Data driven modeling for energy consumption prediction in smart buildings

2017 ◽  
Author(s):  
Aurora Gonzalez-Vidal ◽  
Alfonso P. Ramallo-Gonzalez ◽  
Fernando Terroso-Saenz ◽  
Antonio Skarmeta
Keyword(s):  
Energy Consumption ◽  
Data Driven ◽  
Smart Buildings ◽  
Energy Consumption Prediction ◽  
Data Driven Modeling ◽  
Consumption Prediction

scholarly journals Machine learning for energy consumption prediction and scheduling in smart buildings

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Safae Bourhnane ◽  
Mohamed Riduan Abid ◽  
Rachid Lghoul ◽  
Khalid Zine-Dine ◽  
Najib Elkamoun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Smart Buildings ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

scholarly journals A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1934 ◽  
Author(s):  
Federico Divina ◽  
Miguel García Torres ◽  
Francisco A. Goméz Vela ◽  
José Luis Vázquez Noguera
Keyword(s):  
Energy Consumption ◽  
Comparative Study ◽  
Electric Energy ◽  
Learning Approaches ◽  
University Campus ◽  
Smart Buildings ◽  
Electric Energy Consumption ◽  
Building Systems ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

Smart buildings are equipped with sensors that allow monitoring a range of building systems including heating and air conditioning, lighting and the general electric energy consumption. Thees data can then be stored and analyzed. The ability to use historical data regarding electric energy consumption could allow improving the energy efficiency of such buildings, as well as help to spot problems related to wasting of energy. This problem is even more important when considering that buildings are some of the largest consumers of energy. In this paper, we are interested in forecasting the energy consumption of smart buildings, and, to this aim, we propose a comparative study of different forecasting strategies that can be used to this aim. To do this, we used the data regarding the electric consumption registered by thirteen buildings located in a university campus in the south of Spain. The empirical comparison of the selected methods on the different data showed that some methods are more suitable than others for this kind of problem. In particular, we show that strategies based on Machine Learning approaches seem to be more suitable for this task.

Sign in / Sign up

Export Citation Format

Share Document