Leveraging Feature Sets and Machine Learning for Enhanced Energy Load Prediction: A Comparative Analysis

Fernando Pedro Silva Almeida; Mauro Castelli; Nadine Cí´rte-Real

doi:10.28991/ESJ-2024-08-06-01

Authors

Fernando Pedro Silva Almeida
m20200957@novaims.unl.pt
NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide. 1070-312, Lisboa,, Portugal
Mauro Castelli NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide. 1070-312, Lisboa,, Portugal
Nadine Cí´rte-Real NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide. 1070-312, Lisboa,, Portugal

Vol. 8 No. 6 (2024): December

Research Articles

Downloads

PDF

Abstract
How to Cite
Metrics
References
License

Accurate cooling consumption forecasts are crucial for optimizing energy management, storage, and overall efficiency in interconnected HVAC systems. Weather conditions, building characteristics, and operational parameters significantly impact prediction accuracy. Since meteorological conditions highly influence cooling demand, leveraging external air data and user metrics offers a promising approach to estimate a building's hourly cooling energy usage. This study addresses the gap in existing research by comprehensively analyzing the performance of various machine learning algorithms, including ensemble learning and deep learning models, to improve prediction accuracy. By leveraging weather conditions, building characteristics, and operational parameters, we aim to predict cooling consumption across multiple systems (Cooling Ceiling, Ventilation, Free Cooling, and Total Cooling). Data from four weather stations, encompassing diverse features relevant to the European Central Bank (ECB) building's cooling consumption in Frankfurt, were employed. Our methodology includes the use of K-Nearest Neighbor, Decision Tree, Support Vector Regression, Linear Regression, Random Forest, Gradient Boosting, XGBoost, Adaboost, Long-Short-Term Memory, and Gated Recurrent Unit. Models. The results consistently demonstrate the superiority of the Random Forest model across different weather stations and feature sets. This model achieved a Mean Squared Error of approximately 0.002-0.003, Mean Absolute Error of around 0.031-0.034, and Root Mean Squared Error of about 0.052-0.069. These findings contribute to improved building cooling load management, promoting insights into optimal energy utilization and sustainable building practices.

Doi: 10.28991/ESJ-2024-08-06-01

Full Text: PDF

Almeida, F. P. S., Castelli, M., & Cí´rte-Real, N. (2024). Leveraging Feature Sets and Machine Learning for Enhanced Energy Load Prediction: A Comparative Analysis. Emerging Science Journal, 8(6), 2120–2143. https://doi.org/10.28991/ESJ-2024-08-06-01

Download Citation

Almuhaini, S. H., & Sultana, N. (2023). Forecasting Long-Term Electricity Consumption in Saudi Arabia Based on Statistical and Machine Learning Algorithms to Enhance Electric Power Supply Management. Energies, 16(4), 2035. doi:10.3390/en16042035.

Zou, X., Wang, R., Hu, G., Rong, Z., & Li, J. (2022). CO2 Emissions Forecast and Emissions Peak Analysis in Shanxi Province, China: An Application of the LEAP Model. Sustainability (Switzerland), 14(2), 637. doi:10.3390/su14020637.

Jena, P. R., Managi, S., & Majhi, B. (2021). Forecasting the CO2 emissions at the global level: A multilayer artificial neural network modelling. Energies, 14(19), 1–23. doi:10.3390/en14196336.

IEA. (2024). Energy system Buildings: IEA 50. International Energy Agency (IEA), Paris, France. Available online: https://www.iea.org/energy-system/buildings (accessed on November 2024).

European Parliament. (2023). P9_TA(2023)0068 Energy performance of buildings (recast). European Parliament, Strasbourg, France, Vol. 0426, No. March 2023.

Piazolo, D. (2022). The Paris Climate Agreement as Benchmark for Buildings and Companies. IOP Conference Series: Earth and Environmental Science, 1078(1), 012115. doi:10.1088/1755-1315/1078/1/012115.

Muhamad, W. N. W., Zain, M. Y. M., Wahab, N., Aziz, N. H. A., & Kadir, R. A. (2010). Energy efficient lighting system design for building. ISMS 2010 - UKSim/AMSS 1st International Conference on Intelligent Systems, Modelling and Simulation, 282–286. doi:10.1109/ISMS.2010.59.

Yang, Y., Hu, G., & Spanos, C. J. (2022). Stochastic Optimal Control of HVAC System for Energy-Efficient Buildings. IEEE Transactions on Control Systems Technology, 30(1), 376–383. doi:10.1109/TCST.2021.3057630.

Rocha, P., Siddiqui, A., & Stadler, M. (2015). Improving energy efficiency via smart building energy management systems: A comparison with policy measures. Energy and Buildings, 88, 203–213. doi:10.1016/j.enbuild.2014.11.077.

Sekaranom, A. B., Nurjani, E., Harini, R., & Muttaqin, A. S. (2020). Simulation of daily rainfall data using articulated weather generator model for seasonal prediction of ENSO-affected zones in Indonesia. Indonesian Journal of Geography, 35(2), 143–153. doi:10.22146/ijg.50862.

Moradzadeh, A., Mansour-Saatloo, A., Mohammadi-Ivatloo, B., & Anvari-Moghaddam, A. (2020). Performance evaluation of two machine learning techniques in heating and cooling loads forecasting of residential buildings. Applied Sciences (Switzerland), 10(11), 3829. doi:10.3390/app10113829.

Kathiriya Siddhartha Nuthakki, S. (2023). AI and the Future of Medicine: Pioneering Drug Discovery with Language Models. International Journal of Science and Research (IJSR), 12(3), 1824-1829. doi:10.21275/sr24304173757.

Kim, D., Lee, Y., Chin, K., Mago, P. J., Cho, H., & Zhang, J. (2023). Implementation of a Long Short-Term Memory Transfer Learning (LSTM-TL)-Based Data-Driven Model for Building Energy Demand Forecasting. Sustainability (Switzerland), 15(3), 2340. doi:10.3390/su15032340.

Zeferina, V., Birch, C., Edwards, R., & Wood, R. (2019). Sensitivity analysis of peak and annual space cooling load at simplified office dynamic building model. E3S Web of Conferences, 111(201 9), 4–11. doi:10.1051/e3sconf/201911104038.

Shin, M., & Do, S. L. (2016). Prediction of cooling energy use in buildings using an enthalpy-based cooling degree days method in a hot and humid climate. Energy and Buildings, 110, 57–70. doi:10.1016/j.enbuild.2015.10.035.

Zhao, X., Yin, Y., Zhang, S., & Xu, G. (2023). Data-driven prediction of energy consumption of district cooling systems (DCS) based on the weather forecast data. Sustainable Cities and Society, 90, 104382. doi:10.1016/j.scs.2022.104382.

Dong, F., Yu, J., Quan, W., Xiang, Y., Li, X., & Sun, F. (2022). Short-term building cooling load prediction model based on DwdAdam-ILSTM algorithm: A case study of a commercial building. Energy and Buildings, 272, 112337. doi:10.1016/j.enbuild.2022.112337.

Nuthakki, S., Kumar, S., Kulkarni, C. S., & Nuthakki, Y. (2022). Role of AI Enabled Smart Meters to Enhance Customer Satisfaction. International Journal of Computer Science and Mobile Computing, 11(12), 99–107. doi:10.47760/ijcsmc.2022.v11i12.010.

Nuthakki, S., Kulkarni, C. S., Kathiriya, S., & Nuthakki, Y. (2024). Artificial Intelligence Applications in Natural Gas Industry: A Literature Review. International Journal of Engineering and Advanced Technology, 13(3), 64–70. doi:10.35940/ijeat.c4383.13030224.

Sadeghian Broujeny, R., Ben Ayed, S., & Matalah, M. (2023). Energy Consumption Forecasting in a University Office by Artificial Intelligence Techniques: An Analysis of the Exogenous Data Effect on the Modeling. Energies, 16(10), 4065. doi:10.3390/en16104065.

Lu, S., Cui, M., Gao, B., Liu, J., Ni, J., Liu, J., & Zhou, S. (2024). A Comparative Analysis of Machine Learning Algorithms in Predicting the Performance of a Combined Radiant Floor and Fan Coil Cooling System. Buildings, 14(6), 1659. doi:10.3390/buildings14061659.

Bedi, J., & Toshniwal, D. (2019). Deep learning framework to forecast electricity demand. Applied Energy, 238, 1312–1326. doi:10.1016/j.apenergy.2019.01.113.

Fan, C., Xiao, F., & Zhao, Y. (2017). A short-term building cooling load prediction method using deep learning algorithms. Applied Energy, 195, 222–233. doi:10.1016/j.apenergy.2017.03.064.

Li, A., Xiao, F., Zhang, C., & Fan, C. (2021). Attention-based interpretable neural network for building cooling load prediction. Applied Energy, 299. doi:10.1016/j.apenergy.2021.117238.

Lopes, M. N., & Lamberts, R. (2018). Development of a metamodel to predict cooling energy consumption of HVAC systems in office buildings in different climates. Sustainability (Switzerland), 10(12), 4718. doi:10.3390/su10124718.

Amasyali, K., & El-Gohary, N. (2021). Machine learning for occupant-behavior-sensitive cooling energy consumption prediction in office buildings. Renewable and Sustainable Energy Reviews, 142, 110714. doi:10.1016/j.rser.2021.110714.

Mui, K. W., Satheesan, M. K., & Wong, L. T. (2022). Building cooling energy consumption prediction with a hybrid simulation Approach: Generalization beyond the training range. Energy and Buildings, 276, 112502. doi:10.1016/j.enbuild.2022.112502.

Moon, J. W., Jung, S. K., Lee, Y. O., & Choi, S. (2015). Prediction performance of an artificial neural network model for the amount of cooling energy consumption in hotel rooms. Energies, 8(8), 8226–8243. doi:10.3390/en8088226.

Borowski, M., & Zwolińska, K. (2020). Prediction of cooling energy consumption in hotel building using machine learning techniques. Energies, 13(23), 6226. doi:10.3390/en13236226.

Lu, C., Li, S., Reddy Penaka, S., & Olofsson, T. (2023). Automated machine learning-based framework of heating and cooling load prediction for quick residential building design. Energy, 274(February), 127334. doi:10.1016/j.energy.2023.127334.

Liu, C. L., Tseng, C. J., Huang, T. H., Yang, J. S., & Huang, K. Bin. (2023). A multi-task learning model for building electrical load prediction. Energy and Buildings, 278, 112601. doi:10.1016/j.enbuild.2022.112601.

Zhang, C., Tian, X., Zhao, Y., & Lu, J. (2023). Automated machine learning-based building energy load prediction method. Journal of Building Engineering, 80(October), 108071. doi:10.1016/j.jobe.2023.108071.

Pavlatos, C., Makris, E., Fotis, G., Vita, V., & Mladenov, V. (2023). Utilization of Artificial Neural Networks for Precise Electrical Load Prediction. Technologies, 11(3), 1–14. doi:10.3390/technologies11030070.

Tsalikidis, N., Mystakidis, A., Tjortjis, C., Koukaras, P., & Ioannidis, D. (2024). Energy load forecasting: one-step ahead hybrid model utilizing ensembling. Computing, Springer, Vienna, Austria. doi:10.1007/s00607-023-01217-2.

Guo, G., Wang, H., Bell, D., Bi, Y., & Greer, K. (2003). KNN model-based approach in classification. On the Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE: OTM Confederated International Conferences, CoopIS, DOA, and ODBASE 2003, Catania, Sicily, Italy, November 3-7, 2003. doi:10.1007/978-3-540-39964-3_62.

Zhang, F., & O'Donnell, L. J. (2020). Support vector regression. Machine Learning: Methods and Applications to Brain Disorders, 123-140. doi:10.1016/B978-0-12-815739-8.00007-9.

Myles, A. J., Feudale, R. N., Liu, Y., Woody, N. A., & Brown, S. D. (2004). An introduction to decision tree modeling. Journal of Chemometrics, 18(6), 275–285. doi:10.1002/cem.873.

Su, X., Yan, X., & Tsai, C. L. (2012). Linear regression. Wiley Interdisciplinary Reviews: Computational Statistics, 4(3), 275–294. doi:10.1002/wics.1198.

Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13-17-August-2016, 785–794. doi:10.1145/2939672.2939785.

Jin, Z., Shang, J., Zhu, Q., Ling, C., Xie, W., & Qiang, B. (2020). RFRSF: Employee Turnover Prediction Based on Random Forests and Survival Analysis. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12343 LNCS, 503–515. doi:10.1007/978-3-030-62008-0_35.

Natekin, A., & Knoll, A. (2013). Gradient boosting machines, a tutorial. Frontiers in Neurorobotics, 7(Dec), 1-21. doi:10.3389/fnbot.2013.00021.

Solomatine, D. P., & Shrestha, D. L. (2004). AdaBoost.RT: A boosting algorithm for regression problems. IEEE International Conference on Neural Networks - Conference Proceedings, 2, 1163–1168. doi:10.1109/ijcnn.2004.1380102.

Gers, F. A., Schmidhuber, J., & Cummins, F. (2000). Learning to forget: Continual prediction with LSTM. Neural computation, 12(10), 2451-2471. doi:10.1162/089976600300015015.

Yao, K., Cohn, T., Vylomova, K., Duh, K., & Dyer, C. (2015). Depth-gated Recurrent Neural Networks. arXiv preprint arXiv:1508.03790. doi:10.48550/arXiv.1508.03790.

Alaraj, M., Kumar, A., Alsaidan, I., Rizwan, M., & Jamil, M. (2021). Energy Production Forecasting from Solar Photovoltaic Plants Based on Meteorological Parameters for Qassim Region, Saudi Arabia. IEEE Access, 9, 83241–83251. doi:10.1109/ACCESS.2021.3087345.

Dadhich, M., Pahwa, M. S., Jain, V., & Doshi, R. (2021). Predictive Models for Stock Market Index Using Stochastic Time Series ARIMA Modeling in Emerging Economy. Lecture Notes in Mechanical Engineering, 281–290. doi:10.1007/978-981-16-0942-8_26.

Shrivastava, S., Bal, P. K., Ashrit, R., Sharma, K., Lodh, A., & Mitra, A. K. (2017). Performance of NCUM global weather modeling system in predicting the extreme rainfall events over the central India during the Indian summer monsoon 2016. Modeling Earth Systems and Environment, 3(4), 1409–1419. doi:10.1007/s40808-017-0387-8.

Brahimi, T. (2019). Using artificial intelligence to predict wind speed for energy application in Saudi Arabia. Energies, 12(24), 4669. doi:10.3390/en12244669.

Fan, C., & Ding, Y. (2019). Cooling load prediction and optimal operation of HVAC systems using a multiple nonlinear regression model. Energy and Buildings, 197, 7–17. doi:10.1016/j.enbuild.2019.05.043.

He, N., Liu, L., Chu, D., & Qian, C. (2022). Air Conditioning Cooling Load Prediction Based on LSTM-ANN. 2022 5th International Symposium on Autonomous Systems, ISAS 2022, 1–6. doi:10.1109/ISAS55863.2022.9757345.

Bekdaş, G., Aydın, Y., Isıkdağ, íœ., Sadeghifam, A. N., Kim, S., & Geem, Z. W. (2023). Prediction of Cooling Load of Tropical Buildings with Machine Learning. Sustainability (Switzerland), 15(11), 9061. doi:10.3390/su15119061.

Myat, A., Kondath, N., Soh, Y. L., & Hui, A. (2024). A hybrid model based on multivariate fast iterative filtering and long short-term memory for ultra-short-term cooling load prediction. Energy and Buildings, 307, 113977. doi:10.1016/j.enbuild.2024.113977.

Jo, Y. Y., Cho, Y., Lee, S. Y., Kwon, J. Myoung, Kim, K. H., Jeon, K. H., Cho, S., Park, J., & Oh, B. H. (2021). Explainable artificial intelligence to detect atrial fibrillation using electrocardiogram. International Journal of Cardiology, 328, 104–110. doi:10.1016/j.ijcard.2020.11.053.

Acceptance Rate:	21%
Review Speed:	74 days
Issue Per Year:	6
Number of Volumes:	7
Number of Issues:	44
Number of Articles:	493
Number of Reviewers:	1187
Number of Contributors:	1394
Contributing Countries:	83
No. of WoS Citations:	2609
No. of Scopus Citations:	2936
No. of Google Citations:	4161
Google h-index:	29
Google i10-index:	126
Abstract Views:	681,807
PDF Download:	492,524

Leveraging Feature Sets and Machine Learning for Enhanced Energy Load Prediction: A Comparative Analysis

Authors

Downloads

Downloads

Login

submission

Publisher & Affiliated Societies

Indexing & Abstracting

SidebarMenu

IndexedBy

Indexing and Abstracting

twitter

Social Media

Analytics

Analytics

Information

Editorial Pick

Optical and Structural Characterization of Bi2FexNbO7 Nanoparticles for Environmental Applications

Impediments of Green Finance Adoption System: Linking Economy and Environment

Effect of Addition of Alccofine on the Compressive Strength of Cement Mortar Cubes

Digital Transformation: Opportunities and Challenges for Leaders in the Emerging Countries in Response to Covid-19 Pandemic

Thermal Regeneration and Reuse of Carbon and Glass Fibers from Waste Composites

Address

Contact Info:

Leveraging Feature Sets and Machine Learning for Enhanced Energy Load Prediction: A Comparative Analysis

Authors

Downloads

Downloads

Login

submission

Publisher & Affiliated Societies

Indexing & Abstracting

SidebarMenu

social

Journal Imprint

Journal Metrics

IndexedBy

Indexing and Abstracting

twitter

Social Media

Analytics

Analytics

Information

Editorial Pick

Optical and Structural Characterization of Bi2FexNbO7 Nanoparticles for Environmental Applications

Impediments of Green Finance Adoption System: Linking Economy and Environment

Effect of Addition of Alccofine on the Compressive Strength of Cement Mortar Cubes

Digital Transformation: Opportunities and Challenges for Leaders in the Emerging Countries in Response to Covid-19 Pandemic

Thermal Regeneration and Reuse of Carbon and Glass Fibers from Waste Composites