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http://dx.doi.org/10.5351/KJAS.2019.32.2.319

Prediction of electricity consumption in A hotel using ensemble learning with temperature  

Kim, Jaehwi (Department of Statistics, Duksung Women's University)
Kim, Jaehee (Department of Statistics, Duksung Women's University)
Publication Information
The Korean Journal of Applied Statistics / v.32, no.2, 2019 , pp. 319-330 More about this Journal
Abstract
Forecasting the electricity consumption through analyzing the past electricity consumption a advantageous for energy planing and policy. Machine learning is widely used as a method to predict electricity consumption. Among them, ensemble learning is a method to avoid the overfitting of models and reduce variance to improve prediction accuracy. However, ensemble learning applied to daily data shows the disadvantages of predicting a center value without showing a peak due to the characteristics of ensemble learning. In this study, we overcome the shortcomings of ensemble learning by considering the temperature trend. We compare nine models and propose a model using random forest with the linear trend of temperature.
Keywords
ensemble learning; temperature; bagging; random forest; time series forecast;
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Times Cited By KSCI : 1  (Citation Analysis)
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