/lstm

Primary LanguagePython

List Of Models

LSTM

Short-Term Wind Power Forecast Based on Continuous Conditional Random Field

FFNN

AWNN-Assisted Wind Power Forecasting Using Feed-Forward Neural Network

GPCF

A High-Accuracy Wind Power Forecasting Model (Letter)

XGBOOST

濮博【Adapted，原先功率他做的是分位预测，我把他竞价预测的拟合方法直接挪到功率上用了】

GPNN

Xin Yu's Implementation

RandomForest

Day-Ahead Hourly Forecasting of Power Generation From Photovoltaic Plants

Steps

  1. create a yaml configuration file under ./conf.
  2. Use preprocess.py. Remeber to replace SPLIT variable to the split associated with your newly created file in ./conf first!
  3. Replace source_power_stat with the preprocessed sorted one by preprocess.py.
  4. Train! But please note that checkpoint path for several models need to be manually specified.
  5. Gather your results with gather_results.py