Eiji Mimura

Bayesian networks for thermal comfort analysis

To analyze the causal relationships between thermal comfort and the related factors, this paper presents a model of Bayesian networks and its experimental simulation. The samples used in the experimentation consisted of 1,584 (22 participants under 9 conditions tested 8 times) pieces of questionnaire data which included ratings by the 22 participants of their thermal sensation votes in five bodily regions (face, chest, hands, buttocks, and soles of the feet). The proposed Bayesian network consists of four layers: the external environment, thermal sensation change, thermal sensation votes and comfortability. Assigning evidence to the thermal comfort for the Bayesian network, the experimental simulation shows that the thermal sensations of the face play an important role in a persons overall comfortability. The sensitivity of the model is also discussed.

Human Adaptive Control Strategy for Multiple Air Conditioners

This paper concerns a control strategy for multiple systems that affect the condition of the air in a room such as electric heaters and floor heating systems. Based on a resident lifestyle and case-based approach, the control system with the proposed strategy optimizes the weighted summation of three evaluation indices: cost, the time needed to reach a comfortable state and the rate of the comfortable state where the comfortable state is defined by well-known PMV (predicted mean vote). This paper discusses how the three indices are evaluated and describes the roles of various components in the proposed system. This paper also presents the user interface for expressing a users lifestyle and it touches on future issues.

Causal Analysis for Thermal Comfort Votes

To analyze the causal relationship between comfort environment vote and thermal sensation votes (of face, chest, hands, buttocks, and soles of the foot), this paper presents how three methods work in experimentation: multiple regression analysis, conjoint analysis and structural equation modeling (SEM). The samples for the experimentation consisted of 1,584 (22 subjects x 9 conditions x 8 times) questionnaire data. Our main interest is whether there are any differences in the causal relationship among subjects, conditions or methods. Paying attention to preprocessing, we found the following: (1) the importance of each thermal sensation vote differed not only according to subject but also according to the analysis method; (2) SEM is the most suitable method to explain the causal relationship; and (3) the lower body is more important than the upper body in controlling thermal comfort.

DEA-based data mining for energy consumption

This paper discusses the potential of DEA (data envelopment analysis) for performance evaluation on energy consumption. The purpose is to develop evaluation methodology for finding features of energy provision and consumption from observed data. While there are four evaluation measures (economy, environment, comfort, and risk) in general, this paper focuses on economical and environmental aspects, and selects energy consumption, CO2 discharge and cost as inputs for the first step model. In order to consider the evaluation methodology based on four plants data, this paper has focused on hot-water supply as output. Then the numerical study illustrates the findings from data on energy supply patterns, seasonal effects, and so on