Genetic algorithm

The aim of the present study was to determine the applicability of a genetic algorithm for the optimization of daylighting systems, as well as the requirements for the lighting simulations to be used. Furthermore, by testing the daylighting performance of a building's facade when several parameters are allowed to change simultaneously, the results were used as a complement of previous parametric studies. The goal of the optimization was to maximize energy savings by reducing visual discomfort while maintaining good daylight penetration.

This paper presents a method for predicting occupants’ indoor thermal sensation in naturally-ventilated environments, based on real thermal sensation samples, using a GA-BP neural network model. This method improves the traditional back propagation neural network by incorporating an integrated genetic algorithm into the BP neutral network which aims to optimise the connection weight or threshold of the parameters in the input layer of the GA-BP neutral network model, which represent the factors affecting adaptive thermal comfort.

District cooling system (DCS) is a mass-scale production of chilled water generated at a central andremote chiller plant. Through an underground piping network, the chilled water is delivered to serve agroup of consumer buildings in a district area. DCS can offer both economical and environmentalbenefits. Because of the substantial capital investment and running energy involved, an optimal designof the distribution piping network is one of the crucial factors for successful implementation of thedistrict cooling scheme.

The optimization of building thermal performance has traditionally been based on designers’ experience. However, optimization algorithms such as Genetic Algorithms (GA) have lately been used extensively in order to find the optimization configuration of a