Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Detailed information about a home's occupancy is necessary to implement many advanced energy-efficiency optimizations. Hover, monitoring occupancy directly is intrusive, typically requiring the deployment of multiple environmental sensors, e.g., motion, acoustic, CO2, etc. ,  explore the potential for Non-Intrusive Occupancy Monitoring (NIOM) by using electricity data from smart meters to infer occupancy.  first observe that a home's pattern of electricity usage generally changes when occupants are present due to their interact with electrical loads.  empirically evaluate these interactions by monitoring ground truth occupancy in two homes, then correlating it with changes in statistical metrics of smart meter data, such as por's mean and variance, over short intervals. In particular,  use each metric's maximum value at night as a proxy for its maximum value in an unoccupied home, and then signal occupancy whenever the daytime value exceeds it. Our results highlight NIOM's potential and its challenges.
['Dong Chen', 'Sean Kenneth Barker', 'Adarsh Subbaswamy', 'David E. Irwin', 'Prashant J. Shenoy']
I am writing a research paper on a idea Non-Intrusive Occupancy Monitoring using Smart Meters
[{"from": "human", "value": "I am writing a research paper on a idea Non-Intrusive Occupancy Monitoring using Smart Meters"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Detailed information about a home's occupancy is necessary to implement many advanced energy-efficiency optimizations. Hover, monitoring occupancy directly is intrusive, typically requiring the deployment of multiple environmental sensors, e.g., motion, acoustic, CO2, etc. ,  explore the potential for Non-Intrusive Occupancy Monitoring (NIOM) by using electricity data from smart meters to infer occupancy.  first observe that a home's pattern of electricity usage generally changes when occupants are present due to their interact with electrical loads.  empirically evaluate these interactions by monitoring ground truth occupancy in two homes, then correlating it with changes in statistical metrics of smart meter data, such as por's mean and variance, over short intervals. In particular,  use each metric's maximum value at night as a proxy for its maximum value in an unoccupied home, and then signal occupancy whenever the daytime value exceeds it. Our results highlight NIOM's potential and its challenges."}]