Economic Efficiency of Using Smart Home Technology in Individual Residential Buildings
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-36960-5_86
Reference19 articles.
1. Machorro-Cano, I., et al.: A big data and machine learning-based smart home system for energy. Sav. Energ. 13(5), 1097 (2020). https://doi.org/10.3390/en13051097
2. Mrówczyńska, M., Skiba, M., Bazan-Krzywoszańska, A., Sztubecka, M.: Household standards and socio-economic aspects as a factor determining energy consumption in the city. Appl. Energy 264, 114680 (2020). https://doi.org/10.1016/j.apenergy.2020.114680
3. Mancini, F., Basso, G.L.: How climate change affects the building energy consumptions due to cooling, heating, and electricity demands of Italian residential sector. Energies 13(2), 410 (2020). https://doi.org/10.3390/en13020410
4. Herie Park Human: Comfort-based-home energy management for demand response participation. Energies 13(10), 2463 (2020). https://doi.org/10.3390/en13102463
5. Fedorczak-Cisak, M., Kotowicz, A., Radziszewska-Zielina, E., Sroka, B., Tatara, T., Barnaś, K.: Multi-criteria optimisation of an experimental complex of single-family nearly zero-energy buildings. Energies 13(7), 1541 (2020). https://doi.org/10.3390/en13071541
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