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forecasting:meeting_minutes_september_27_2016 [2016/09/28 03:27] jobatake |
forecasting:meeting_minutes_september_27_2016 [2021/09/19 21:59] |
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| - | ====== Forecasting Meeting Minutes for Week of September 27, 2016 ====== | ||
| - | ** Present: Gordon, Brieanna, Jaimie ** | ||
| - | |||
| - | ===== Updates ===== | ||
| - | * More coordination with Seyyed | ||
| - | |||
| - | ===== Expectations ===== | ||
| - | * 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 | ||
| - | |||
| - | ===== 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 Ing coursera | ||
| - | |||
| - | ===== Reminders ===== | ||