Differences
This shows you the differences between two versions of the page.
Next revision | Previous revision | ||
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) |
||
---|---|---|---|
Line 1: | Line 1: | ||
====== 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 ===== | ||