Dynamics of predicting temperature-humidity index and power consumption in small-scale system utilizing Bayesian inference via the EAP estimation

By making use of environmental quantities, such as temperature and relative humidity, we search optimal conditions on thermal index called as the temperature-humidity index (THI) at each sampling point and power consumption due to air conditioning, both of which are estimated by repeating the Bayesian inference using the EAP estimation with an increase in the ratio of the coefficient of the model prior as to that of the likelihood. Then, we estimate static property of the Bayesian inference and dynamic property of the iterative method by making use of numerical calculations for several cases. Numerical results show that the iterative method succeeds in searching the optimal conditions on the environmental variables, if we increase the ratio up to its optimum respective of the choice of observed variables. These results are confirmed by the mean-field theory for the full-connected model.