Present Tuesday: Austin, Gordon, Brieanna, Jaimie

Present Wednesday:

• Sharif will meet with us on Wednesday

• All of this is normalized (w/ Zenith Angle)
• X' = [X / 1 1 1 1 1 1]
• X'.T = [X.T | 1]
• X'*X'.T = [X*X.T | X1]
  • [1.T*X.T | m ]
• (X'*X'.T)/m
• Mean: X1/m
• Correlation(R) : X*X.T/m
• Samples along diagonal of correlation matrix should be similar
  • Like toeplitz matrix
    • Right-hand bottom corner needs to be 1
    • Number of columns (m) in array rows (n)
    • Number of times you are doing prediction
    • Estimate 9-5 (8 hours, every 5 mins, 96 solar irradiance times/day *365 days/year= 35040 solar irradiance data points/year)
    • Diagonals same and symmetrical
• 1's on bottom are for the zenith normalized data
• Do not need for the statistically normalized data

• Types of Prediction:
• Average
• Persistence: Most recent value for prediction (tap=1)

• Python programming help from Sharif tomorrow
• MSE value: D-Y=E(365*96)
• Inner product of E/entries = 2D array
• (Sum |E|^2)^.5
• Root Mean Squared Working this week
  • Training to use same data for RMS
  • Go down with number of taps increasing
    • Take square root of the error
    • (90,000)^.5 = 300
• Weight for taps
  • Positive values
  • Decrease in value
• Train on 2011 and test on 2012
• Train on 2012 and test on 2011
• RMS graphs for the 2011 (Test and train) and 2012 (test and train)
  • Higher on test than training
  • Can become higher though
  • More time then train based on seasons
    • Winter 2011/2012, Spring 2011/2012, Summer 2011/2012, and Fall 2011/2012
• Interested in power solar panels will be making

Contributing authors:

Created by jobatake on 2016/10/26 02:37.