Name
Abstract | ||
|---|---|---|
Energy disaggregation is to discover the energy consumption of individual appliances from their aggregated energy values. To solve the problem, most existing approaches rely on either appliances' signatures or their state transition patterns, both hard to obtain in practice. Aiming at developing a simple, universal model that works without depending on sophisticated machine learning techniques or auxiliary equipments, we make use of easily accessible knowledge of appliances and the sparsity of the switching events to design a Sparse Switching Event Recovering (SSER) method. By minimizing the total variation (TV) of the (sparse) event matrix, SSER can effectively recover the individual energy consumption values from the aggregated ones. To speed up the process, a Parallel Local Optimization Algorithm (PLOA) is proposed to solve the problem in active epochs of appliance activities in parallel. Using real-world trace data, we compare the performance of our method with that of the state-of-the-art solutions, including the popular Least Square Estimation (LSE) methods and a recently-developed machine learning method using iterative Hidden Markov Model (HMM). The results show that our approach has an overall better performance in both detection accuracy and overhead. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/SmartGridComm.2014.7007707 | SmartGridComm |
Keywords | Field | DocType |
optimisation,machine learning method,detection accuracy,learning (artificial intelligence),appliances signatures,model-driven approach,matrix algebra,sser method,energy disaggregation,energy consumption values,machine learning techniques,total variation,power system measurement,sparse event matrix,least squares approximations,state transition patterns,switching events,least square estimation methods,energy consumption,iterative hidden markov model,aggregated energy values,sparse switching event recovering,hidden markov models,appliance activities,auxiliary equipments,domestic appliances,iterative methods,parallel local optimization algorithm,vectors,accuracy,switches | Least squares,Mathematical optimization,Matrix (mathematics),Local search (optimization),Engineering,Hidden Markov model,Energy consumption,Speedup | Conference |
ISSN | Citations | PageRank |
2373-6836 | 6 | 0.52 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guoming Tang | 1 | 67 | 17.62 |
| Kui Wu | 2 | 32 | 6.79 |
| Jingsheng Lei | 3 | 691 | 69.87 |
| Jiuyang Tang | 4 | 46 | 12.86 |