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forecasting:meeting_minutes_september_13_2016 [2016/09/14 02:49] jobatake |
forecasting:meeting_minutes_september_13_2016 [2021/09/19 21:59] (current) |
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Tuesday, September 13, 2016 | Tuesday, September 13, 2016 | ||
+ | * Produce: | ||
+ | * Well-written code | ||
+ | * New python libraries to pull off of | ||
+ | * Build library of functions other groups can use | ||
+ | * Solar Industry | ||
+ | * Calibrate Data | ||
+ | * Decisions: | ||
+ | * Normalization Method (Zenith Angle?) | ||
+ | * Prediction time (1 hr?) | ||
+ | * Solar Irradiance doesn't need to be sampled too quickly (1-2 mins ok) | ||
* Understanding PV: | * Understanding PV: | ||
* How much in Hawaii | * How much in Hawaii | ||
* Commercial and residential | * Commercial and residential | ||
* Projection of these | * Projection of these | ||
- | * Solar Thermal | + | * Solar Thermal |
- | * Solar Farms | + | * Solar Farms |
- | * Concentrated Solar | + | * Concentrated Solar |
- | * Energy Production | + | * Energy Production |
- | * PV Data Sheets | + | * PV Data Sheets |
- | * 10-15% efficiency w/ lower cost panels | + | * 10-15% efficiency w/ lower cost panels |
- | * 40% efficiency w/ increase in efficiencies, cost, etc. | + | * 40% efficiency w/ increase in efficiencies, cost, etc. |
- | * Breaks for Federal and State and HECO | + | * Breaks for Federal and State and HECO |
- | * Larger contracts (PPA) | + | * Larger contracts (PPA) |
* Forecasting: | * Forecasting: | ||
* Weather and time series prediction | * Weather and time series prediction | ||
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* Depends on normalization (ex. subtract mean of specific time of day/std, or zenith angle) | * Depends on normalization (ex. subtract mean of specific time of day/std, or zenith angle) | ||
* Take **zenith angle**: Time of day and day of year in account | * Take **zenith angle**: Time of day and day of year in account | ||
- | * Depends on first and second order (mean and covariance matrixes) | + | * Window (FIR filters) |
+ | * Straight average of data over time | ||
* Online Methods: | * Online Methods: | ||
* Estimate of parameter weight and update estimate based on new data | * Estimate of parameter weight and update estimate based on new data | ||
* New linear estimate | * New linear estimate | ||
* Recursive Least Squares (RLS) | * Recursive Least Squares (RLS) | ||
+ | * Depends on first and second order (mean and covariance matrixes) | ||
* Least Mean Squares (LMS) | * Least Mean Squares (LMS) | ||
* Won't perform as well | * Won't perform as well | ||
- | + | * Supervised and Unsupervised Learning: | |
- | Thursday, September 15, 2016 | + | * Nearest Neighbor (supervised) |
- | * | + | * Support Vector Machine Kernels () |
+ | * Deep Learning () | ||
+ | * Clustering used for large dimensional data (unsupervised, Lots of factors) | ||
+ | * Estimation (regression) and Detection (classification) | ||
===== Reminders ===== | ===== Reminders ===== | ||
- | * | + | * Weekly REIS Thursday Seminar 4:30-5:30 |