Location-aware Energy Disaggregation in Smart Homes

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Providing detailed appliance level energy consumption may lead consumers to understand their usage behavior and encourage them to optimize the energy usage. Non-intrusive load monitoring (NILM) or energy disaggregation aims to estimate appliance level energy consumption from aggregate consumption data of households. Hitherto, proposed NILM algorithms are either centralized or require high performance systems to derive appliance level data, owing to the computational complexity associated. This approach raises several issues related to scalability and privacy of consumer's data. In this thesis, we present the \textit{NILM-Loc Framework} that utilizes occupancy of users to derive accurate appliance level usage information. NILM-Loc framework limits the appliances considered for disaggregation based on the current location of the occupants. Thus, it can provide real-time feedback on appliance level energy consumption and run on an embedded system locally at the household. We propose several accuracy metrics to study the performance of NILM-Loc. To test its robustness, we empirically evaluated it across multiple publicly available datasets. NILM-Loc has significantly higher energy disaggregation accuracy while exponentially reducing the computational complexity. NILM-Loc presents accuracy improvements up to 30\% better than other traditional methods. It reaches an accuracy of 89\% for the evaluated datasets. We also detail a case study for the use of the fine-grained appliance-level energy information obtained. We present a load scheduler that minimizes cost and discomfort based on hourly day-ahead pricing. The proposed Demand Response (DR) system ensures user discomfort is minimzed by abstracting patterns from past user behavior and incorporating them to the designed cost-optimal schedule.