With the development of urbanization, the smart city is gradually receiving widespread attention. The construction industry accounts for nearly half of China's total carbon emissions, which is one of the main contributors to the greenhouse effect. Accurate prediction of building energy consumption is of vital significance for energy conservation and smart city development. However, the traditional statistical methods for building energy consumption prediction have some problems such as low fitting accuracy and inaccurate prediction results. Machine learning algorithms are developing rapidly, which have significantly improved the predictive accuracy of building energy consumption. In this study, a stacking-based ensemble learning model based on architectural features is proposed. The building energy consumption mainly consists of the heating load and cooling load, which are used as target variables to predict the building energy consumption. Firstly, the normalized preprocessing is performed on the raw data. Subsequently, the ensemble model is obtained by integrating the optimal base predictors using stacking-based ensemble learning method. In the experiments, the dataset from the building energy area is tested with three metrics to evaluate the performance of the proposed model in building energy consumption prediction. The experimental results show that the proposed ensemble model outperforms the base predictors in solving the building energy consumption prediction problem.
| Published in | American Journal of Energy Engineering (Volume 11, Issue 2) |
| DOI | 10.11648/j.ajee.20231102.14 |
| Page(s) | 61-66 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2023. Published by Science Publishing Group |
Machine Learning, Prediction, Stacking, Ensemble Model, Building Energy Consumption
| [1] | Silva, B. N., Khan, M., & Han, K. (2018). Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities. Sustainable cities and society, 38, 697-713. |
| [2] | Visvizi, A., & Lytras, M. D. (2018). Rescaling and refocusing smart cities research: From mega cities to smart villages. Journal of Science and Technology Policy Management, 9 (2), 134-145. |
| [3] | Chui, K. T., Lytras, M. D., & Visvizi, A. (2018). Energy sustainability in smart cities: Artificial intelligence, smart monitoring, and optimization of energy consumption. Energies, 11 (11), 2869. |
| [4] | Kim, T., Kang, B., Kim, H., Park, C., & Hong, W. H. (2019). The study on the Energy Consumption of middle school facilities in Daegu, Korea. Energy Reports, 5, 993-1000. |
| [5] | Xu, Z., Hu, G., & Spanos, C. J. (2017). Coordinated optimization of multiple buildings with a fair price mechanism for energy exchange. Energy and Buildings, 151, 132-145. |
| [6] | Enayet, A., Razzaque, M. A., Hassan, M. M., Alamri, A., & Fortino, G. (2018). A mobility-aware optimal resource allocation architecture for big data task execution on mobile cloud in smart cities. IEEE Communications Magazine, 56 (2), 110-117. |
| [7] | Jing W., Guan H., Luo W., Li C., & Liu X. (2022). Energy consumption prediction model of office building based on improved GWO-BP. Building Energy Efficiency, 50 (8), 125-129. |
| [8] | Montazeri, M., Montazeri, M., Montazeri, M., & Beigzadeh, A. (2016). Machine learning models in breast cancer survival prediction. Technology and Health Care, 24 (1), 31-42. |
| [9] | Pai, P. F., & Wang, W. C. (2020). Using machine learning models and actual transaction data for predicting real estate prices. Applied Sciences, 10 (17), 5832. |
| [10] | Phan, H. C., & Dhar, A. S. (2021). Predicting pipeline burst pressures with machine learning models. International Journal of Pressure Vessels and Piping, 191, 104384. |
| [11] | Kwon, H., Oh, K. C., Choi, Y., Chung, Y. G., & Kim, J. (2021). Development and application of machine learning-based prediction model for distillation column. International Journal of Intelligent Systems, 36 (5), 1970-1997. |
| [12] | Yan, L., & Liu, Y. (2020). An ensemble prediction model for potential student recommendation using machine learning. Symmetry, 12 (5), 728. |
| [13] | Chen, Y., Li, X., & Sun, W. (2020). Research on stock selection strategy based on AdaBoost algorithm. In Proceedings of the 4th International Conference on Computer Science and Application Engineering, Sanya, China, October 20-22, 2020, pp. 1-5. |
| [14] | Park, B., & Bae, J. K. (2015). Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data. Expert systems with applications, 42 (6), 2928-2934. |
| [15] | Al-Saadi, S. N., & Zhai, Z. J. (2015). A new validated TRNSYS module for simulating latent heat storage walls. Energy and Buildings, 109, 274-290. |
| [16] | Peng, C. (2016). Calculation of a building's life cycle carbon emissions based on Ecotect and building information modeling. Journal of Cleaner Production, 112, 453-465. |
| [17] | Yu, S., Cui, Y., Xu, X., & Feng, G. (2015). Impact of civil envelope on energy consumption based on EnergyPlus. Procedia Engineering, 121, 1528-1534. |
| [18] | Fumo, N., & Biswas, M. R. (2015). Regression analysis for prediction of residential energyconsumption. Renewable and sustainable energy reviews, 47, 332-343. |
| [19] | Bourdeau, M., Zhai, X. Q., Nefzaoui, E., Guo, X., & Chatellier, P. (2019). Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society, 48, 101533. |
APA Style
Chang Lu, Yining Jing, Xinxue Lin, Kun Li. (2023). Predicting Building Energy Consumption Using Stacking-Based Ensemble Model. American Journal of Energy Engineering, 11(2), 61-66. https://doi.org/10.11648/j.ajee.20231102.14
ACS Style
Chang Lu; Yining Jing; Xinxue Lin; Kun Li. Predicting Building Energy Consumption Using Stacking-Based Ensemble Model. Am. J. Energy Eng. 2023, 11(2), 61-66. doi: 10.11648/j.ajee.20231102.14
AMA Style
Chang Lu, Yining Jing, Xinxue Lin, Kun Li. Predicting Building Energy Consumption Using Stacking-Based Ensemble Model. Am J Energy Eng. 2023;11(2):61-66. doi: 10.11648/j.ajee.20231102.14
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Download@article{10.11648/j.ajee.20231102.14,
author = {Chang Lu and Yining Jing and Xinxue Lin and Kun Li},
title = {Predicting Building Energy Consumption Using Stacking-Based Ensemble Model},
journal = {American Journal of Energy Engineering},
volume = {11},
number = {2},
pages = {61-66},
doi = {10.11648/j.ajee.20231102.14},
url = {https://doi.org/10.11648/j.ajee.20231102.14},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajee.20231102.14},
abstract = {With the development of urbanization, the smart city is gradually receiving widespread attention. The construction industry accounts for nearly half of China's total carbon emissions, which is one of the main contributors to the greenhouse effect. Accurate prediction of building energy consumption is of vital significance for energy conservation and smart city development. However, the traditional statistical methods for building energy consumption prediction have some problems such as low fitting accuracy and inaccurate prediction results. Machine learning algorithms are developing rapidly, which have significantly improved the predictive accuracy of building energy consumption. In this study, a stacking-based ensemble learning model based on architectural features is proposed. The building energy consumption mainly consists of the heating load and cooling load, which are used as target variables to predict the building energy consumption. Firstly, the normalized preprocessing is performed on the raw data. Subsequently, the ensemble model is obtained by integrating the optimal base predictors using stacking-based ensemble learning method. In the experiments, the dataset from the building energy area is tested with three metrics to evaluate the performance of the proposed model in building energy consumption prediction. The experimental results show that the proposed ensemble model outperforms the base predictors in solving the building energy consumption prediction problem.},
year = {2023}
}
TY - JOUR T1 - Predicting Building Energy Consumption Using Stacking-Based Ensemble Model AU - Chang Lu AU - Yining Jing AU - Xinxue Lin AU - Kun Li Y1 - 2023/05/28 PY - 2023 N1 - https://doi.org/10.11648/j.ajee.20231102.14 DO - 10.11648/j.ajee.20231102.14 T2 - American Journal of Energy Engineering JF - American Journal of Energy Engineering JO - American Journal of Energy Engineering SP - 61 EP - 66 PB - Science Publishing Group SN - 2329-163X UR - https://doi.org/10.11648/j.ajee.20231102.14 AB - With the development of urbanization, the smart city is gradually receiving widespread attention. The construction industry accounts for nearly half of China's total carbon emissions, which is one of the main contributors to the greenhouse effect. Accurate prediction of building energy consumption is of vital significance for energy conservation and smart city development. However, the traditional statistical methods for building energy consumption prediction have some problems such as low fitting accuracy and inaccurate prediction results. Machine learning algorithms are developing rapidly, which have significantly improved the predictive accuracy of building energy consumption. In this study, a stacking-based ensemble learning model based on architectural features is proposed. The building energy consumption mainly consists of the heating load and cooling load, which are used as target variables to predict the building energy consumption. Firstly, the normalized preprocessing is performed on the raw data. Subsequently, the ensemble model is obtained by integrating the optimal base predictors using stacking-based ensemble learning method. In the experiments, the dataset from the building energy area is tested with three metrics to evaluate the performance of the proposed model in building energy consumption prediction. The experimental results show that the proposed ensemble model outperforms the base predictors in solving the building energy consumption prediction problem. VL - 11 IS - 2 ER -
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