina, F., Gilson, A., Goméz-Vela, F., García Torres, M., & Torres, J. F. (2018). Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting. Energies, 11(4). https://doi.org/10.3390/en11040949 DOI: 10.3390/en11040949

[ref-12] Friansa, K., Pradipta, J., Haq, I. N., Utama, P. H. K., Wasesa, M., & Leksono, E. (2023). Enhancing HVAC Electricity Load Prediction Accuracy using Bi-LSTM Method based on Daily Dataset. 2023 8th International Conference on Instrumentation, Control, and Automation (ICA), 92–96. https://doi.org/10.1109/ICA58538.2023.10273121 DOI: 10.1109/ica58538.2023.10273121

[ref-13] Guo, K., Wan, X., Liu, L., Gao, Z., & Yang, M. (2021). Fault Diagnosis of Intelligent Production Line Based on Digital Twin and Improved Random Forest. Applied Sciences, 11(16). https://doi.org/10.3390/app11167733 DOI: 10.3390/app11167733

[ref-14] Hansen, L. K., & Salamon, P. (1990). Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(10), 993–1001. https://doi.org/10.1109/34.58871 DOI: 10.1109/34.58871

[ref-15] Jaramillo, M., & Carrión, D. (2022). An Adaptive Strategy for Medium-Term Electricity Consumption Forecasting for Highly Unpredictable Scenarios: Case Study Quito, Ecuador during the Two First Years of COVID-19. Energies, 15(22). https://doi.org/10.3390/en15228380 DOI: 10.3390/en15228380

[ref-16] Ji, Q., Zhang, S., Duan, Q., Gong, Y., Li, Y., Xie, X., Bai, J., Huang, C., & Zhao, X. (2022). Short- and Medium-Term Power Demand Forecasting with Multiple Factors Based on Multi-Model Fusion. Mathematics, 10(12). https://doi.org/10.3390/math10122148 DOI: 10.3390/math10122148

[ref-17] Jozi, A., Pinto, T., & Vale, Z. (2022). Contextual learning for energy forecasting in buildings. International Journal of Electrical Power & Energy Systems, 136, 107707. https://doi.org/https://doi.org/10.1016/j.ijepes.2021.107707 DOI: 10.1016/j.ijepes.2021.107707

[ref-18] Kelo, S., & Dudul, S. (2012). A wavelet Elman neural network for short-term electrical load prediction under the influence of temperature. International Journal of Electrical Power & Energy Systems, 43(1), 1063–1071. https://doi.org/https://doi.org/10.1016/j.ijepes.2012.06.009 DOI: 10.1016/j.ijepes.2012.06.009

[ref-19] Khairalla, M. A., Ning, X., AL-Jallad, N. T., & El-Faroug, M. O. (2018). Short-Term Forecasting for Energy Consumption through Stacking Heterogeneous Ensemble Learning Model. Energies, 11(6). https://doi.org/10.3390/en11061605 DOI: 10.3390/en11061605

[ref-20] Klyuev, R. V, Morgoev, I. D., Morgoeva, A. D., Gavrina, O. A., Martyushev, N. V, Efremenkov, E. A., & Mengxu, Q. (2022). Methods of Forecasting Electric Energy Consumption: A Literature Review. Energies, 15(23). https://doi.org/10.3390/en15238919 DOI: 10.3390/en15238919

[ref-21] Kumar, A., Nandal, R., & Joshi, K. (2024). A Structured Literature Review and Meta-analysis of Forecasting Methods for Energy Consumption in Smart Buildings (pp. 37–62). https://doi.org/10.1007/978-981-97-1961-7_3 DOI: 10.1007/978-981-97-1961-7_3

[ref-22] Lee, J., & Cho, Y. (2022). National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model? Energy, 239, 122366. https://doi.org/https://doi.org/10.1016/j.energy.2021.122366 DOI: 10.1016/j.energy.2021.122366

[ref-23] Leksono, E., Mandhany, A., Nashirul Haq, I., Pradipta, J., Handre Kertha Utama, P., Fauzi Iskandar, R., & Mahesa

[ref-24] Nanda, R. (2023). Development of Non-Intrusive Load Monitoring of Electricity Load Classification with Low-Frequency Sampling Based on Support Vector Machine. Journal of Engineering and Technological Sciences, 55(2), 109–119. https://doi.org/10.5614/j.eng.technol.sci.2023.55.2.1 DOI: 10.5614/j.eng.technol.sci.2023.55.2.1

[ref-25] Li, K., Zhang, J., Chen, X., & Xue, W. (2022). Building’s hourly electrical load prediction based on data clustering and ensemble learning strategy. Energy and Buildings, 261, 111943. https://doi.org/https://doi.org/10.1016/j.enbuild.2022.111943 DOI: 10.1016/j.enbuild.2022.111943

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