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Predicting Solar Generation from Weather Forecasts Using Machine Learning

Predicting Solar Generation from Weather Forecasts Using Machine Learning. Navin Sharma, Pranshu Sharma, David Irwin , and Prashant Shenoy. Harvesting Examples. Perpetual Sensor Networks Run forever off harvested energy [ EWSN 2009 ] Off-the-grid infrastructure Power cellular towers & ATM

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Predicting Solar Generation from Weather Forecasts Using Machine Learning

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  1. Predicting Solar Generation from Weather Forecasts Using Machine Learning Navin Sharma, Pranshu Sharma, David Irwin, and Prashant Shenoy

  2. Harvesting Examples • Perpetual Sensor Networks • Run forever off harvested energy [EWSN 2009] • Off-the-grid infrastructure • Power cellular towers & ATM • Smart homes and smart cities • Use on-site solar & wind power [BuildSys 2011]

  3. Renewables are Intermittent • Example: Solar shows significant variation Nearly no energy How much energy will we harvest today?

  4. Predictions are Important • Better predictions == Better performance • Examples: • Smart homes [BuildSys 2011] • Reduce utility bill by 2.7X • Eliminate peak power demands • Sensor Network [SECON 2010] • Lexicographical sensor network: increases sensing rate by 60% • Sensor testbeds: serve 70% more requests

  5. Prediction Methods • Existing Prediction Methods • Past Predicts Future (PPF) • Variants of PPF • EWMA [TECS 2007] • WCMA [VITAE 2009] • Past Predicts Future • Accurate for short time scales (seconds to minutes) • Hard to predict at medium time scales (hours to days)

  6. Problem Statement How can we statistically predict solar harvesting ? • Approach: • Leverage weather forecast to predict solar energy • Use statistical power of machine learning

  7. Outline • Motivation • Intuition & Methodology • Prediction Model • Evaluation • Conclusion

  8. Forecast-based Predictions • Idea for using weather forecasts • PPF accurate for constant weather • Forecasts also predict significant weather changes

  9. Methodology • Analyze Weather Data • Forecast data from National Weather Service • Formulate Forecast  Solar Intensity Model • Use machine learning regression techniques • Solar Intensity = F (time, multiple weather parameters) • Derive Solar Intensity  Solar Energy Model • Empirically from our solar panel deployment

  10. Data Analysis Solar intensity exhibits strong (but not perfect) correlation with sky cover, humidity, and precipitation

  11. Data Analysis Solar intensity exhibits no correlation with wind speed, but weak correlation with temperature

  12. Prediction Technique • ML Regression Techniques • Training data set to find regression coefficients • Testing data set to verify the model’s accuracy • Our data set • Training data set: First 8 months of 2010 • Testing data set: Next 2 months of 2010 • What to predict? • Solar intensity at noon • Based on 3-hr weather forecast at 9 AM

  13. Support Vector Machines • Support Vector Machine (SVM) • Used for classification & regression • Independent of input space dimensionality • Resistant to overfitting • Kernel Function • Maps data from low-dimensional input space to high-dimensional feature space • Common Kernels • Linear kernel • Polynomial kernel • Radial Basis Function (RBF)

  14. SVM Regression: Steps • Step 1: Data Preparation • Normalize to zero mean and unit variance • Step 2: Kernel Selection • RBF performs better than linear & polynomial • Grid search to find optimal parameters • Optimal parameters: • cost (soft margin parameter) = 256 • γ (Gaussian function parameter) = 0.015625 • ε (loss function parameter) = 0.001953125

  15. SVM with RBF Kernel Average prediction error: 22 %

  16. Dimensionality Reduction • Redundant Information • Reduces prediction accuracy • Principal Component Analysis (PCA) • Correlated variables  uncorrelated variables • Uncorrelated variables called principal components • Choose first 4 PCs with first 4 (highest) Eigen values

  17. SVM with RBF Kernel 7-dimensions 4-dimensions Reducing dimensions from 7 to 4 reduces prediction error from 22 % to 2 %

  18. Comparison with Cloudy Model Cloudy-forecast: Sky cover based empirical model for solar prediction [SECON 2010] Cloudy-forecast SVM-RBF SVM-RBF with 4 dimensions predicts 27 % better than cloudy-forecast

  19. Intensity  Energy Model • Solar power from solar intensity • Depends on solar panel characteristics • Panel orientation & surrounding environments • Empirically derived for a particular setup • Our solar panel deployment • Kyocera KC65T Solar Panel • Power = 0.0444 * Intensity - 2.65 Accurate to within 2.5 % of actual harvesting

  20. Conclusions • Weather forecasts can improve prediction accuracy • See dramatic weather changes before they occur • Facilitates better planning • ML statistical models work well • Future Work • Design a better kernel function • Hybrid Prediction: use a combination of past & forecast • Apply to wind and wind gust

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