predicting solar generation from weather forecasts using machine learning

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


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]


Renewables are Intermittent

Example: Solar shows significant variation

Nearly no energy

How much energy will we harvest today?


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


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)


Problem Statement

How can we statistically predictsolar harvesting ?

Approach: Leverage weather forecast to predict solar energy Use statistical power of machine learning


Outline


Forecast-based Predictions

Idea for using weather forecasts PPF accurate for constant weather Forecasts also predict significant weather

changes


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


Data Analysis

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


Data Analysis

Solar intensity exhibits no correlation with wind speed, but weak correlation with temperature


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


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)


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


SVM with RBF Kernel

Average prediction error: 22 %


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


SVM with RBF Kernel

Reducing dimensions from 7 to 4 reducesprediction error from 22 % to 2 %

4-dimensions

7-dimensions


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]


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


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

predicting solar generation from weather forecasts using machine learning

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