A Bayesian Approach for Modeling and Forecasting Solar Photovoltaic Power Generation.

Area of Science:

• Statistics
• Renewable Energy Modeling
• Machine Learning

Background:

• Accurate solar power forecasting is crucial for grid management.
• Traditional methods may struggle with the inherent variability and complex patterns of solar energy generation.
• Bayesian approaches offer a robust framework for uncertainty quantification in time-series modeling.

Purpose of the Study:

• To develop a Bayesian approach for estimating and forecasting daily solar power generation curves.
• To model the unknown function of solar power generation using a Gaussian-process prior.
• To provide smooth function estimates through interpolation for improved forecasting.

Main Methods:

• Utilizing a Bayesian model with a Gaussian-process prior on daily solar power values.
• Employing a Gibbs sampling algorithm to estimate model parameters due to the lack of a known posterior distribution form.
• Estimating smooth functions by interpolating points from a k-variate normal distribution.

Main Results:

• The proposed Bayesian approach effectively models and forecasts solar power generation curves.
• Simulation studies and real-world data application showed near-zero Mean Absolute Percentage Error (MAPE) and Root-Mean-Square Error (RMSE).
• The method produced smooth function estimates, indicating high accuracy and reliability.

Conclusions:

• The Bayesian method with Gaussian processes is a highly effective tool for solar power curve estimation and forecasting.
• The Gibbs sampling implementation provides accurate parameter estimates for the complex model.
• The approach demonstrates significant potential for improving solar energy management and integration into power grids.