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forecasting:meeting_minutes_september_27_2016 [2016/09/28 02:58]
jobatake created
forecasting:meeting_minutes_september_27_2016 [2021/09/19 21:59] (current)
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 ====== Forecasting Meeting Minutes for Week of September 27, 2016 ====== ​ ====== Forecasting Meeting Minutes for Week of September 27, 2016 ====== ​
-** Present: Gordon, Brieanna, Jaimie **+** Present ​Tuesday: Gordon, Brieanna, Jaimie ** 
 + 
 +** Present Thursday: Gordon, Brieanna, Jaimie, Austin ** 
  
 ===== Updates ===== ===== Updates =====
   * More coordination with Seyyed   * More coordination with Seyyed
  
 +
 +===== Meeting with Dr. Kuh =====
 ===== Expectations ===== ===== Expectations =====
   * 1. Well documented code   * 1. Well documented code
-  ​* 2. Learning ​abt signal processing/​machine learning+    * Inputs/​Outputs:​ 
 +      * Mean Squared Error 
 +      * Input 
 +      * Weight of length 
 +  ​* 2. Learning signal processing/​machine learning ​algorithms
   * Reproducing what Masaki did   * Reproducing what Masaki did
     * Visualizing Data w/ 3D plotting:     * Visualizing Data w/ 3D plotting:
       * Contact Masaki       * Contact Masaki
-  * Work on normalized data with Zenith Angle +  * Work on normalized data with __Zenith Angle__ 
-  * Code with forecasting+  * 3. Forecast Solar Irradiance 
 +    * Recursive least squares 
 +    * Least mean squares
   * Machine learning aspect   * Machine learning aspect
-  * 3. Understanding solar data+  * 4. Understanding solar data
     * PV and solar data (non-linear relationship)     * PV and solar data (non-linear relationship)
     * Weather effects sudden changes in weather     * Weather effects sudden changes in weather
       * Reserve or storage energy       * Reserve or storage energy
       * Shift demands       * Shift demands
 +
 +===== Machine Learning =====
 + 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 Ng coursera
 +
 +===== Group Progress =====
 +   * Worked on implementation of Recursive Algorithm ​
 +   * Compared computation time with Regression
 +    * Derived relationship between samples and computation time for algorithms
 +
  
 ===== Reminders ===== ===== Reminders =====
  
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