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GECCO 2013 Industrial Competition

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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 / - PowerPoint PPT Presentation
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GECCO 2013 Industrial Competition Computer Engineering Lab, School of Electrical and IT Engineering Farzad Noorian
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Page 1: GECCO 2013 Industrial Competition

GECCO 2013 Industrial Competition

Computer Engineering Lab, School of Electrical and IT EngineeringFarzad Noorian

Page 2: GECCO 2013 Industrial Competition

2

GECCO 2013

› Genetic and Evolutionary Computation Conference- Organized by ACM SIGEVO

› GECCO Industrial challenge:- http://www.spotseven.de/gecco-challenge/

- sponsored by GreenPocket GmbH

Page 3: GECCO 2013 Industrial Competition

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Introduction

› About the Competition

› Pre-processing

› Features

› Training and Cross-validation

› Results

Page 4: GECCO 2013 Industrial Competition

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The Competition

› Real room climate time series- Outside temperature as an additional input

- Irregular time-series

- Very noisy

Page 5: GECCO 2013 Industrial Competition

5

Preprocessing

› From original data

Page 6: GECCO 2013 Industrial Competition

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Preprocessing

› Outliers were removed

Page 7: GECCO 2013 Industrial Competition

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Preprocessing

› A weighted moving average with a small window

Page 8: GECCO 2013 Industrial Competition

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Preprocessing

› Regularized using linear approximation

Page 9: GECCO 2013 Industrial Competition

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Preprocessing

› Only values at hourly boundaries were used.

Page 10: GECCO 2013 Industrial Competition

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Features

› Only the outside temperature was given.

› No outside humidity.

› Human perception based on both.

Page 11: GECCO 2013 Industrial Competition

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Features

› Publicly available data from Weather Underground™ for Köln- Temperature

- Humidity

- Dew Point

Page 12: GECCO 2013 Industrial Competition

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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

Page 13: GECCO 2013 Industrial Competition

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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

Page 14: GECCO 2013 Industrial Competition

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Learner

› Support Vector Machines- With Radial Kernel

› Advantages of SVMs- Efficiently trained

- Unique global optima

Page 15: GECCO 2013 Industrial Competition

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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

Page 16: GECCO 2013 Industrial Competition

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Final Results

› Prediction in hourly, linearly approximated to 10 minutes

Page 17: GECCO 2013 Industrial Competition

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Questions?

› Feel free to email: [email protected]


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