1. Machine Learning Techniques for Solar Radiation Modeling
Hamza Amhaz Darragh Punch Chase Rosenthal Connor Winton

2. Artificial Neural Networks (ANNs)
Why ANNs?
• ANNs are useful in predicting the most efficient spatial area of a PV system
(Mellit, et al. 2003.)
• Neural networks can be trained to predict results from examples and “learn” from their
mistakes and successes. (Mellit, et al. 2003.)
• How does a child learn not to touch a boiling pot?

3. Artificial Neural Networks (ANNs)
ANNs and PV Systems
• Mellit, et al. define the PV system capacity as follows:
• 𝐶𝐴 =
η 𝐺 𝐴 𝐺 𝐻
𝐿
• 𝐶𝐴: PV system capacity
• η 𝐺: PV system efficiency
• 𝐴 𝐺: PV system physical area
• 𝐻: Avg. daily solar radiation
• 𝐿: Avg. daily energy consumption
Back-propagation Algorithms

4. Artificial Neural Networks (ANNs)
A: PV system spatial area
C: PV system useful capacity
p: predicted
m: measured
(Mellit et al. 2003.)

5. Support Vector Machines (SVMs) and Radial Basis Functions (RBFs)
Why SVMs?
• SVMs are 27% more accurate when predicting solar radiation using weather forecast
metrics vs. existing forecast models (Sharma, et al. 2011.)
• SVMs are superior to past-predicts-future models (PPF) which cannot predict
weather changes
• SVMs require the right kernel function (“probability-changing” piece of a prob.
density function) and parameters, which brings in…
RBFs
• Sharma et al. used SVMs with RBF kernels because of the RBFs’ sparsity property and
their ability to handle non-linearity in data

6. Support Vector Machines (SVMs) and Radial Basis Functions (RBFs)
(Sharma et al. 2011.)

7. Support Vector Regression (SVR)
Lauret et al. offer an SVM and RBF approach with more emphasis on building an
equation:
𝑘∗ 𝑡 + ℎ =
𝑖=1
𝑛
𝑎𝑖 𝑘 𝑟𝑏𝑓 𝑥𝑖, 𝑥∗ + 𝑏
where 𝑘 𝑟𝑏𝑓 refers to the radial basis covariance function, a special kind of RBF.
Why SVR?
• Results from multi-class SVMs were compared to the results from SVR; SVR results
were overwhelmingly a better representation (Lauret et al. 2015.)

8. Non-Traditional Machine Learning Techniques to Consider
Gaussian Processes (GP)
• Lauret et al. suggest GP as the best annual predictor for Global Horizontal solar
Irradiance (GHI) for the Reunion-Saint Pierre power grid
• Lauret et al. present the following equation to forecast the clear sky index using GP:
𝑘∗ 𝑡 + ℎ =
𝑖=1
𝑛
𝑎𝑖 𝑘 𝑓 𝑥𝑖, 𝑥∗
where 𝑘 𝑓 refers to the squared exponential covariance function.

9. Non-Traditional Machine Learning Techniques to Consider
Numerical Weather Prediction (NWP)
• NWP is not explicitly a machine learning technique.
• Since our atmosphere is a fluid, NWP samples the current state of the fluid.
• NWP then uses fluid dynamics to predict the state of the fluid in the future.
Why NWP?
• Lauret et al. cite papers that suggest that NWP models are the most accurate when
trying to forecast weather conditions 6+ hours into the future.
• A forecast of greater scope such as this is particularly relevant to Intel’s investors
who are seeking information to support the idea of building a long-term solar
farm in Arizona.

10. Works Cited
[1] A. Mellit et al., “Modelling of Sizing the Photovoltaic System Parameters Using
Artificial Neural Network,” 2003 IEEE Control Applications Conf., 2003, pp. 353-357.
[2] N. Sharma et al., “Predicting Solar Generation from Weather Forecasts Using
Machine Learning,” 2011 Smart Grid Communications Int. Conf., Brussels, 2011,
pp. 528-533.
[3] P. Lauret et al. A benchmarking of machine learning techniques for solar radiation
forecasting in an insular context. Solar Energy, Elsevier, 2015, pp.00.