Comfort evaluation of seasonally and daily used residential load in smart buildings for hottest areas via predictive mean vote method

Abstract

In this paper, two energy management controllers: Binary Particle Swarm Optimization Fuzzy Mamdani (BPSOFMAM) and BPSOF Sugeno (BPSOFSUG) are proposed and implemented. Daily and seasonally used appliances are considered for the analysis of the efficient energy management through these controllers. Energy management is performed using the two Demand Side Management (DSM) strategies: load scheduling and load curtailment. In addition, these DSM strategies are evaluated using the meta-heuristic and artificially intelligent algorithms as BPSO and fuzzy logic. BPSO is used for scheduling of the daily used appliances, whereas fuzzy logic is applied for load curtailment of seasonally used appliances, i.e., Heating, Ventilation and Air Conditioning (HVAC) systems. Two fuzzy inference systems are applied in this work: fuzzy Mamdani and fuzzy Sugeno. This work is proposed for the energy management of the hottest areas of the world. The input parameters are: indoor temperature, outdoor temperature, occupancy, price, decision control variables, priority and length of operation times of the appliances, whereas the output parameters are: energy consumption, cost and thermal and appliance usage comfort. Moreover, the comfort level of the consumers regarding the usage of the appliances is computed using Fanger's predictive mean vote method. The comfort is further investigated by incorporating the renewable energy sources, i.e., photovoltaic systems. Simulation results show the effectiveness of the proposed controllers as compared to the unscheduled case. BPSOFSUG outperforms to the BPSOFMAM in terms of energy consumption and cost of the proposed scenario.