Abstract
Predicting a structure’s energy usage is an essential part of achieving energy efficiency objectives. Engineering, AI-based, and hybrid approaches can all be used to predict how much energy a building will require; however, we choose the AI-based method because it uses historical data to make predictions about future energy usage rather than thermodynamic equations the other approaches rely on. As a result, the objective of this study is to put several prediction models for energy usage into practice and assess them, the recommended algorithms are linear regression, random forest, and artificial neural network. Our study’s data set was gathered from a house that served as a case study, and we compared each approach’s efficacy using RMSE, R squared, MAE, and MAPE measurements.
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References
Le Corbusier, C.-E.J.: Vers une architecture (In french: Towards an architecture), collection de “l’espirit nouveau”, les ’EDITIONS g. Paris, France: CR‘ES ET C (1923)
Clements-Croome, T., Derek, J.: What do we mean by intelligent buildings? Autom. Constr. 6(5), 395–400 (1997)
Himanen, M.: The intelligence of intelligent buildings: the feasibility of the intelligent building concept in office buildings. VTT Technical Research Centre of Finland (2003)
Xie, X., Lu, Q., Herrera, M., Yu, Q., Parlikad, A.K., Schooling, J.M.: Does historical data still count? Exploring the applicability of smart building applications in the post-pandemic period. Sustain. Cities Soc. 69, 102804 (2021)
Wang, Z., Liu, J., Zhang, Y., Yuan, H., Zhang, R., Srinivasan, R.S.: Practical issues in implementing machine-learning models for building energy efficiency: moving beyond obstacles. Renew. Sustain. Energy Rev. 143, 110929 (2021)
Wong, J., Li, H., Lai, J.: Evaluating the system intelligence of the intelligent building systems: Part 1: development of key intelligent indicators and conceptual analytical framework. Autom. Constr. 17(3), 284–302 (2008)
Xu, D., et al.: A classified identification deep-belief network for predicting electric-power load. In: 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2), pp. 1–6. IEEE (2018)
Foucquier, A., Robert, S., Suard, F., St’ephan, L., Jay, A.: State of the art in building modelling and energy performances prediction: aD review. Renew. Sustain. Energy Rev. 23, 272–288 (2013)
Gassar, A.A.A., Yun, G.Y., Kim, S.: Data-driven approach to prediction of residential energy consumption at urban scales in London. Energy 187, 115973 (2019)
Massana, J., Pous, C., Burgas, L., Melendez, J., Colomer, J.: Short-term load forecasting for non-residential buildings contrasting artificial occupancy attributes. Energy Build. 130, 519–531 (2016)
Chae, Y.T., Horesh, R., Hwang, Y., Lee, Y.M.: Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings. Energy Build. 111, 184–194 (2016)
Jones, R.V., Fuertes, A., Lomas, K.J.: The socio-economic, dwelling and appliance related factors affecting electricity consumption in domestic buildings. Renew. Sustain. Energy Rev. 43, 901–917 (2015)
Arghira, N., Hawarah, L., Ploix, S., Jacomino, M.: Prediction of appliances energy use in smart homes. Energy 48(1), 128–134 (2012)
Candanedo, L.M., Feldheim, V., Deramaix, D.: Data driven prediction models of energy use of appliances in a low-energy house. Energy Build. 140, 81–97 (2017)
Wang, Z., Wang, Y., Zeng, R., Srinivasan, R.S., Ahrentzen, S.: Random Forest based hourly building energy prediction. Energy Build. 171, 11–25 (2018)
Segal, M.R.: Machine learning benchmarks and random forest regression (2004)
Ahmad, M.W., Mourshed, M., Rezgui, Y.: Trees vs Neurons: comparison between random forest and ANN for high-resolution prediction of building energy consumption. Energy Build. 147, 77–89 (2017)
Maulud, D., Abdulazeez, A.M.: A review on linear regression comprehensive in machine learning. J. Appl. Sci. Technol. Trends 1(4), 140–147 (2020)
Kenney, J.F.: Mathematics of statistics. D. Van Nostrand (1939)
Everitt, B.S., Skrondal, A.: The Cambridge dictionary of statistics (2010)
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The authors would like to thank the National Center for Scientific and Technical Research (CNRST) for supporting and funding this research.
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Khaoula, E., Amine, B., Mostafa, B. (2023). Evaluation and Comparison of Energy Consumption Prediction Models Case Study: Smart Home. In: Hassanien, A.E., et al. The 3rd International Conference on Artificial Intelligence and Computer Vision (AICV2023), March 5–7, 2023. AICV 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 164. Springer, Cham. https://doi.org/10.1007/978-3-031-27762-7_17
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DOI: https://doi.org/10.1007/978-3-031-27762-7_17
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