State-of-the-art review on energy and load forecasting in microgrids using artificial neural networks, machine learning, and deep learning techniques
Forecasting renewable energy efficiency significantly impacts system management and
operation because more precise forecasts mean reduced risk and improved stability and …
operation because more precise forecasts mean reduced risk and improved stability and …
[HTML][HTML] Benefits of physical and machine learning hybridization for photovoltaic power forecasting
MJ Mayer - Renewable and sustainable energy reviews, 2022 - Elsevier
Irradiance-to-power conversion is an essential step of state-of-the-art photovoltaic (PV)
power forecasting, regardless of the source and post-processing of irradiance forecasts. The …
power forecasting, regardless of the source and post-processing of irradiance forecasts. The …
COA-CNN-LSTM: Coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications
Power prediction is now a crucial part of contemporary energy management systems, which
is important for the organization and administration of renewable resources. Solar and wind …
is important for the organization and administration of renewable resources. Solar and wind …
[HTML][HTML] Short-term photovoltaic power forecasting using meta-learning and numerical weather prediction independent Long Short-Term Memory models
Short-term photovoltaic (PV) power forecasting is essential for integrating renewable energy
sources into the grid as it provides accurate and timely information on the expected output of …
sources into the grid as it provides accurate and timely information on the expected output of …
Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters
The industrial sector consumes about one-third of global energy, making them a frequent
target for energy use reduction. Variation in energy usage is observed with weather …
target for energy use reduction. Variation in energy usage is observed with weather …
Multi-timescale photovoltaic power forecasting using an improved Stacking ensemble algorithm based LSTM-Informer model
As more and more photovoltaic (PV) systems are integrated into the grid, the intelligent
operation of the grid system is facing significant challenges. Therefore, accurately …
operation of the grid system is facing significant challenges. Therefore, accurately …
Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model
The stability operation and real-time control of the integrated energy system with distributed
energy resources determines the higher and higher requirements for the accuracy of solar …
energy resources determines the higher and higher requirements for the accuracy of solar …
[HTML][HTML] Machine learning for forecasting a photovoltaic (PV) generation system
To mitigate the carbon print of buildings, they should have on-site renewable energy
generation systems to supply energy for the buildings without relying on the national grid …
generation systems to supply energy for the buildings without relying on the national grid …
Capacity optimization and economic analysis of PV–hydrogen hybrid systems with physical solar power curve modeling
Using photovoltaic (PV) power for hydrogen production presents an alluring prospect under
humanity's ongoing pursuit of carbon neutrality by mid of this century. An indispensable …
humanity's ongoing pursuit of carbon neutrality by mid of this century. An indispensable …
Metaheuristic-based hyperparameter tuning for recurrent deep learning: application to the prediction of solar energy generation
As solar energy generation has become more and more important for the economies of
numerous countries in the last couple of decades, it is highly important to build accurate …
numerous countries in the last couple of decades, it is highly important to build accurate …