Sen Huang

A Bayesian network model for the optimization of a chiller plant’s condenser water set point

To implement the condenser water set point optimization, one can employ a regression model. However, existing regression-based methods have difficulties to handle non-linear chiller plant behaviour. To address this problem, we develop a Bayesian network model and compare it to both a linear and a polynomial regression model via a case study. The results show that the Bayesian network model can predict the optimal condenser water set points with a lower root mean square deviation for both a mild month and a summer month than the linear and the polynomial models. The energy-saving ratios by the Bayesian network model are 25.92% and 1.39% for the mild month and the summer month, respectively. As a comparison, the energy-saving ratios by the linear and the polynomial models are less than 19.00% for the mild month and even lead to more energy consumption in the summer month (up to 3.73%).

Genetic Algorithm for Building Optimization: State-of-the-Art Survey

Model-based building operation optimization can be used to reduce building energy consumption, so as to improve the indoor environment quality. Genetic Algorithm (GA) is one of the commonly used optimization algorithms for building applications. To provide readers up-to-date information, this paper attempts to summarize recent researches on building optimization with GA. Firstly, the principle of GA is introduced. Then, we summarize the literatures according to different categories, including applied system types and optimization objectives. We also provide some insights into the parameter setting and operator selection for GA. This review paper intends to give a better understanding and some future directions for building research community on how to apply GA for building energy optimization.