Predictive models for building's energy consumption: An Artificial Neural Network (ANN) approach

Abstract

Building's energy demand is influenced by many factors, such as: weather conditions, building structure and characteristics, energy consumption of components (lighting and HVAC systems), level of occupancy and user's behavior. As consequence of multi-variable impact on building's energy consumption, theoretical models based on first principles are not able to forecast actual energy demand of a generic building. In this paper, an Artificial Neural Network (ANN) model applied to a real case consisting in a dataset of monthly historical building electric energy consumption is developed. Results show that accuracy of energy consumption forecast runs, in terms of RMSPE (root mean square percentage error), in the range 15.7% to 17.97% and varies slightly according to the prediction horizon (3 months, 6 months and 12 months).