SolarBoost is an advanced boosting method for distributed photovoltaic (DPV) power forecasting, developed by DAMO Academy, Alibaba Group. This repository contains the implementation and experimental code for the SolarBoost algorithm.

• Accurate forecasting for distributed photovoltaic power systems
• Boosting-based methodology for improved prediction accuracy
• Support for multiple datasets including AR, Kalman, and real-world city data
• Comprehensive experimental analysis and benchmarking

To reproduce our experimental results, run exp.py with one of the following options:

1. python exp.py table2 - Reproduces Table 2 (AR grid analysis)
2. python exp.py table3 - Reproduces Table 3 (Aggregate output analysis for AR and Kalman datasets)
3. python exp.py table4 - Reproduces Table 4 (City A dataset analysis)
4. python exp.py figure9 - Generates capacity plots for Figure 9

• ./data/ - Contains the datasets
  • 0.2.npz - Real-world data from City A
• ./models/ - Stores trained models
• ./figures/ - Output directory for generated figures
• ./tables/ - Output directory for result tables

Models are automatically trained if they don't exist for the specified parameters. Configuration parameters can be found in:

• ar1.py - AR model parameters
• kalman.py - Kalman filter parameters
• city_a.py - City A dataset parameters