Present: Gordon, Brieanna, Jaimie

• Modified Meeting Hours: Tuesday 4:30-7:00 & Thursday 4:30-7:00

Saturday, September 10, 2016 (Proposal Presentations)

Tuesday, September 13, 2016

• Produce:
  • Well-written code
  • New python libraries to pull off of
  • Build library of functions other groups can use
  • Solar Industry
  • Calibrate Data
• Decisions:
  • Normalization Method (Zenith Angle?)
  • Prediction time (1 hr?)
  • Solar Irradiance doesn't need to be sampled too quickly (1-2 mins ok)
• Understanding PV:
  • How much in Hawaii
    • Commercial and residential
    • Projection of these
  • Solar Thermal
  • Solar Farms
  • Concentrated Solar
  • Energy Production
    • PV Data Sheets
    • 10-15% efficiency w/ lower cost panels
    • 40% efficiency w/ increase in efficiencies, cost, etc.
  • Breaks for Federal and State and HECO
    • Larger contracts (PPA)
• Forecasting:
  • Weather and time series prediction
  • Predicting an hour
  • Reasonable with just Least Squares
    • Depends on normalization (ex. subtract mean of specific time of day/std, or zenith angle)
    • Take zenith angle: Time of day and day of year in account
  • Window (FIR filters)
    • Straight average of data over time
• Online Methods:
  • Estimate of parameter weight and update estimate based on new data
  • New linear estimate
  • Recursive Least Squares (RLS)
    • Depends on first and second order (mean and covariance matrixes)
  • Least Mean Squares (LMS)
    • Won't perform as well
• Supervised and Unsupervised Learning:
  • Nearest Neighbor (supervised)
  • Support Vector Machine Kernels ()
  • Deep Learning ()
  • Clustering used for large dimensional data (unsupervised, Lots of factors)
• Estimation (regression) and Detection (classification)

• Weekly REIS Thursday Seminar 4:30-5:30

Contributing authors:

Created by bsundberg on 2016/09/08 04:28.