Illinois building energy consumption

This repository contains two datasets of 592 Illinois buildings each, one being more heterogenous than the other. The data is sourced from the NREL ComStock model/dataset.

Usage

Multivariate dataset

The file custom_dataset.py contains the function get_data_and_generate_train_val_test_sets, which takes in three arguments as follows:

data_array: This takes in a np.ndarray of shape (num_buildings, time_points, num_features) (note that for our experiments, num_features is fixed to 8). You can load them from the .npz files provided in this repository as data_array = np.load(./IllinoisHeterogenous.npz)['data'].
split_ratios: A list of positives that sum upto 1, denoting the split (along the time axis) into train-validation-test sets. For example, split_ratios = [0.8,0.1,0.1]. Must sum to 1.
dataset_kwargs: Additional kwargs for configuring data. For example, dataset_kwargs = { 'lookback':96, 'lookahead':4, 'normalize':True, 'transformer':True }.
- 'lookback` is the number of previous points fed as input. Also denoted by L.
- lookahead is the number of points ahead to predict. Also denoted by T.
- normalize (boolean): If set to True, data is normalized per-feature.
- transformer (boolean): If set to True and normalize is also True, then categorical time features are not normalized. Useful for embedding said features in Transformers.

The outputs are as follows:

train: torch.utils.data.Dataset containing the train data.
val: torch.utils.data.Dataset containing the validation data.
test: torch.utils.data.Dataset containing the test data.
mean: np.ndarray containing the featurewise mean. If normalization is False, then it defaults to all 0s.
std: np.ndarray containing the featurewise standard deviation. If normalization is False, then it defaults to all 1s.

Univariate dataset

The file custom_dataset_univariate.py is used in the same way as custom_dataset.py, except dataset_kwargs does not take in a key called transformer (it is deprecated) since there are no categorical features to normalize.

Requirements and Example

This repository only requires numpy and torch packages to run. For details on elements of each dataset, plus its different configurations, one is encouraged to run example_dataset.py.

ComStock Notice

This data includes information from the ComStock™ dataset developed by the National Renewable Energy Laboratory (NREL) with funding from the U.S. Department of Energy (DOE). This model was trained using ComStock release 2023.2. NREL regularly publishes updated datasets which generally improve the representation of building energy consumption. Users interested in training their own models should review the latest dataset releases to assess whether recent updates offer features relevant to their modeling objectives.

Suggested Citation:

Parker, Andrew, et al. 2023. ComStock Reference Documentation. Golden, CO: National Renewable Energy Laboratory. NREL/TP-5500-83819. https://www.nrel.gov/docs/fy23osti/83819.pdf