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PV-Power-Generation-Forecasting

architecture

🌞 Overview

This project focuses on predicting solar photovoltaic (PV) power generation based on regional microclimate data. The objective is to forecast the power output of PV devices installed in various terrains using environmental data such as temperature, humidity, wind speed, solar radiation, and rainfall.

💻 Installation

  1. Clone the repository: 
    git clone https://github.com/Shengwei-Peng/PV-Power-Generation-Forecasting.git
  2. Navigate to the project directory: 
    cd PV-Power-Generation-Forecasting
  3. Install the required dependencies: 
    pip install -r requirements.txt

📊 Dataset

1. TrainingData & TrainingData_Additional

TrainingData/
├── L1_Train.csv
├── L2_Train.csv
├── L3_Train.csv
├── L4_Train.csv
├── L5_Train.csv
├── L6_Train.csv
├── L7_Train.csv
├── L8_Train.csv
├── L9_Train.csv
├── L10_Train.csv
├── L11_Train.csv
├── L12_Train.csv
├── L13_Train.csv
├── L14_Train.csv
├── L15_Train.csv
├── L16_Train.csv
└── L17_Train.csv

TrainingData_Additional/
├── L2_Train_2.csv
├── L4_Train_2.csv
├── L7_Train_2.csv
├── L8_Train_2.csv
├── L9_Train_2.csv
├── L10_Train_2.csv
└── L12_Train_2.csv

  • AI CUP 2024

    Show/Hide Column Table
    Column Name Description Unit
    LocationCode Location identifier -
    DateTime Measurement timestamp -
    WindSpeed(m/s) Wind speed m/s
    Pressure(hpa) Atmospheric pressure hPa
    Temperature(°C) Temperature °C
    Humidity(%) Humidity percentage %
    Sunlight(Lux) Sunlight intensity Lux
    Power(mW) Power output mW

2. External Data

ExternalData/
├── 72T250.csv
├── 466990.csv
├── COT9E0.csv
├── COT870.csv
├── COZ100.csv
├── COZ150.csv
├── IFENGL2.csv
└── IHUALIEN3.csv

  • 72T250, 466990, COT9E0, C0T870, C0Z100, C0Z150

    Show/Hide Column Table
    Column Name Description Unit
    Station Pressure (hPa) Station-level pressure hPa
    Sea Level Pressure (hPa) Sea-level adjusted pressure hPa
    Temperature (°C) Air temperature °C
    Dew Point (°C) Dew point temperature °C
    Relative Humidity (%) Air humidity %
    Wind Speed (m/s) Wind speed m/s
    Wind Direction (°) Wind direction °
    Max Wind Speed (m/s) Maximum wind speed m/s
    Max Wind Direction (°) Direction of max wind speed °
    Precipitation (mm) Total precipitation mm
    Sunshine Duration (h) Duration of sunshine h
    Solar Radiation (MJ/m²) Solar radiation MJ/m²
    Ground Temp (0cm) Ground temperature (0cm) °C
    Ground Temp (5cm) Ground temperature (5cm) °C
    Ground Temp (10cm) Ground temperature (10cm) °C
    Ground Temp (20cm) Ground temperature (20cm) °C
    Ground Temp (50cm) Ground temperature (50cm) °C
    Ground Temp (100cm) Ground temperature (100cm) °C

  • IFENGL2, IHUALIEN3

    Show/Hide Column Table
    Column Name Description Unit
    Temperature Air temperature °C
    Dew Point Dew point temperature °C
    Humidity Air humidity %
    Wind Wind direction °
    Speed Wind speed km/h
    Gust Peak wind speed km/h
    Pressure Atmospheric pressure hPa
    Precip. Rate Rainfall rate mm
    Precip. Accum. Total rainfall mm
    UV UV radiation index -
    Solar Solar radiation W/m²

🛠️ Usage

Follow the steps in main.ipynb:

  1. Import and Setup
    Load libraries and functions for data handling and modeling.

  2. Data Preprocessing (Optional)
    If train_x.csv, train_y.csv, and test_x.csv are not available, preprocess the raw data to generate them.

  3. Data Loading
    Load preprocessed data and select feature columns.

  4. Model Training
    Train CatBoost, LightGBM, and XGBoost, make predictions, and save models and results.

  5. Ensemble
    Combine model predictions using an ensemble method and generate the final submission.csv for evaluation.

⚠️ Warning: It is recommended to use a device with at least 64GB of RAM and a GPU for training.

📂 Final Output Structure

After completing the training and prediction, the following files will be generated:

AICUP/
├── training_data.csv     # Combined data from `TrainingData` and `TrainingData_Additional` folder
├── external_data.csv     # Combined external data from the `ExternalData` folder
├── train_x.csv           # Features used for training the models
├── train_y.csv           # Target variable used for training the models
├── test_x.csv            # Features used for making predictions
├── catboost_model.pkl    # Trained CatBoost model
├── lightgbm_model.pkl    # Trained LightGBM model
├── xgboost_model.pkl     # Trained XGBoost model
├── catboost_pred.csv     # Predictions from CatBoost model
├── lightgbm_pred.csv     # Predictions from LightGBM model
├── xgboost_pred.csv      # Predictions from XGBoost model
└── submission.csv        # The final submission file containing the ensemble predictions

🔄 Reproduction

Run the reproduction script:

To reproduce the results of this project, simply run the reproduce.sh script.

bash reproduce.sh

This script will automatically:

  • Install the required dependencies using pip.
  • Uses gdown to download necessary files from Google Drive.
  • Create a Reproduction folder to store the results.
  • Execute src.reproduc to generate the outputs.

You can also find the best results and best models here.

Retrain the models:

If you want to retrain the models from scratch, run the following command:

bash reproduce.sh --retrain

📈 Results

score_trend

Show/Hide Results Table
ID Upload Time Public Score Private Score
01 2024-11-18 05:00:40 2080700.95 2279572.22
02 2024-11-18 09:44:50 1789119.30 2174549.41
03 2024-11-18 09:57:53 1936269.36 2148889.63
04 2024-11-18 10:08:51 1811221.68 1842050.13
05 2024-11-18 10:32:02 1837177.48 2096727.91
06 2024-11-19 12:04:01 1987431.54 2148135.81
07 2024-11-19 03:32:53 1720059.50 2060600.83
08 2024-11-19 09:59:17 985780.43 960078.27
09 2024-11-19 10:14:01 854752.05 879881.16
10 2024-11-19 10:48:57 1072685.57 1101202.66
11 2024-11-20 12:54:06 824205.41 870917.05
12 2024-11-20 01:01:58 628281.62 695738.97
13 2024-11-20 05:09:11 581359.93 667819.00
14 2024-11-20 07:23:46 560151.59 647792.75
15 2024-11-20 07:43:33 570915.15 598089.80
16 2024-11-21 01:09:59 503108.75 590526.12
17 2024-11-21 01:34:51 575301.23 617709.15
18 2024-11-21 01:42:31 566783.82 601752.06
19 2024-11-21 02:03:29 503497.19 556083.36
20 2024-11-21 02:54:46 651794.75 721346.92
21 2024-11-22 12:10:06 500977.86 558400.65
22 2024-11-22 12:24:05 500588.64 551565.93
23 2024-11-22 01:55:31 502596.13 541711.74
24 2024-11-22 04:29:29 2067988.84 2212960.51
25 2024-11-22 05:21:02 483064.06 593766.05
26 2024-11-23 01:52:24 472851.80 568710.49
27 2024-11-23 02:22:18 461282.36 564863.34
28 2024-11-23 03:19:37 418670.68 532466.45
29 2024-11-23 11:41:31 407476.71 508553.97
30 2024-11-23 12:05:03 401027.85 511159.04
31 2024-11-24 12:05:48 407476.71 507011.25
32 2024-11-24 12:19:11 400222.27 460386.16
33 2024-11-24 02:35:06 442772.71 531965.98
34 2024-11-24 03:32:58 398627.30 506857.27
35 2024-11-24 08:29:17 393346.07 451063.93
36 2024-11-25 01:07:29 417173.37 495012.90
37 2024-11-25 01:52:03 404582.89 523922.83
38 2024-11-25 10:25:27 407552.42 514735.62
39 2024-11-25 12:58:02 377853.09 485979.75
40 2024-11-25 09:43:45 397801.42 491359.28
41 2024-11-26 01:27:38 388330.38 473736.73
42 2024-11-26 11:42:06 390831.75 491397.55
43 2024-11-26 02:03:14 378537.11 487647.68
44 2024-11-26 06:52:44 368758.14 424508.42
45 2024-11-26 07:51:48 381491.54 495907.34
46 2024-11-27 03:05:57 388500.90 487503.77
47 2024-11-27 03:37:11 377664.51 452648.44
48 2024-11-27 04:39:25 402065.87 488541.13
49 2024-11-27 05:05:18 397547.12 482793.11
50 2024-11-27 11:41:17 431869.78 512407.47
51 2024-11-28 02:02:39 388686.62 482839.81
52 2024-11-28 10:59:42 366082.67 423951.51
53 2024-11-28 06:20:57 394886.78 499104.56
54 2024-11-28 09:22:29 386407.25 490622.69
55 2024-11-28 11:57:48 356359.07 419747.93

🙏 Acknowledgements

We would like to express our sincere gratitude to the following organizations for their support and contributions to this project:

  • Competition Guidance Unit: Information and Technology Education Department of the Ministry of Education.
  • Competition Planning Unit: Artificial Intelligence Competition and Annotation Data Collection Project Office of the Ministry of Education.
  • Topic Provider: Department of Information Engineering, National Dong Hwa University.
  • Platform Sponsor: Trend Micro.

🤝 Contributing

We welcome contributions to the project! Please follow the guidelines below:

  1. Fork the repository.
  2. Create a new branch (feature/your-feature-name).
  3. Commit your changes.
  4. Submit a pull request.

⚖️ License

This project is licensed under the Apache License 2.0. See the LICENSE file for more details.

📧 Contact

For any questions or inquiries, please contact [email protected]