Estimating synthetic load profile based on student behavior using fuzzy inference system for demand side management application

Abstract

This paper proposes a novel approach of estimating synthetic load profiles based on the electrical usage behavior using the fuzzy inference system (FIS) for demand side management (DSM). In practice, DSM is utilized to change the pattern of electrical energy consumed by end-users to modify the load profile by manipulating the price of the electricity. This study focuses on the energy consumption consumed by students who are paying electricity bills indirectly. Therefore, the effectiveness of conventional DSM methods on this user requires further investigation. In this study, the FIS estimates the synthetic load profile based on the student's behavior profile. Then, three DSM techniques: load clipping, load shifting, and load conservation, are applied to the electrical usage behavior model. The FIS estimates the synthetic load profile based on the modified electrical usage behavior model with these DSM techniques. From this estimation, the synthetic load profiles are analyzed and compared to evaluate the effectiveness of the DSM methods on the students. The result shows that the FIS estimates the synthetic load profile satisfactorily. Also, load conservation is the most effective technique in reducing the peak load profile and power consumption for this type of user. Conclusively, the result implies that the proposed methodology can be used to evaluate the effectiveness of the DSM method to reshape the load profile.