Energy Disaggregation via Learning Powerlets and Sparse Coding
Authors: Ehsan Elhamifar, Shankar Sastry
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Real experiments on a publicly available energy dataset demonstrate that our proposed algorithm achieves promising results for energy disaggregation. |
| Researcher Affiliation | Academia | Ehsan Elhamifar and Shankar Sastry Electrical Engineering and Computer Sciences Department University of California, Berkeley |
| Pseudocode | No | The paper describes the proposed algorithms and frameworks using mathematical equations and textual explanations, but it does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about making its source code publicly available, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We evaluate our proposed energy disaggregation framework on the real-world REDD dataset (Kolter and Johnson 2011), a large publicly available dataset for electricity disaggregation. |
| Dataset Splits | No | The paper states 'we use the first week of recorded electricity signals for training and use the rest of data for testing', but does not mention a distinct validation set or specific split percentages for training, validation, and test. |
| Hardware Specification | No | The paper does not provide specific hardware details such as CPU/GPU models, memory, or cloud instance types used for experiments. |
| Software Dependencies | No | The paper mentions using 'the standard integer programming solver of MOSEK' but does not specify its version number or any other software dependencies with versions. |
| Experiment Setup | Yes | We choose a window size of w = 15. We model each device as a mixture of dynamical systems of the form (8), where we set the model order to be m = 3. We use our subset selection scheme in (7) to extract about 20 powerlets for each device. In order to perform disaggregation using the optimization program (19), we set λ = 30 and η = η = 1 and use the prior that kitchen appliances typically work together. |