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User Comfort Enhancement in Home Energy Management Systems using Fuzzy Logic
Abstract and Figures
Energy consumption in residential sector is the 25% of all the sectors. Maintaining user comfort and energy optimization are the major tasks of Home Energy Management System. Appliances of Heating, Ventilation and Air Conditioning (HVAC) and lighting devices constitute up to 64% and 4% of energy consumption respectively in residential buildings. Different techniques like Demand Response (DR) and pricing tariffs like Time Of Use (TOU) has been used to make user participate in the energy consumption reduction. In the literature review, many techniques have been discussed which use Fuzzy Logic System integrated with other techniques for energy consumption minimization. However, user comfort is often sacrificed in these techniques. In this thesis, we have proposed a Fuzzy Inference System (FIS) that uses humidity as an input parameter in order to maintain the thermostat set-point according to user comfort. Additionally, we have used indoor room temperature variation as a feedback to proposed FIS in order to get the better energy consumption simulation. When defining FIS, number of rules in the rule base plays an important role in the correct working. With the increase in number of rules, task of defining them in FIS becomes time consuming and chances of manual errors increase. In this research, we have also proposed the automatic rule base generation using combinatorial method. Proposed techniques are evaluated using Mamdani FIS and Sugeno FIS. The input parameters of proposed FIS are indoor temperature, outdoor temperature, occupancy, price rate, initialized set points and humidity whereas energy consumption is the output of the system. Performance metrics used for the evaluation of the MATLAB simulation results are energy consumption, Peak-to-Average Ratio (PAR), cost, efficiency gain and user comfort. In addition, a model has been proposed which will quantify the user comfort with respect to different energy consumption levels. Simulation result validates that proposed technique reduces energy consumption by 28%.
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