Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Energy Disaggregation via Learning Powerlets and Sparse Coding
Authors: Ehsan Elhamifar, Shankar Sastry
AAAI 2015 | Venue PDF | 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. |