Linear random forest to predict energy consumption

Zammarchi, Gianpaolo
2023-01-01

Abstract

Forecasting electricity consumption is a relevant task to ensure that the supply of energy fed into the grid always equals the demand. In this study we compare the performance of random forest and linear random forest in the prediction of daily electricity consumption in Italy. We show that both implementations reach a good performance in this task, with the best results obtained by linear random forest in a model including different features such as lags, difference variables and day - month variables.
2023
Inglese
CLADAG 2023 Book of abstracts and short papers
9788891935632
Pearson Education Resources
ITALIA
Gerda Claeskens, et al.
Pietro Coretto, Giuseppe Giordano, Michele La Rocca, Maria Lucia Parrella, Carla Rampichini
665
668
4
14th Scientific Meeting of the Classification and Data Analysis Group
Esperti anonimi
September 11-13, 2023
Salerno
scientifica
Linear random forest; time series; energy consumption
no
4 Contributo in Atti di Convegno (Proceeding)::4.1 Contributo in Atti di convegno
Zammarchi, Gianpaolo
273
1
4.1 Contributo in Atti di convegno
reserved
info:eu-repo/semantics/conferencePaper
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