Estimating building energy consumption using extreme learning machine method. (15th February 2016)

• Record Type: Journal Article
• Authors: Naji, Sareh; Keivani, Afram; Shamshirband, Shahaboddin; Alengaram, U. Johnson; Jumaat, Mohd Zamin; Mansor, Zulkefli; Lee, Malrey
• Is Part Of: Energy. Volume 97(2016)
• Page Start: 506
• Page End: 516
• Publication Date: 2016-02-15
• Subjects: Energy consumption -- Residential buildings -- Estimation -- Energy efficiency -- ELM (extreme learning machine); Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2015.11.037
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: The current energy requirements of buildings comprise a large percentage of the total energy consumed around the world. The demand of energy, as well as the construction materials used in buildings, are becoming increasingly problematic for the earth's sustainable future, and thus have led to alarming concern. The energy efficiency of buildings can be improved, and in order to do so, their operational energy usage should be estimated early in the design phase, so that buildings are as sustainable as possible. An early energy estimate can greatly help architects and engineers create sustainable structures. This study proposes a novel method to estimate building energy consumption based on the ELM (Extreme Learning Machine) method. This method is applied to building material thicknesses and their thermal insulation capability (K-value). For this purpose up to 180 simulations are carried out for different material thicknesses and insulation properties, using the EnergyPlus software application. The estimation and prediction obtained by the ELM model are compared with GP (genetic programming) and ANNs (artificial neural network) models for accuracy. The simulation results indicate that an improvement in predictive accuracy is achievable with the ELM approach in comparison with GP and ANN. Highlights: Buildings consume huge amounts of energy for operation. Envelope materials and insulation influence building energy consumption. Extreme learning machine is used to estimate energy usage of a sample building. The key effective factors in this study are insulation thickness and K-value.