Publisher
Springer Nature Switzerland
Reference14 articles.
1. Corgnati, S., Bednar, T., Jang, Y., Yoshino, H., Ghiaus, C.: Total Energy Use in Buildings. Analysis and Evaluation Methods, Final Report Annex 53. Statistical Analysis and Prediction Methods (2013)
2. Fu, Y., Li, Z., Zhang, H., Xu, P.: Using support vector machine to predict next day electricity load of public buildings with sub-metering device. Procedia Eng. 12, 1016–1022 (2015)
3. Valgaev, O., Kupzog, F., Schmeck, H.: Low-voltage power demand forecasting using K-nearest neighbors approach. In: IEEE Innovative Smart Grid Technologies - Asia (ISGT - Asia), pp. 1019–1024 (2016)
4. El Khantach, A., Hamlich, M., Belbounaguia, N.E.: Short-term load forecasting using machine learning and periodicity decomposition. AIMS Energy 7(3), 382–394 (2019)
5. González-Briones, A., Hernández, G., Corchado, J.M., Omatu, S., Mohamad, M.S.: Machine learning models for electricity consumption forecasting: a review. In: IEEE Virtual-Ledgers-Tecnologías DLT/Blockchain y Cripto-IOT (2019)