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 [1].
Accurate Household Occupant Behavior Modeling Based on Data Mining Techniques
Authors: Mรกrcia Baptista, Anjie Fang, Helmut Prendinger, Rui Prada, Yohei Yamaguchi
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using real data from four households in Japan we are able to show that our model outperforms the traditional Markov chain model with respect to occupant coordination and generalization of behavior patterns. (...) Next, we compare the performance of traditional Markov chains to our model using real data of household occupant behavior. |
| Researcher Affiliation | Academia | Marcia Baptista, Anjie Fang, Helmut Prendinger National Institute of Informatics 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan (...) Rui Prada INESC-ID and Instituto Superior Tecnico, Universidade de Lisboa Av. Prof. Cavaco Silva, Taguspark Porto Salvo, Portugal (...) Yohei Yamaguchi Sustainable Energy and Environmental Engineering Graduate School of Engineering, Osaka University 2-1 Yamada-oka, Suita, Osaka 565-0871, Japan |
| Pseudocode | Yes | Algorithm 1 Calculating distance of activities |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | No | The data used in our experiment was collected from four households in the region of Osaka, Japan, in late 2011 and beginning of 2012. The paper does not provide concrete access information (link, DOI, repository, or citation) for this dataset, as it was collected by the authors and is not indicated as publicly available. |
| Dataset Splits | No | The paper describes a validation methodology, but it does not provide specific details on how the dataset itself was split into training, validation, and testing subsets for model development and evaluation. It mentions assessing accuracy by comparing output with original data. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as CPU or GPU models, or memory specifications. |
| Software Dependencies | No | The paper mentions the Apriori algorithm but does not specify any software dependencies with version numbers for implementation (e.g., Python 3.x, scikit-learn x.x, or specific library versions). |
| Experiment Setup | Yes | Each of the four households is simulated using the MC model and the NN model (ฯ2 = 0.002). In each simulation of a household, the daily activities of the occupants are reproduced for a period of 500 days. |