Title :
Predictive models for building´s energy consumption: An Artificial Neural Network (ANN) approach
Author :
Ferlito, S. ; Atrigna, Mauro ; Graditi, G. ; De Vito, S. ; Salvato, M. ; Buonanno, A. ; Di Francia, G.
Author_Institution :
I´Energia e lo Sviluppo Economico Sostenibile, ENEA - Agenzia per le Nuove Tecnol., Portici, Italy
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).
Keywords :
building management systems; energy consumption; least mean squares methods; load forecasting; neural nets; power engineering computing; ANN; RMSPE; artificial neural network; building energy demand; energy demand forecasting; generic building; monthly historical building electric energy consumption; predictive models; root mean square percentage error; Accuracy; Artificial neural networks; Buildings; Delays; Energy consumption; Forecasting; Predictive models; Artificial Neural Networks; Building´s Energy consumption; NAR; Prediction;
Conference_Titel :
AISEM Annual Conference, 2015 XVIII
Conference_Location :
Trento
DOI :
10.1109/AISEM.2015.7066836
