Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A variety of energy management and analytics techniques rely on models of the por usage of a device over time. Unfortunately, the models employed by these techniques are often highly simplistic, such as modeling devices as simply being on with a fixed por usage or off and consuming little por. As  show, even the por usage of relatively simple devices exhibits much more complexity than a simple on and off state. To address the problem,  present a Non-Intrusive Model Derivation (NIMD) algorithm to automate modeling of residential electric loads using concepts from por systems, statistics, and machine learning. NIMD automatically derives a compact representation of the time-varying por usage of any residential electrical load, including both the device's energy usage and its pattern of usage over time. Such models are useful for a variety of analytics techniques, such as Non-Intrusive Load Monitoring, that have relied on simple on-off models in the past.  evaluate the accuracy of our models by comparing them with both actual ground truth data, and against models that have been designed manually by human experts.  show that models derived via NIMD are comparable in accuracy to models built by experts and closely approximate the ground truth data.
['Srinivasan Iyengar', 'David E. Irwin', 'Prashant J. Shenoy']
I am writing a research paper on a idea Non-intrusive model derivation: automated modeling of residential electrical loads
[{"from": "human", "value": "I am writing a research paper on a idea Non-intrusive model derivation: automated modeling of residential electrical loads"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A variety of energy management and analytics techniques rely on models of the por usage of a device over time. Unfortunately, the models employed by these techniques are often highly simplistic, such as modeling devices as simply being on with a fixed por usage or off and consuming little por. As  show, even the por usage of relatively simple devices exhibits much more complexity than a simple on and off state. To address the problem,  present a Non-Intrusive Model Derivation (NIMD) algorithm to automate modeling of residential electric loads using concepts from por systems, statistics, and machine learning. NIMD automatically derives a compact representation of the time-varying por usage of any residential electrical load, including both the device's energy usage and its pattern of usage over time. Such models are useful for a variety of analytics techniques, such as Non-Intrusive Load Monitoring, that have relied on simple on-off models in the past.  evaluate the accuracy of our models by comparing them with both actual ground truth data, and against models that have been designed manually by human experts.  show that models derived via NIMD are comparable in accuracy to models built by experts and closely approximate the ground truth data."}]