Publicado en línea: 01 jun 2015
Páginas: 869 - 895
Recibido: 15 ene 2015
Aceptado: 27 mar 2015
DOI: https://doi.org/10.21307/ijssis-2017-787
Palabras clave
© 2015 Yi Zhao et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
In this paper, we proposed to use the Efficient Indoor Thermal Time Constant (EITTC) to characterize the indoor thermal response in old buildings. Accordingly, a low cost, energy-efficient, wide-applicable indoor thermal modeling solution is developed by combining Wireless Sensor Network (WSN) and Artificial Neural Network (ANN). Experiments on both prototype and building room showed consistent results that the combination of WSN and ANN can provide accurate indoor thermal models. A linear approximation of these models makes it possible to estimate the EITTC of building room. Statistical computations confirmed these estimations by showing a strong correlation between the model’s predicted EITTC and measured data. Thus the indoor thermal response under different indoor/outdoor conditions can be characterized. Finally, a model based adaptive heating Start/Shut control method is proposed and tested, with which, direct energy saving is achieved.