Patrick Emami, Abhijeet Sahu, Peter Graf
We lifted 6 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| NREL/BuildingsBench | canonical | 0 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| absolute_error | Not yet run | NREL/BuildingsBench/buildings_bench/evaluation/metrics.py code served (permissive licence) · get_code("f0c9e682f0cab1f6") |
| bias_error | Not yet run | NREL/BuildingsBench/buildings_bench/evaluation/metrics.py code served (permissive licence) · get_code("f975e66c06137e29") |
| get_puma_county_lookup_table | Not yet run | NREL/BuildingsBench/buildings_bench/utils.py code served (permissive licence) · get_code("6c1ee89025af6a2e") |
| load_model_checkpoint | Not yet run | NREL/BuildingsBench/buildings_bench/utils.py code served (permissive licence) · get_code("673a7f585bf3c71b") |
| squared_error | Not yet run | NREL/BuildingsBench/buildings_bench/evaluation/metrics.py code served (permissive licence) · get_code("97e0d2619679e5a7") |
| time_features_to_datetime | Not yet run | NREL/BuildingsBench/buildings_bench/utils.py code served (permissive licence) · get_code("40bc59777d0a77cc") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Short-term forecasting of residential and commercial building energy consumption is widely used in power systems and continues to grow in importance. Data-driven short-term load forecasting (STLF), although promising, has suffered from a lack of open, large-scale datasets with high building diversity. This has hindered exploring the pretrain-then-fine-tune paradigm for STLF. To help address this, we present BuildingsBench, which consists of: 1) Buildings-900K, a large-scale dataset of 900K simulated buildings representing the U.S. building stock; and 2) an evaluation platform with over 1,900 real residential and commercial buildings from 7 open datasets. BuildingsBench benchmarks two under-explored tasks: zero-shot STLF, where a pretrained model is evaluated on unseen buildings without fine-tuning, and transfer learning, where a pretrained model is fine-tuned on a target building. The main finding of our benchmark analysis is that synthetically pretrained models generalize surprisingly well to real commercial buildings. An exploration of the effect of increasing dataset size and diversity on zero-shot commercial building performance reveals a power-law with diminishing returns. We also show that finetuning pretrained models on real commercial and residential buildings improves performance for a majority of target buildings. We hope that BuildingsBench encourages and facilitates future research on generalizable STLF. All datasets and code can be accessed from https://github.com/NREL/BuildingsBench.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2307.00142")
get_code_for_paper("2307.00142")
have("2307.00142")
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