Update README.md

README.md

@@ -23,6 +23,14 @@ The dataset used in this example is `Weijie1996/load_timeseries`, which contains
 - `target`
 - `category`
 
+## Data Sources
+
+The research uses raw data from the following open-source databases:
+
+- **Netherlands Smart Meter Data**: [Liander Open Data](https://www.liander.nl/partners/datadiensten/open-data/data)
+- **UK Smart Meter Data**: [London Datastore](https://data.london.gov.uk/dataset/smartmeter-energy-use-data-in-london-households)
+- **Germany Smart Meter Data**: [Open Power System Data](https://data.open-power-system-data.org/household_data/2020-04-15)
+
 ## Requirements
 
 - Python 3.6+
@@ -58,4 +66,15 @@ df = df.compute()
 2  NL_1  2013-01-01 02:00:00  0.103173      60m
 3  NL_1  2013-01-01 03:00:00  0.101686      60m
 4  NL_1  2013-01-01 04:00:00  0.099632      60m
-```
+```
+
+## Related Work
+
+This dataset has been utilized in the following research studies:
+
+1. **Comparative Assessment of Generative Models for Transformer- and Consumer-Level Load Profiles Generation**  
+   - GitHub Repository: [Generative Models for Customer Profile Generation](https://github.com/xiaweijie1996/Generative-Models-for-Customer-Profile-Generation)
+
+2. **A Flow-Based Model for Conditional and Probabilistic Electricity Consumption Profile Generation and Prediction**  
+   - GitHub Repository: [Full Convolutional Profile Flow](https://github.com/xiaweijie1996/Full-Convolutional-Profile-Flow?tab=readme-ov-file)
+