Building energy performance prediction using neural networks

The energy in buildings is influenced by numerous factors characterized by non-linear multi-interrelationships. Consequently, the prediction of the energy performance of a building, in the presence of these factors, becomes a complex task. The work presented in this paper utilizes risk and sensitivity analysis and applies artificial neural networks (ANNs) to predict the energy performance of buildings in terms of primary energy consumption and CO2 emissions represented in the Building Energy Rating (BER) scale. Training, validation, and testing of the utilized ANN was implemented using simulation data generated from a stochastic analysis on the 'Dwellings Energy Assessment Procedure' (DEAP) energy model. Four alternative ANN models for varying levels of detail and accuracy are devised for fast and efficient energy performance prediction. Two fine-detailed models, one with 68 energy-related input factors and one with 34 energy-related input factors, offer quick and multi-factored estimations of the energy performance of buildings with 80 and 85% accuracy, respectively. Two low-detailed models, one with 16 and one with 8 energy-related input factors, offer less computationally intensive yet sufficiently accurate predictions with 92 and 94% accuracy, respectively.