Congress 1109
  • Sadi Alawadi, David Mera, Manuel Fernández-Delgado, and José A. Taboada
  • International Conference on Smart Cities. Malaga, España. 2017

Comparative study of artificial neural network models for forecasting the indoor temperature in smart buildings

The implementation of efficient building energy management plans is key to the road-map of the European Union for reducing the effects of the climate change. Firstly, accurate models of the currently energy systems need to be developed. In particular, simulations of Heating, Ventilation and Air Conditioning (HVAC) systems are essential since they have a relevant impact in both energy consumption and building comfort. This paper presents a comparative of four different machine learning approaches, based on Artificial Neural Networks (ANN), for modeling an HVAC system. The developed models have been tuned to forecast three consecutive hours of the indoor temperature of a public research building. Tests revealed that an on-line learning ANN, which is also fully trained weekly, is less affected by sensor noise and anomalies than the remaining approaches. Moreover, it can be also automatically adapted to deal with specific environmental conditions.
Keywords: Smart buildings, time series prediction, energy efficiency, Neural network
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