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GECCO 2013 Industrial Competition

GECCO 2013 Industrial Competition. Computer Engineering Lab, School of Electrical and IT Engineering. Farzad Noorian. GECCO 2013. Genetic and Evolutionary Computation Conference Organized by ACM SIGEVO GECCO Industrial challenge: http ://www.spotseven.de/gecco-challenge /

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GECCO 2013 Industrial Competition

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  1. GECCO 2013 Industrial Competition Computer Engineering Lab, School of Electrical and IT Engineering FarzadNoorian

  2. GECCO 2013 • Genetic and Evolutionary Computation Conference • Organized by ACM SIGEVO • GECCO Industrial challenge: • http://www.spotseven.de/gecco-challenge/ • sponsored by GreenPocketGmbH

  3. Introduction • About the Competition • Pre-processing • Features • Training and Cross-validation • Results

  4. The Competition • Real room climate time series • Outside temperature as an additional input • Irregular time-series • Very noisy

  5. Preprocessing • From original data

  6. Preprocessing • Outliers were removed

  7. Preprocessing • A weighted moving average with a small window

  8. Preprocessing • Regularized using linear approximation

  9. Preprocessing • Only values at hourly boundaries were used.

  10. Features • Only the outside temperature was given. • No outside humidity. • Human perception based on both.

  11. Features • Publicly available data from Weather Underground™ for Köln • Temperature • Humidity • Dew Point

  12. Features for Temperature Forecasting • Weekday seasonality → Only weekdays used • Seasonality removed only from indoor temperature • A window of last n hours room temperatures • A window of previous m and next m dew points from Wunderground

  13. Features for Humidity Forecasting • A window of last n hours • m previous and m next external humidity from Wunderground • Open, Low, High and Close of that days humidity • No seasonality or data filtering

  14. Learner • Support Vector Machines • With Radial Kernel • Advantages of SVMs • Efficiently trained • Unique global optima

  15. Cross-validation • Using R package caret • Cross validation for features and parameters • Using from a 4-day window to 15-day window to train • Validating using next 3 available days • Final training on all data

  16. Final Results • Prediction in hourly, linearly approximated to 10 minutes

  17. Questions? • Feel free to email: farzad.noorian@sydney.edu.au

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