Statistics

Firefly protocol

Algorithm

Electrical engineering

Machine learning

Physics

Engineering

Sociology

Artificial intelligence

Firefly algorithm

Energy (signal processing)

Reliability engineering

Sensitivity (control systems)

Electronic engineering

Efficient energy use

Energy consumption

Consumption (sociology)

Mathematics

Social science

Computer science

Artificial neural network

Quantum mechanics

Particle swarm optimization

Biology

Zoology

Electromagnetism

Energy

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.

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