Abstract
Humans spend approximately 90% of the daytime in buildings, and greenhouse gases (GHGs) emitted by buildings account for approximately 20% of total GHG emissions. As the energy consumed during building operation from a building life-cycle perspective amounts to approximately 70–90% of the total energy, it is essential to accurately predict the energy consumption of buildings for their efficient operation. This study aims to optimize a model for predicting the thermal energy consumption of buildings by (i) first extracting major variables through feature selection and deriving significant variables in addition to the collected data and (ii) predicting the thermal energy consumption using a machine learning model. Feature selection using random forest was performed, and 11 out of 17 available data were selected. The accuracy of the prediction model was significantly improved when the hour of day variable was added. The prediction model was constructed using an artificial neural network (ANN), and the improvement in the prediction accuracy was analyzed by comparing different cases of variable combinations. The ANN prediction accuracy was improved by 15% using the feature selection process compared to when all data were used as input data, and 25% coefficient of variation of the root mean square error (CVRMSE) accuracy was achieved.
Highlights
greenhouse gases (GHGs) emissions by 37% compared to Business As Usual (BAU) by 2030 in the Paris Agreement of the Intergovernmental Panel on Climate Change (IPCC) in 2016 [2]
The results show that Case 2, which case with evaluation criterion based on the ASHRAE guideline 14
An prediction model for the thermal energy consumption of a building can be constructed by extracting excellent prediction model for the thermal energy consumption of a building can be constructed by major variables through feature selection and adding significant variables to the input data used for extracting major variables through feature selection and adding significant variables to the input data the model rather than by using all raw data as input data
Summary
The emission of greenhouse gas (GHG) has steeply increased by approximately 82.5% since. 1979 [1], which has been a major contributor to climate change globally. This gives rise to considerable global efforts to reduce its emissions. GHG emissions by 37% compared to Business As Usual (BAU) by 2030 in the Paris Agreement of the Intergovernmental Panel on Climate Change (IPCC) in 2016 [2]. Humans, who are mostly responsible for GHG emissions, spend approximately 90% of the daytime in buildings [3]. Many studies have been conducted to reduce energy consumption in buildings in an effort to reduce GHG emissions from buildings
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