Exploration of an Efficient Control Rule Set of a Heat Pump Water Heater by Genetic Algorithms
The system most widely employed in existing heat pump water heaters is based on three rules, the parameters of which used to calculate the amount of hot water required are fixed. A method exists to optimize parameters in the three rules; however, this parameter optimization method remains insufficient for many types of heaters since the method does not take demand patterns fully into account. Parameter optimization optimizes a control rule set simply assuming that a storage tank has sufficient capacity for a whole day demand. We propose a new method for generating an control rule set that minimizes running costs based on hot water demand patterns. Our method adopts a new rule format which can control the heaters more flexibly than parameter optimization. The control rule set is explored by Genetic Algorithm (GA) and a computer simulation of the heat pump water heater. To evaluate our method, we explored control rule sets for two types of storage tanks for three actual consumers. Our method generated efficient control rule sets that adapt to changes of hot water demand patterns. Consequently, over 20% of running costs were reduced compared to the control rule set optimized by parameter optimization.