Forecasting Meeting Minutes for Week of September 27, 2016

Present: Gordon, Brieanna, Jaimie

• More coordination with Seyyed

• 1. Well documented code
  • Inputs/Outputs:
    • Mean Squared Error
    • Input
    • Weight of length _ * 2. Learning signal processing/machine learning algorithms * Reproducing what Masaki did * Visualizing Data w/ 3D plotting: * Contact Masaki * Work on normalized data with Zenith Angle__
• 3. Forecast Solar Irradiance
  • Recursive least squares
  • Least mean squares
• Machine learning aspect
• 4. Understanding solar data
  • PV and solar data (non-linear relationship)
  • Weather effects sudden changes in weather
    • Reserve or storage energy
    • Shift demands

Know linear, unsupervised and classification * 2 types of algorithms:

• Regression/Estimation
• Classification (binary/descrete)/Detection
• Classification: Iris Problem
  • Linear threshold function: Hyperplane separating data (0 | 1)
    • 2 items from 1 item = possible

* Unsupervised Learning:

• Cluster
• PCA

* Support Vector Machines:

• Non-linear

* Multi-learning networks:

• Deep learning

* Andrew Ing coursera