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

S. Ferlito, Mauro Atrigna, G. Graditi, S. De Vito, M. Salvato, A. Buonanno, G. Di Francia

Research output: Contribution to conferencePaper

16 Citations (Scopus)

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).
Original languageEnglish
DOIs
Publication statusPublished - 23 Mar 2015
Event18th Conference on Sensors and Microsystems, AISEM 2015 - Trento, Italy
Duration: 23 Mar 2015 → …

Conference

Conference18th Conference on Sensors and Microsystems, AISEM 2015
CountryItaly
CityTrento
Period23/3/15 → …

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All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Computer Science Applications

Cite this

Ferlito, S., Atrigna, M., Graditi, G., De Vito, S., Salvato, M., Buonanno, A., & Di Francia, G. (2015). Predictive models for building's energy consumption: An Artificial Neural Network (ANN) approach. Paper presented at 18th Conference on Sensors and Microsystems, AISEM 2015, Trento, Italy. https://doi.org/10.1109/AISEM.2015.7066836