Challenges in Gaussian Processes for Non Intrusive Load Monitoring
Non-intrusive load monitoring (NILM) or energy disaggregation aims to break down total household energy consumption into constituent appliances. Prior work has shown that providing an energy breakdown can help people save up to 15\% of energy. In recent years, deep neural networks (deep NNs) have ma...
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| creator | Desai, Aadesh Vashishtha, Gautam Patel, Zeel B Batra, Nipun |
| description | Non-intrusive load monitoring (NILM) or energy disaggregation aims to break down total household energy consumption into constituent appliances. Prior work has shown that providing an energy breakdown can help people save up to 15\% of energy. In recent years, deep neural networks (deep NNs) have made remarkable progress in the domain of NILM. In this paper, we demonstrate the performance of Gaussian Processes (GPs) for NILM. We choose GPs due to three main reasons: i) GPs inherently model uncertainty; ii) equivalence between infinite NNs and GPs; iii) by appropriately designing the kernel we can incorporate domain expertise. We explore and present the challenges of applying our GP approaches to NILM. |
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| subjects | Artificial neural networks Domains Energy consumption Gaussian process Monitoring Neural networks Residential energy |
| title | Challenges in Gaussian Processes for Non Intrusive Load Monitoring |
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