Prediction of energy production in a building-integrated photovoltaic system using machine learning algorithms-Reference-Cited by-同舟云学术

Prediction of energy production in a building-integrated photovoltaic system using machine learning algorithms

Published:2024 Issue: Volume:236 Page:75-82
ISSN:1877-0509
Container-title:Procedia Computer Science
language:en
Short-container-title:Procedia Computer Science

Author:

ZOUBIR Zineb,ES-SAKALI Niima,ER-RETBY Houda,MGHAZLI Mohamed Oualid

Publisher

Elsevier BV

Reference27 articles.

1. Building energy efficiency improvement using multi-objective optimization for heating and cooling VRF thermostat setpoints;Es-Sakali,2023

2. A review on buildings energy information: Trends, end-uses, fuels and drivers;González-Torres;Energy Reports,2022

3. Energy management and sustainability assessment of renewable energy communities: The Italian context;Cutore;Energy Convers Manag,2023

4. A photovoltaic power prediction approach enhanced by feature engineering and stacked machine learning model;Abdelmoula;Energy Reports,2022

5. Control and Implementation of an Energy Management Strategy for a PV–Wind–Battery Microgrid Based on an Intelligent Prediction Algorithm of Energy Production;Mahjoub;Energies (Basel),2023

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1. Battery energy storage with renewable energy sources integration in unbalanced distribution network considering time of use pricing;Renewable Energy Focus;2024-10

2. Integration of LSTM networks with gradient boosting machines (GBM) for assessing heating and cooling load requirements in building energy efficiency;Energy Exploration & Exploitation;2024-08-02