Optimizing energy consumption of air-conditioning systems with the fuzzy logic controllers in residential buildings: Optimizing energy consumption of air-conditioning systems in residential buildings

Abstract

In this research work, two energy management controllers are proposed: swarm optimization fuzzy mamdani (SOFM) and swarm optimization fuzzy sugeno (SOFS) for the efficient scheduling and controlling of the electric loads in a residential building which is comprised of 10 apartments having the single family setup. Two types of electric loads are considered in terms of: daily used appliances and the seasonally used appliances. In addition, two demand side management strategies are used for managing both type of loads concerning to the load shifting and load curtailment. Daily used electric loads are the commonly used appliances considered in the residential buildings; whereas in the seasonally used electric loads, only air-conditioning systems are considered. Load scheduling technique (binary particle swarm optimization) is applied for the scheduling of daily used electric loads whereas load curtailment strategy (fuzzy logic) is applied for the seasonally used electric loads in order to manage the load in an effective fashion. The input parameters are: number of appliances, time-slots, power rating, length of operation time and utility price in case of the daily used electric loads; whereas in case of the seasonally used electric loads, we considered the following input parameters: initialized setpoints, user occupancy, price ratings, indoor and outdoor temperature. Output parameters considered for both types of the electric loads are: energy consumption (EC), cost, peak to average ratio (PAR) and energy efficiency. Simulations are performed in Matlab and results proved that our proposed controllers: SOFM and SOFS outperformed the unscheduled and existing approaches in terms of minimizing EC, cost, PAR and energy efficiency. SOFM outperformed to the existing and unscheduled approach till 45% and 48% in terms of minimizing EC and cost reduction.