Investigation of a Radiant Building Heating System using Inverse Method

This article presents an inverse method aided by genetic algorithm (GA)-based inverse technique to predict necessary heating capacity along with acquired indoor relative humidity and temperatures when a radiant panel heating unit (RPHU) is provided for room heating. Due to absence of solar energy in winter, the RPHU was predominantly operated with biomass-based electricity and its waste thermal energy. Ambient temperature and relative humidity values under cold weather of Ropar, Punjab in India were taken from in-house experiments. From the acquired datasets, 144 samples were used for deriving suitable correlation between heating capacity and ambient conditions described by temperature and relative humidity. The GA-based inverse method is used to derive undetermined coefficients, and average capacity of the RPHU has been compared with the experimental data. Thereafter, for a single day, the GA was used to determine the response of indoor temperature and its relative humidity with time. Finally, the coefficients were used to forecast the indoor temperature for any day. It was found that the proposed inverse method could predict the average cooling capacity with above 99% accuracy, whereas the maximum errors in the predicted room temperature and relatively humidity were limited within 18.63 % and 5.72 %, respectively.