Physical-Rules-Based Adaptive Neuro-Fuzzy Inferential Sensor Model for Predicting the Indoor Temperature in Heating Systems
Previous research demonstrated that inferential sensors-based control technology can significantly improve the energy efficiency of space heating systems. However, the performance strongly relies on the accuracy and robustness of the dynamic model upon which the inferential model is built. Traditional methods, such as simplified physical model, adaptive neurofuzzy inferential sensor- (ANFIS-) based model, were developed and tested in this research. In attempt to improve both the accuracy and robustness of inferential model, this study aims to investigate how to improve the performance of inferential sensors using physical-rules-based ANFIS in prediction of the hydraulic system temperature in order to adapt the good power needed in the dwellings. This paper presents the structure of this innovative method. The performance is tested using experimental data and is compared with that of previous methods using three performance measures: RMSE, RMS, and R2. The results show that the physical-rule-based ANFIS inferential model is more accurate and robust. The impact of this improvement on the overall control performance is also discussed.
