Hierarchical control for microgrids: a survey on classical and machine learning-based methods
Microgrids create conditions for efficient use of integrated energy systems containing
renewable energy sources. One of the major challenges in the control and operation of …
renewable energy sources. One of the major challenges in the control and operation of …
Application of temporal fusion transformer for day-ahead PV power forecasting
M López Santos, X García-Santiago… - Energies, 2022 - mdpi.com
The energy generated by a solar photovoltaic (PV) system depends on uncontrollable
factors, including weather conditions and solar irradiation, which leads to uncertainty in the …
factors, including weather conditions and solar irradiation, which leads to uncertainty in the …
[HTML][HTML] Comparison of decision tree based ensemble methods for prediction of photovoltaic maximum current
The intermittent nature of the output power of photovoltaic (PV) systems, in addition to the
fast-varying solar irradiance, has prompted the development of fast, accurate, and reliable …
fast-varying solar irradiance, has prompted the development of fast, accurate, and reliable …
AI Applications to Enhance Resilience in Power Systems and Microgrids—A Review
This paper presents an in-depth exploration of the application of Artificial Intelligence (AI) in
enhancing the resilience of microgrids. It begins with an overview of the impact of natural …
enhancing the resilience of microgrids. It begins with an overview of the impact of natural …
[HTML][HTML] Integrating autoencoder and decision tree models for enhanced energy consumption forecasting in microgrids: A meteorological data-driven approach in …
At this time, as the world and nations move to reduce the use of fossil fuels, research is
oriented toward improving the energy consumption of people and buildings. Recent …
oriented toward improving the energy consumption of people and buildings. Recent …
Employing machine learning for advanced gap imputation in solar power generation databases
This research evaluates the application of advanced machine learning algorithms,
specifically Random Forest and Gradient Boosting, for the imputation of missing data in solar …
specifically Random Forest and Gradient Boosting, for the imputation of missing data in solar …
Global solar irradiation modelling and prediction using machine learning models for their potential use in renewable energy applications
Global solar irradiation is an important variable that can be used to determine the suitability
of an area to install solar systems; nevertheless, due to the limitations of requiring …
of an area to install solar systems; nevertheless, due to the limitations of requiring …
Optimal strategy for comfort-based Home Energy Management System considering impact of battery degradation cost model
B Han, Y Zahraoui, M Mubin, S Mekhilef… - Mathematics, 2023 - mdpi.com
With the deployment of renewable energy generation, home energy storage systems
(HESSs), and plug-in electric vehicles (PEVs), home energy management systems (HEMSs) …
(HESSs), and plug-in electric vehicles (PEVs), home energy management systems (HEMSs) …
[HTML][HTML] Comprehensive study of the artificial intelligence applied in renewable energy
In the innovative domain of sustainable and renewable energy, artificial intelligence
incorporation has appeared as a critical stimulant for improving productivity, cutting costs …
incorporation has appeared as a critical stimulant for improving productivity, cutting costs …
Stochastic Energy Management for Battery Storage System-Based Microgrid Considering Different Forecasting Models
As a result of the recent innovations in the deployment of distributed Energy Storage
Systems (ESS) such as Battery Energy Storage Systems (BESS), this technology can play …
Systems (ESS) such as Battery Energy Storage Systems (BESS), this technology can play …