id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-283907-ev1ghlwl	Cao, Lingyan	Electrical load prediction of healthcare buildings through single and ensemble learning	2020-11-30		.txt	text/plain	8756	418	44	Therefore, in this paper, the authors propose a one day-ahead electrical load forecasting model based on single and ensemble machine learning algorithms. In the present study, electrical load forecasting models of healthcare buildings are developed based on single and ensemble machine learning algorithms by taking account multi-factors simultaneously. To address this gap, this study takes into account the occupancy of outpatients, emergency patients, and inpatients and employs single and ensemble machine learning algorithms to predict the electric load demand of healthcare buildings. It can be seen that the electric load prediction for the healthcare buildings includes three steps: (1) Identify the relevant features and gather data, (2) Train single and ensemble learning models with prepared dataset, and (3) Compare the prediction performance of different models. Electrical load forecasting is naturally considered to be a regression problem in machine learning, aiming to accurately predict the energy demand of buildings based on its relationship with a given set of independent input variables.	./cache/cord-283907-ev1ghlwl.txt	./txt/cord-283907-ev1ghlwl.txt