seer
A GAN (generative adversarial network) for projecting synthetic building performance profiles.
Science Score: 57.0%
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Low similarity (6.2%) to scientific vocabulary
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Repository
A GAN (generative adversarial network) for projecting synthetic building performance profiles.
Basic Info
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Metadata Files
README.md
seer
Synthetic Energy & Environment Replicator
Description
Seer is a conditional GAN that creates synthetic building performance profiles. Synthetic projections of building performance profiles are conditioned based on climate and operation constraints [see: "https://doi.org/10.1016/j.enbuild.2021.111334"]. The model requires three sets of inputs for training, i.e. "performance", "operation", and "weather": * The "performance" input should be a tensor of size [x,24,y], where x is the number of samples and y is the number of building perfromance features. * The "operation" input should be a Boolean array of size [x,z]: where z corresponds to the length of the one-hot-encoded operation constraints. * The "weather" input should have a size of [x,w], where w is the length of the weather constraints.
The data for training seer is obtained from "https://github.com/intelligent-environments-lab/CityLearn". Training data and synthetic outputs are available from "https://doi.org/10.5281/zenodo.4696060".
Architecture
Requirements
Seer has been tested using Python 3.8 and the following libraries: * Numpy 1.18.5 * Scipy 1.4.1 * TensorFlow 2.3.0
Contact
Fazel Khayatian, Urban Energy Systems Laboratory, Empa. https://www.empa.ch/web/khfa
Owner
- Name: khayatian
- Login: Khayatian
- Kind: user
- Repositories: 0
- Profile: https://github.com/Khayatian
Citation (CITATION.cff)
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cff-version: 1.2.0
title: 'seer: Synthetic Energy & Environment Replicator'
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If you use this software, please cite it using the
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authors:
- given-names: Fazel
family-names: Khayatian
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Swiss Federal Laboratories for Materials
Science and Technology-Empa