Bayesian inference using the expected a posterior estimation for predicting comfort environment and effective usage of power based on thermal index via the temperature-humidity index

By making use of Bayesian inference using the expected a posterior (EAP) estimation, we construct an information technique for providing temperature and relative humidity so as to realize effective usage of electric power under comfortable environment based on the temperature-humidity index (THI) at a small-scale target system. In this method, we estimate the temperature and the relative humidity as expectations which are averaged over the posterior probability composed of the model of the true prior generating a set of ideal environments at the target room and the likelihood rewriting each original ideal environment to a realistic one observed at the target room. Numerical calculations find that we succeed in providing the temperature and the relative humidity both of which lead to comfortable environment and effective usage of power due to air conditioning at the target room, if we tune parameters appropriately. Also, we find that upper bound of the overlap is realized, if we use the assumed true prior and the transition probability from the original to observed states.