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forecasting:meeting_minutes_september_27_2016 [2016/09/28 03:28] jobatake [Machine Learning] |
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 ===== | ||