An artificial neural network (ANN) expert system enhanced with the electromagnetism-based firefly algorithm (EFA) for predicting the energy consumption in buildings. (1st January 2020)

• Record Type: Journal Article
• Authors: Bui, Dac-Khuong; Nguyen, Tuan Ngoc; Ngo, Tuan Duc; Nguyen-Xuan, H.
• Is Part Of: Energy. Volume 190(2020)
• Publication Date: 2020-01-01
• Subjects: Electromagnetism-based firefly algorithm -- Artificial neural network -- Machine learning -- Energy consumption; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2019.116370
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: In this study, a new hybrid model, namely the Electromagnetism-based Firefly Algorithm - Artificial Neural Network (EFA-ANN), is proposed to forecast the energy consumption in buildings. The model is applied to evaluate the heating load (HL) and cooling load (CL) using two given datasets. Each dataset was obtained by monitoring the effect of the façade system and dimensions of the building, respectively, on energy consumption. The performance of EFA-ANN is validated by comparing the obtained results with other methods. It is shown that EFA-ANN provides a faster and more accurate prediction of HL and CL. A sensitivity analysis is performed to identify the impact of each input on the energy performance of the building. From the results of this study, it is evident that EFA-ANN can assist civil engineers and construction managers in the early designs of energy-efficient buildings. Highlights: Early prediction of HL and CL in buildings can help to design energy-efficient buildings. A hybrid model is proposed to predict the HL and CL in buildings. The model is based on ANN model and EFA optimization method. Two different datasets were used to validate the model. The proposed approach can assist in the early stage of building design.