Enhancing energy efficiency with ai: a review of machine learning models in electricity demand forecasting

AO Aderibigbe, EC Ani, PE Ohenhen… - Engineering Science & …, 2023 - fepbl.com
This study presents a comprehensive review of the impact of artificial intelligence (AI) and
machine learning (ML) on enhancing energy efficiency, particularly in the context of …

[HTML][HTML] Photovoltaic systems operation and maintenance: A review and future directions

H Abdulla, A Sleptchenko, A Nayfeh - Renewable and Sustainable Energy …, 2024 - Elsevier
The expansion of photovoltaic systems emphasizes the crucial requirement for effective
operations and maintenance, drawing insights from advanced maintenance approaches …

A day-ahead photovoltaic power prediction via transfer learning and deep neural networks

SM Miraftabzadeh, CG Colombo, M Longo, F Foiadelli - Forecasting, 2023 - mdpi.com
Climate change and global warming drive many governments and scientists to investigate
new renewable and green energy sources. Special attention is on solar panel technology …

[HTML][HTML] Advanced techniques for wind energy production forecasting: Leveraging multi-layer Perceptron+ Bayesian optimization, ensemble learning, and CNN-LSTM …

SM Malakouti, F Karimi, H Abdollahi, MB Menhaj… - Case Studies in …, 2024 - Elsevier
Due to the shortage of fossil fuels in many countries, power plants that rely on fossil fuels will
be phased out in favor of wind turbines as the primary source of energy generation. These …

[HTML][HTML] The true value of a forecast: Assessing the impact of accuracy on local energy communities

D Putz, M Gumhalter, H Auer - Sustainable Energy, Grids and Networks, 2023 - Elsevier
Energy communities have become a key component of growing smart grids that integrate
distributed renewable energy resources, energy storage technologies, and load …

Day-Ahead Hourly Solar Photovoltaic Output Forecasting Using SARIMAX, Long Short-Term Memory, and Extreme Gradient Boosting: Case of the Philippines

IB Benitez, JA Ibañez, CIIID Lumabad, JM Cañete… - Energies, 2023 - mdpi.com
This study explores the forecasting accuracy of SARIMAX, LSTM, and XGBoost models in
predicting solar PV output using one-year data from three solar PV installations in the …

A machine learning-based electricity consumption forecast and management system for renewable energy communities

M Matos, J Almeida, P Gonçalves, F Baldo, FJ Braz… - Energies, 2024 - mdpi.com
The energy sector is currently undergoing a significant shift, driven by the growing
integration of renewable energy sources and the decentralization of electricity markets …

Day-ahead solar photovoltaic energy forecasting based on weather data using LSTM networks: a comparative study for photovoltaic (PV) panels in Turkey

Z Garip, E Ekinci, A Alan - Electrical Engineering, 2023 - Springer
Photovoltaic (PV) panels are used to generate electricity by using solar energy from the sun.
Although the technical features of the PV panel affect energy production, the weather plays …

Enhanced efficiency in smart grid energy systems through advanced AI-based thermal modeling

BVS Krishna, S Pauline, S Sivakumar… - Thermal Science and …, 2024 - Elsevier
Integrating smart grid (SG) and artificial intelligence (AI) technologies can potentially
enhance energy efficiency and sustainability in sports facilities. This research presents an …

[HTML][HTML] Behavior enabled IoT: A software architecture for self-adapting a renewable energy community

A De Caro, E Zimeo - Internet of Things, 2024 - Elsevier
The availability of large amounts of data generated by a growing number of Internet of
Things devices disseminated in everyday life objects joined with the ability to use data from …