Abstract
AbstractThe international community has largely recognized that the Earth’s climate is changing. Mitigating its global effects requires international actions. The European Union (EU) is leading several initiatives focused on reducing the problems. Specifically, the Climate Action tries to both decrease EU greenhouse gas emissions and improve energy efficiency by reducing the amount of primary energy consumed, and it has pointed to the development of efficient building energy management systems as key. In traditional buildings, households are responsible for continuously monitoring and controlling the installed Heating, Ventilation, and Air Conditioning (HVAC) system. Unnecessary energy consumption might occur due to, for example, forgetting devices turned on, which overwhelms users due to the need to tune the devices manually. Nowadays, smart buildings are automating this process by automatically tuning HVAC systems according to user preferences in order to improve user satisfaction and optimize energy consumption. Towards achieving this goal, in this paper, we compare 36 Machine Learning algorithms that could be used to forecast indoor temperature in a smart building. More specifically, we run experiments using real data to compare their accuracy in terms of R-coefficient and Root Mean Squared Error and their performance in terms of Friedman rank. The results reveal that the ExtraTrees regressor has obtained the highest average accuracy (0.97%) and performance (0,058%) over all horizons.
Publisher
Springer Science and Business Media LLC
Subject
General Energy,Economics and Econometrics,Modelling and Simulation
Reference73 articles.
1. Alawadi, S., Delgado, M.F., Pérez, D.M.: Machine learning algorithms for pattern visualization in classification tasks and for automatic indoor temperature prediction. Ph.D. thesis, Universidade de Santiago de Compostela (2018)
2. Alawadi, S., Mera, D., Fernández-Delgado, M., Taboada, J.A.: Comparative study of artificial neural network models for forecasting the indoor temperature in smart buildings. In: 2017 2nd International Conference on Smart Cities, pp. 29–38. Springer (2017)
3. Aliberti, A., Bottaccioli, L., Macii, E., Di Cataldo, S., Acquaviva, A., Patti, E.: A non-linear autoregressive model for indoor air-temperature predictions in smart buildings. Electronics 8(9), 979 (2019)
4. Alzubi, J., Nayyar, A., Kumar, A.: Machine learning from theory to algorithms: an overview. In: 2018 2nd National Conference on Computational Intelligence (NCCI): Journal of Physics, vol. 1142, p. 012012. IOP Publishing (2018)
5. Alzubi, J.A.: Diversity based improved bagging algorithm. In: 2015 1st Proceedings of The International Conference on Engineering & MIS (ICEMIS) 2015, pp. 35–40. ACM (2015)
Cited by
58 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献