Evolutionary Optimization Applied to Usage of Solar Energy for Powering a Heat Pump

Abstract: This paper researches the impact of different settings on an Infinite Impulse Response-filter (IIR-filter) used on a NIBE heat pump in combination with photovoltaic panels (PV-panel). The IIR-filter is applied to the level of the PV-panel’s power and its output is used by the heat pump’s control to harvest as much solar power as possible for supplying the heat pump with electricity. In some of the experiments weather data is used in the form of a forecast regarding the incoming cloudiness in the area, called “cloud coverage”. My objective is to find out which setting performs the best, and whether an evolutionary algorithm can find an optimal setting. The evolutionary algorithms I try are Genetic Algorithm, Simulated Annealing and the Hill Climbing algorithm. Historical data is collected from one of NIBE’s active heat pumps running in a field test. The data is processed and experimented on using an algorithm that analyzes how close a certain setting of values for the coefficient used in the filter and sensitivity of the cloud coverage forecast performs compared to an ideal reference. By using an evolutionary algorithm a better solution to the usage of solar energy can be found, compared to the non-evolutionary algorithm, by using a combination of different values for the coefficient in the filter, and also the cloud coverage forecast, which decides when we should change to another value for the filter coefficient.