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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
HVAC-Aware Occupancy Scheduling (Extended Abstract)
Authors: Boon-Ping Lim
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Results show that the HVAC-aware scheduling model leads to significant consumption reduction (50% to 70% in our experiments) when compared to occupancybased HVAC control using arbitrary schedules or energyaware schedules generated by heuristic methods. |
| Researcher Affiliation | Academia | Boon-Ping Lim Optimisation Research Group, NICTA Research School of Computer Science, ANU EMAIL |
| Pseudocode | No | The paper describes algorithms conceptually but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information for source code. |
| Open Datasets | No | The paper mentions 'our experiments' but does not provide any specific dataset names, links, or citations to publicly available data used for training. |
| Dataset Splits | No | The paper does not provide specific details on dataset splits (e.g., training, validation, test percentages or counts). |
| Hardware Specification | No | The paper does not mention any specific hardware (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper mentions 'MILP model' and 'MILP solvers' but does not specify any software names with version numbers. |
| Experiment Setup | Yes | In our destroy step, we remove all meetings in two, three, or four randomly selected zones. We do, however, limit the MILP runtime to avoid excessive search during repair. |