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UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTS ASSACHUSETTS AAMHERST • MHERST • Department of Computer Science Department of Computer Science • 2011 • 2011
Predicting Solar Generation from Weather Forecasts Using
Machine Learning
Navin Sharma, Pranshu Sharma,David Irwin, and Prashant Shenoy
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTS ASSACHUSETTS AAMHERST • MHERST • Department of Computer Science Department of Computer Science • 2011 • 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]
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTS ASSACHUSETTS AAMHERST • MHERST • Department of Computer Science Department of Computer Science • 2011 • 2011
Renewables are Intermittent
Example: Solar shows significant variation
Nearly no energy
How much energy will we harvest today?
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTS ASSACHUSETTS AAMHERST • MHERST • Department of Computer Science Department of Computer Science • 2011 • 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
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTS ASSACHUSETTS AAMHERST • MHERST • Department of Computer Science Department of Computer Science • 2011 • 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)
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTS ASSACHUSETTS AAMHERST • MHERST • Department of Computer Science Department of Computer Science • 2011 • 2011
Problem Statement
How can we statistically predictsolar harvesting ?
Approach: Leverage weather forecast to predict solar energy Use statistical power of machine learning
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTS ASSACHUSETTS AAMHERST • MHERST • Department of Computer Science Department of Computer Science • 2011 • 2011
Outline
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTS ASSACHUSETTS AAMHERST • MHERST • Department of Computer Science Department of Computer Science • 2011 • 2011
Forecast-based Predictions
Idea for using weather forecasts PPF accurate for constant weather Forecasts also predict significant weather
changes
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTS ASSACHUSETTS AAMHERST • MHERST • Department of Computer Science Department of Computer Science • 2011 • 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
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTS ASSACHUSETTS AAMHERST • MHERST • Department of Computer Science Department of Computer Science • 2011 • 2011
Data Analysis
Solar intensity exhibits strong (but not perfect) correlation with sky cover, humidity, and precipitation
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTS ASSACHUSETTS AAMHERST • MHERST • Department of Computer Science Department of Computer Science • 2011 • 2011
Data Analysis
Solar intensity exhibits no correlation with wind speed, but weak correlation with temperature
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTS ASSACHUSETTS AAMHERST • MHERST • Department of Computer Science Department of Computer Science • 2011 • 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
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTS ASSACHUSETTS AAMHERST • MHERST • Department of Computer Science Department of Computer Science • 2011 • 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)
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTS ASSACHUSETTS AAMHERST • MHERST • Department of Computer Science Department of Computer Science • 2011 • 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) = 0.015625 ε (loss function parameter) = 0.001953125
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTS ASSACHUSETTS AAMHERST • MHERST • Department of Computer Science Department of Computer Science • 2011 • 2011
SVM with RBF Kernel
Average prediction error: 22 %
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTS ASSACHUSETTS AAMHERST • MHERST • Department of Computer Science Department of Computer Science • 2011 • 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
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTS ASSACHUSETTS AAMHERST • MHERST • Department of Computer Science Department of Computer Science • 2011 • 2011
SVM with RBF Kernel
Reducing dimensions from 7 to 4 reducesprediction error from 22 % to 2 %
4-dimensions
7-dimensions
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTS ASSACHUSETTS AAMHERST • MHERST • Department of Computer Science Department of Computer Science • 2011 • 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]
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTS ASSACHUSETTS AAMHERST • MHERST • Department of Computer Science Department of Computer Science • 2011 • 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 = 0.0444 * Intensity - 2.65
Accurate to within 2.5 % of actual harvesting
UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTS ASSACHUSETTS AAMHERST • MHERST • Department of Computer Science Department of Computer Science • 2011 • 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