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A Review of the Main Machine Learning Methods for Predicting Residential Energy Consumption

Tersimpan di:
Main Authors: Alfonso González-Briones, Guillermo Hernández, Tiago Pinto, Zita Vale, Juan M. Corchado
Format: Proceeding
Bahasa: eng
Terbitan: , 2019
Subjects:
Energy Forecasting
Machine Learning
Gradient Boosting
XGBoost
Lasso
Ridge regression
SGDRegressor
MLP
Online Access: https://zenodo.org/record/3786927
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Internet

https://zenodo.org/record/3786927

Lihat Juga

  • Review of the state of the art of machine models for household consumption prediction
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    Terbitan: (2019)
  • Distributed learning of energy contracts negotiation strategies with collaborative reinforcement learning
    oleh: Tiago Pinto, et al.
    Terbitan: (2019)
  • Day-ahead forecasting approach for energy consumption of an office building using support vector machines
    oleh: Aria Jozi, et al.
    Terbitan: (2019)
  • Case-based reasoning using expert systems to determine electricity reduction in residential buildings
    oleh: Ricardo Faia, et al.
    Terbitan: (2018)
  • Ensemble learning for electricity consumption forecasting in office buildings
    oleh: Tiago Pinto, et al.
    Terbitan: (2020)
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