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SDG_Algorithms-Data

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# Synthetic Data Generation Algorithms and Data Sharing
This repository works for IEEE PES Task Force on Power System Synthetic Data Generation and Sharing. https://cmte.ieee.org/pes-pssdgs/

We encourage researchers to contribute to the community by open-sourcing their algorithms on the topic of Synthetic Data Generation.

## Organizers
Yi Hu (North Carolina State University)

Ning Lu (North Carolina State University)

## Contact
Please send your questions and comments to: hugh19flyer@gmail.com

## Citation
If you are using the shared dataset, please cite this website:

_Yi Hu, Ning Lu "Synthetic data generation algorithm and data sharing", https://github.com/SyntheticDataGenerationAndSharing/SDG_Algorithms-Data_

If you are using specific algorithm shared in this repository, please cite the corresponding paper.

## Open-source Algorithms
### 1. BERT-PIN
A BERT powered framework for recovering missing data segments in load profiles. https://github.com/hughwln/BERT-PIN_public

Basic idea of BERT-PIN. If someone is proficient in speaking French, "France" may emerge as the most probable response. Nevertheless, "Quebec" and "Cameroon" also stand as viable alternatives, as people from these regions also use French as an official language. Likewise, when restoring an MDS, multiple patching options are available, each with a comparable likelihood of being the best match. Nevertheless, the majority of existing inpainting techniques only provide a single viable solution.

For more details, please refer to the paper:

_Yi Hu, Kai Ye, Hyeonjin Kim, and Ning Lu, “BERT-PIN: A BERT-based Framework for Recovering Missing Data Segments in Time-series Load Profiles”, 2023, arXiv: 2310.17742. Available: http://arxiv.org/abs/2310.17742_

**Abstract**: Inspired by the success of the Transformer model in natural language processing and computer vision, this paper introduces BERT-PIN, a Bidirectional Encoder Representations from Transformers (BERT) powered Profile Inpainting Network. BERT¬-PIN recovers multiple missing data segments (MDSs) using load and temperature time-series profiles as inputs. To adopt a standard Transformer model structure for profile inpainting, we segment the load and temperature profiles into line segments, treating each segment as a word and the entire profile as a sentence. We incorporate a top candidates selection process in BERT-PIN, enabling it to produce a sequence of probability distributions, based on which users can generate multiple plausible imputed data sets, each reflecting different confidence levels. We develop and evaluate BERT-PIN using real-world dataset for two applications: multiple MDSs recovery and demand response baseline estimation. Simulation results show that BERT-PIN outperforms the existing methods in accuracy while is capable of restoring multiple MDSs within a longer window. BERT-PIN, served as a pre-trained model, can be fine-tuned for conducting many downstream tasks, such as classification and super resolution.

### 2. MultiLoad-GAN & SingleLoad-GAN
Generate a group of load profiles considering spatial-temporal correlations. https://github.com/hughwln/MultiLoad-GAN_public

Basic idea of MultiLoad-GAN and SingleLoad-GAN:

![image](https://github.com/SyntheticDataGenerationAndSharing/SDG_Algorithms-Data/assets/140336634/fdaf2841-dff2-4acb-9909-361470c97b8e)

Generated load group example:

![image](https://github.com/SyntheticDataGenerationAndSharing/SDG_Algorithms-Data/assets/140336634/9298d246-f6d2-4d0c-98cd-4c9122b1054a)

For more details, please refer to the paper:

_Yi Hu, Yiyan Li, Lidong Song, Han Pyo Lee, PJ Rehm, Mattew Makdad, Edmond Miller, and Ning Lu, "MultiLoad-GAN: A GAN-Based Synthetic Load Group Generation Method Considering Spatial-Temporal Correlations," in IEEE Transactions on Smart Grid, doi: 10.1109/TSG.2023.3302192._

**Abstract**: This paper presents a deep-learning framework, Multi-load Generative Adversarial Network (MultiLoad-GAN), for generating a group of synthetic load profiles (SLPs) simultaneously. The main contribution of MultiLoad-GAN is the capture of spatial-temporal correlations among a group of loads that are served by the same distribution transformer. This enables the generation of a large amount of correlated SLPs required for microgrid and distribution system studies. The novelty and uniqueness of the MultiLoad-GAN framework are three-fold. First, to the best of our knowledge, this is the first method for generating a group of load profiles bearing realistic spatial-temporal correlations simultaneously. Second, two complementary realisticness metrics for evaluating generated load profiles are developed: computing statistics based on domain knowledge and comparing high-level features via a deep-learning classifier. Third, to tackle data scarcity, a novel iterative data augmentation mechanism is developed to generate training samples for enhancing the training of both the classifier and the MultiLoad-GAN model. Simulation results show that MultiLoad-GAN can generate more realistic load profiles than existing approaches, especially in group level characteristics.  With little finetuning, MultiLoad-GAN can be readily extended to generate a group of load or PV profiles for a feeder or a service area.

### 3. profile_infilling
GAN-based method to restore missing data and estimate baseline of CVR events. https://github.com/EricLDS/Load_Profile_Inpainting

_Yiyan Li, Lidong Song, Yi Hu, Hanpyo Lee, Di Wu, PJ Rehm, Ning Lu, "Load Profile Inpainting for Missing Load Data Restoration and Baseline Estimation," in IEEE Transactions on Smart Grid, doi: 10.1109/TSG.2023.3293188._

**Abstract**: This paper introduces a Generative Adversarial Nets (GAN) based, Load Profile Inpainting Network (Load-PIN) for restoring missing load data segments and estimating the baseline for a demand response event. The inputs are time series load databefore and after the inpainting period together with explanatory variables (e.g., weather data). We propose a Generator structure consisting of a coarse network and a fine-tuning network. The coarse network provides an initial estimation of the data segment in the inpainting period. The fine-tuning network consists of selfattention blocks and gated convolution layers for adjusting the initial estimations. Loss functions are specially designed for the fine-tuning and the discriminator networks to enhance both the point-to-point accuracy and realisticness of the results. We test the Load-PIN on three real-world data sets for two applications: patching missing data and deriving baselines of conservation voltage reduction (CVR) events. We benchmark the performance of Load-PIN with five existing deep-learning methods. Our simulation results show that, compared with the state-of-the-art methods, Load-PIN can handle varying-length missing data events and achieve 15-30% accuracy improvement.

### 4. ProfileSR-GAN
https://github.com/EricLDS/ProfileSR_GAN

_Song, Lidong, Yiyan Li, and Ning Lu. "ProfileSR-GAN: A gan based super-resolution method for generating high-resolution load profiles." IEEE Transactions on Smart Grid 13.4 (2022): 3278-3289._

## Shared dataset
### 1. MultiLoad-GAN and SingleLoad-GAN generated data sample: 
https://drive.google.com/drive/folders/1uenITdDWMVU3MTGXlJ-VFnsTMd-Cwncq

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