1. Statistical Tools for Solar Resource Forecasting Vivek Vijay IIT Jodhpur Date: 16/12/2013

2. Solar Resource Assessment Types of Data Regression Analysis – Modeling of Cross Sectional Data Statistical Tests Dimensionality Reduction Time Series Forecasting Learning Algorithm - ANN Outline

3. Solar Resource Assessment (SRA) is a characterization of solar irradiance available for energy conversion for a region or specific location over a historical time period of interest. Forecasting solar irradiance is an important first step toward predicting the performance of a solar-energy conversion system and ensuring stable operation of electricity grid. PV plants are fairly linear in their conversion of solar power to electricity, that is, their overall conversion efficiency during operation typically changes less than 20%. On the other hand, assessment of CSP production is more challenging due to the non-linear nature of thermodynamic parameters. Solar Resource Assessment

4. Cross Sectional Data Multiple individuals at the same time Time Series Data Single individuals at multiple points in time Panel or Longitudinal Data Multiple individuals at multiple time periods Types of Data

5. Regression Analysis

6. The accuracy of the estimated models must be judged by statistical indicators, such as Correlation Coefficient Mean Bias Error Root Mean Square Error Percentage Error Coefficient of Determination Statistical Test

7. Dimensionality Reduction

9. Time Series Forecasting

10. Some Measures

11. Artificial Neural Networks – When the data is non-linear in nature, ANN is a good methodology for forecasting. The gradient decent algorithm can be used for updation. Issues How many number of hidden neurons? How many number of hidden layers? Overestimation A Learning Algorithm - ANN

12. Thank You