Development of an Improved Model to Predict Building Thermal Energy Consumption by Utilizing Feature Selection

Autor: Seung Bok Leigh, Jeehang Lee, Gahee Kim, Kyungyong Park, Jihoon Jang, Eunjo Son, Joosang Lee
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Control and Optimization
Mean squared error
020209 energy
Energy Engineering and Power Technology
Feature selection
02 engineering and technology
010501 environmental sciences
01 natural sciences
lcsh:Technology
feature selection
building energy conservation
Statistics
0202 electrical engineering
electronic engineering
information engineering
building operation
Electrical and Electronic Engineering
Engineering (miscellaneous)
0105 earth and related environmental sciences
Artificial neural network
Renewable Energy
Sustainability and the Environment
business.industry
lcsh:T
building energy consumption
Energy consumption
Random forest
thermal energy
Variable (computer science)
artificial neural network
Environmental science
business
Energy (signal processing)
Thermal energy
Energy (miscellaneous)
Zdroj: Energies, Vol 12, Iss 21, p 4187 (2019)
Energies; Volume 12; Issue 21; Pages: 4187
ISSN: 1996-1073
Popis: 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.
Databáze: OpenAIRE
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