gecco 2013 industrial competition computer engineering lab, school of electrical and it engineering...
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GECCO 2013 Industrial Competition
Computer Engineering Lab, School of Electrical and IT EngineeringFarzad Noorian
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GECCO 2013
› Genetic and Evolutionary Computation Conference
- Organized by ACM SIGEVO
› GECCO Industrial challenge:
- http://www.spotseven.de/gecco-challenge/
- sponsored by GreenPocket GmbH
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Introduction
› About the Competition
› Pre-processing
› Features
› Training and Cross-validation
› Results
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The Competition
› Real room climate time series
- Outside temperature as an additional input
- Irregular time-series
- Very noisy
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Preprocessing
› From original data
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Preprocessing
› Outliers were removed
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Preprocessing
› A weighted moving average with a small window
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Preprocessing
› Regularized using linear approximation
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Preprocessing
› Only values at hourly boundaries were used.
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Features
› Only the outside temperature was given.
› No outside humidity.
› Human perception based on both.
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Features
› Publicly available data from Weather Underground™ for Köln
- Temperature
- Humidity
- Dew Point
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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
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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
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Learner
› Support Vector Machines
- With Radial Kernel
› Advantages of SVMs
- Efficiently trained
- Unique global optima
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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
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Final Results
› Prediction in hourly, linearly approximated to 10 minutes
