U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 2011 Predicting Solar Generation from Weather Forecasts Using Machine Learning Navin Sharma, Pranshu Sharma, David Irwin, and Prashant Shenoy

U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 2011 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]

U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 2011 Renewables are Intermittent Example: Solar shows significant variation Nearly no energy How much energy will we harvest today?

U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 2011 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

U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 2011 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)

U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 2011 Problem Statement How can we statistically predict solar harvesting ? Approach: Leverage weather forecast to predict solar energy Use statistical power of machine learning

U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 2011 Outline Motivation Intuition & Methodology Prediction Model Evaluation Conclusion

U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 2011 Forecast-based Predictions Idea for using weather forecasts PPF accurate for constant weather Forecasts also predict significant weather changes

U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 2011 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

U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 2011 Data Analysis Solar intensity exhibits strong (but not perfect) correlation with sky cover, humidity, and precipitation

U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 2011 Data Analysis Solar intensity exhibits no correlation with wind speed, but weak correlation with temperature

U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 2011 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

U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 2011 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)

U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 2011 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) = ε (loss function parameter) =

U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 2011 SVM with RBF Kernel Average prediction error: 22 %

U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 2011 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

U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 2011 SVM with RBF Kernel Reducing dimensions from 7 to 4 reduces prediction error from 22 % to 2 % 4-dimensions7-dimensions

U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 2011 Comparison with Cloudy Model SVM-RBF with 4 dimensions predicts 27 % better than cloudy-forecast SVM-RBFCloudy-forecast Cloudy-forecast: Sky cover based empirical model for solar prediction [SECON 2010]

U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 2011 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 = * Intensity Accurate to within 2.5 % of actual harvesting

U NIVERSITY OF M ASSACHUSETTS A MHERST Department of Computer Science 2011 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