Prediction of electrical power consumption in the household: fresh evidence from machine learning approach
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Energy
Link
https://link.springer.com/content/pdf/10.1007/s12053-023-10155-z.pdf
Reference28 articles.
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3. Fan, H., MacGill, I. F., & Sproul, A. B. (2017). Statistical analysis of drivers of residential peak electricity demand. Energy and Buildings, 141, 205–217. https://doi.org/10.1016/j.enbuild.2017.02.030
4. Hernandez, L., Baladron, C., Aguiar, J. M., Carro, B., Sanchez-Esguevillas, A. J., Lloret, J., & Massana, J. (2014). A survey on electric power demand forecasting: Future trends in smart grids, microgrids and smart buildings. IEEE Communications Surveys and Tutorials, 16(3), 1460–1495. https://doi.org/10.1109/SURV.2014.032014.00094
5. Huebner, G., Shipworth, D., Hamilton, I., Chalabi, Z., & Oreszczyn, T. (2016). Understanding electricity consumption: A comparative contribution of building factors, socio-demographics, appliances, behaviours and attitudes. Applied Energy, 177, 692–702. https://doi.org/10.1016/j.apenergy.2016.04.075
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