Analysis of Optimizers to Regulate Occupant's Actions for Building Energy Management

Occupants and their actions play major roles in building energy management as reported by previous studies, which involves finding the optimal schedule of user actions, under a given physical context, in order to minimize their dissatisfaction. However, comparison and performance analysis of various optimizers, for the concerned problem, have not been studied previously, which is essential to gain insight into the underlying characteristics of the problem. In this work, the performance of four popular and contemporary multi-objective optimization algorithms viz. DEMO, NSGA-II, NSGA-III, and Θ-DEA, for estimating the optimal schedule has been analyzed in terms of their abilities to find minimal average indoor conditions' to discover more number of alternative trade-off solutions (flexibility) and to promptly converge to a smaller minimal net dissatisfaction value (speed of convergence). Results show that NSGA-II has slightly better capabilities than NSGA-III and Θ-DEA, but it clearly outperforms DEMO. The recently developed population dynamics indicators are also applied to support the observed features of the optimizers. The proposed analyzing paradigm can also be used when the optimization problem is extended to include several other objectives.