Implementation of intelligent methods of building energy management for economic optimization

Rapid growth of infrastructures, industries, and buildings has led to increase energy needed for heating and cooling. On the other hand, as buildings are one of the greatest energy consumers, optimization of energy management significantly affects energy consumption. The cost difference between optimized and non-optimized system, motivates researchers to find a way to manage dealing with energy sources. The present study aims to apply intelligent algorithms including genetic algorithm and a set of fuzzy rules in order to optimize a gas/solar hybrid system from the perspective of building energy management. These two techniques are used to design a hybrid heating system controller of a building in order to set the house temperature and reduce the cost of energy supply and consumption of gas. Finally, it is concluded that fuzzy trial and error control system can lead to 13% reduction of energy consumption costs in normal situation and 15% in disturbed situation and set house temperature faster and more accurate compare to other methods.