GECCO 2013 Industrial Competition

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