A survey of machine learning models in renewable energy predictions
The use of renewable energy to reduce the effects of climate change and global warming
has become an increasing trend. In order to improve the prediction ability of renewable …
has become an increasing trend. In order to improve the prediction ability of renewable …
A review of the applications of artificial intelligence in renewable energy systems: An approach-based study
Recent advancements in data science and artificial intelligence, as well as the development
of clean and sustainable energy sources, have created numerous opportunities for energy …
of clean and sustainable energy sources, have created numerous opportunities for energy …
A comprehensive review on ensemble solar power forecasting algorithms
With increasing demand for energy, the penetration of alternative sources such as
renewable energy in power grids has increased. Solar energy is one of the most common …
renewable energy in power grids has increased. Solar energy is one of the most common …
High-temperature molten-salt thermal energy storage and advanced-Ultra-supercritical power cycles
A Boretti, S Castelletto - Journal of Energy Storage, 2021 - Elsevier
The work explores the opportunities offered by higher temperature heat transfer/heat storage
fluids, and higher temperature power cycles, in higher concentration solar thermal power …
fluids, and higher temperature power cycles, in higher concentration solar thermal power …
Inverter efficiency analysis model based on solar power estimation using solar radiation
The photovoltaic (PV) industry is an important part of the renewable energy industry. With
the growing use of PV systems, interest in their operation and maintenance (O&M) is …
the growing use of PV systems, interest in their operation and maintenance (O&M) is …
Multi-step solar irradiance forecasting and domain adaptation of deep neural networks
The problem of forecasting hourly solar irradiance over a multi-step horizon is dealt with by
using three kinds of predictor structures. Two approaches are introduced: Multi-Model (MM) …
using three kinds of predictor structures. Two approaches are introduced: Multi-Model (MM) …
[HTML][HTML] The history and practice of AI in the environmental sciences
Artificial intelligence (AI) and machine learning (ML) have become important tools for
environmental scientists and engineers, both in research and in applications. Although …
environmental scientists and engineers, both in research and in applications. Although …
Combining artificial intelligence with physics-based methods for probabilistic renewable energy forecasting
A modern renewable energy forecasting system blends physical models with artificial
intelligence to aid in system operation and grid integration. This paper describes such a …
intelligence to aid in system operation and grid integration. This paper describes such a …
Artificial intelligence for management of variable renewable energy systems: a review of current status and future directions
This review paper provides a summary of methods in which artificial intelligence (AI)
techniques have been applied in the management of variable renewable energy (VRE) …
techniques have been applied in the management of variable renewable energy (VRE) …
A cloud-based Bi-directional LSTM approach to grid-connected solar PV energy forecasting for multi-energy systems
The drive for smarter, greener, and more livable cities has led to research towards more
effective solar energy forecasting techniques and their integration into traditional power …
effective solar energy forecasting techniques and their integration into traditional power …