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Seasonal solar irradiance forecasting using artificial intelligence techniques with uncertainty analysis.

V Gayathry1, Deepa Kaliyaperumal2, Surender Reddy Salkuti3

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This summary is machine-generated.

Accurate solar irradiance forecasting is vital for renewable energy integration. This study used artificial intelligence (AI) to combine point and interval forecasts, improving grid reliability by quantifying prediction uncertainty.

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Area of Science:

  • Renewable Energy Systems
  • Artificial Intelligence in Energy
  • Grid Integration

Background:

  • Renewable energy integration into utility grids is essential for optimizing energy consumption.
  • Accurate forecasting of renewable energy generation is critical for grid planning and stability.
  • Existing research often focuses on point forecasts, neglecting forecast uncertainty and variability.

Purpose of the Study:

  • To develop and evaluate artificial intelligence (AI) techniques for solar irradiance forecasting.
  • To combine point forecasts with interval forecasts to provide comprehensive uncertainty information.
  • To enhance the reliability of renewable energy integration through improved forecasting methods.

Main Methods:

  • Solar irradiance forecasting using Seasonal Auto-Regressive Moving Average with Exogenous factors (SARIMAX), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM) techniques.
  • Performance evaluation of forecasting models using R-squared values across different seasons (winter, summer, monsoon, post-monsoon).
  • Forecast error distribution analysis using Laplace distribution fitting and uncertainty assessment via confidence intervals and coverage rates.

Main Results:

  • The Support Vector Regression (SVR) model demonstrated superior performance, achieving R-squared values of 0.97 (winter), 0.96 (summer), and 0.85 (monsoon and post-monsoon).
  • Laplace distribution fitting for SVR forecast errors proved effective, with excellent coverage rates obtained for various confidence levels across all seasons.
  • An 85% confidence band yielded coverage rates of 89% (winter), 95% (summer), 90% (monsoon), and 88% (post-monsoon).

Conclusions:

  • The study highlights the significance of incorporating forecast error distribution studies and modeling for reliable interval forecasting.
  • Combining point and interval forecasts provides a more comprehensive understanding of prediction uncertainty, crucial for grid management.
  • The developed approach using SVR and Laplace distribution enhances system reliability by providing dependable solar irradiance forecasts.