Abstract
The location where temperature and wind speed sensors are installed is usually restricted when air-conditioning a large multi-purpose indoor space. In some areas of the space, however, it is often impossible to obtain sensor readings that serve as representative values for control. Estimating the temperature and wind speed of any particular spot is therefore essential in terms of effective control. This paper presents a method for estimating these distributions based on fuzzy-logic models. The main advantage of this method is that both fuzzy rules and their membership functions are directly generated from physical I/O data, thereby building theses models. First, a series of steps of the method is shown, and then a two-variable nonlinear function is identified as an example and its fundamental characteristics are analyzed. Next, modeling is performed using temperature and wind-speed distribution data on large-space air-conditioning based on many input variables and a high degree of nonlinearity. Through this approach, this paper will demonstrate that the models thus built are effective in estimating such distributions.
