Disambiguating Energy Disaggregation: A Collective Probabilistic Approach
Authors: Sabina Tomkins, Jay Pujara, Lise Getoor
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our proposed framework on two real-world data sets. Empirical results demonstrate that our proposed probabilistic model significantly outperforms existing state-of-the-art techniques. |
| Researcher Affiliation | Academia | Sabina Tomkins and Jay Pujara and Lise Getoor University of California, Santa Cruz {satomkin, jpujara, getoor}@ucsc.edu |
| Pseudocode | No | The paper describes the probabilistic framework and rules using logical expressions and mathematical formulations, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code available: https://bitbucket.org/linqs/appliance_disambiguation |
| Open Datasets | Yes | We evaluate on two public datasets: The Reference Energy Disaggregation Dataset (REDD) [Kolter and Johnson, 2011] and Pecan Street Inc. (DATAPORT) [2016]. |
| Dataset Splits | Yes | The models for each home (including the weights) are trained using the first 50% of the data, the next 25% of the data is used as a validation set for model parameters, and the model was evaluated on the final 25% of the data. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments (e.g., specific CPU/GPU models, memory, or cluster configurations). |
| Software Dependencies | Yes | To learn the required parameters we used the Matlab HMM toolbox [Murphy, 1998] and the first 75% of the data for each home. |
| Experiment Setup | Yes | The models for each home (including the weights) are trained using the first 50% of the data, the next 25% of the data is used as a validation set for model parameters, and the model was evaluated on the final 25% of the data. [...] To find the thresholds to partition duration lengths into very short, short, medium, and long, we found quartiles for the interval lengths, such that 25% of all duration lengths were assigned to each duration. [...] To assign feasible appliance sets for each interval, we compute the absolute difference between the mean consumption for each appliance set and the observed power, scaled to be in [0, 1] and we then retain only those sets which are within 0.5 of the actual power consumption. If there are no such sets, we select the top three closest sets. |