Speakers
Dr
Emil Stefan CHIFU
(Technical University of Cluj-Napoca)Dr
Viorica Rozina CHIFU
(Technical University of Cluj-Napoca)Dr
Cristina Bianca POP
(Technical University of Cluj-Napoca)
Description
This paper comparatively analyzes the performance of two machine learning algorithms (i.e. Random Forests and Gradient Boosting) in the field of forecasting the energy consumption based on historical data. The two algorithms are applied
in order to forecast the energy consumption individually, and then combined together by using a Weighted Average Ensemble Method. The comparison among the achieved experimental results proves that the Weighted Average Ensemble Method
provides more accurate results than each of the two algorithms applied alone.
Authors
Dr
Emil Stefan CHIFU
(Technical University of Cluj-Napoca)
Mrs
Corina CORDEA
(Technical University of Cluj-Napoca)
Dr
Viorica Rozina CHIFU
(Technical University of Cluj-Napoca)
Dr
Cristina Bianca POP
(Technical University of Cluj-Napoca)
Mr
Octav BARSAN
(Technical University of Cluj-Napoca)
