Household Structure Analysis via Hawkes Processes for Enhancing Energy Disaggregation
Authors: Liangda Li, Hongyuan Zha
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on both synthetic data and four real world data sets demonstrate the effectiveness of our model, which outperforms state-of-the-art approaches in not only decomposing the entire consumed energy to each appliance in houses, but also the inference of household structures. We evaluated our Para Hawkes-LDA model on both synthetic and real-world data sets, and compared the performance with the following baselines. |
| Researcher Affiliation | Academia | Liangda Li1,2 and Hongyuan Zha2,1 1Software Engineering Institute, East China Normal University, Shanghai, China 2College of Computing, Georgia Institute of Technology, Atlanta, GA, USA |
| Pseudocode | No | The paper describes the model and inference algorithm using mathematical equations and textual explanations, but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about making its source code available, nor does it include a link to a code repository. |
| Open Datasets | Yes | We evaluated our Para Hawkes-LDA model on both synthetic and real-world data sets... The first data set is Smart* [Barker et al., 2012]... The second data set is Reference Energy Disaggregation Dataset (REDD) [Kolter and Johnson, 2011]... The third data set is Pecan Street 2 http://www.pecanstreet.org/... The fourth data set is a Irish smart-grid data set collected by Commission for Energy Regulation (CER) 3 http://www.cer.ie/ |
| Dataset Splits | No | To avoid overfitting issues, we adopt a k-fold cross validation strategy, and select the optimal number of user appliance usage pattern K. However, the paper does not specify the exact percentages or counts for the validation split. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as CPU or GPU models, or memory specifications. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers (e.g., libraries, frameworks, or programming language versions) used for the implementation or experiments. |
| Experiment Setup | No | The paper mentions 'The default value of σ0 is set to be 1.' for noisy data generation and 'In our experiments, we use a constant σ.' for Gaussian noise. However, it does not provide specific hyperparameters or system-level training settings like learning rates, batch sizes, number of epochs, or optimizer configurations for the Para Hawkes-LDA model itself. |