A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization
Integration of photovoltaics into power grids is difficult as solar energy is highly dependent
on climate and geography; often fluctuating erratically. This causes penetrations and voltage …
on climate and geography; often fluctuating erratically. This causes penetrations and voltage …
A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models
The uncertainty associated with modeling and performance prediction of solar photovoltaic
systems could be easily and efficiently solved by artificial intelligence techniques. During the …
systems could be easily and efficiently solved by artificial intelligence techniques. During the …
[HTML][HTML] Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach
An accurate solar energy forecast is of utmost importance to allow a higher level of
integration of renewable energy into the controls of the existing electricity grid. With the …
integration of renewable energy into the controls of the existing electricity grid. With the …
Review of photovoltaic power forecasting
Variability of solar resource poses difficulties in grid management as solar penetration rates
rise continuously. Thus, the task of solar power forecasting becomes crucial to ensure grid …
rise continuously. Thus, the task of solar power forecasting becomes crucial to ensure grid …
Deterministic and probabilistic forecasting of photovoltaic power based on deep convolutional neural network
The penetration of photovoltaic (PV) energy into modern electric power and energy systems
has been gradually increased in recent years due to its benefits of being abundant …
has been gradually increased in recent years due to its benefits of being abundant …
A comparative performance analysis of ANN algorithms for MPPT energy harvesting in solar PV system
In this paper, artificial neural network (ANN) based Levenberg-Marquardt (LM), Bayesian
Regularization (BR) and Scaled Conjugate Gradient (SCG) algorithms are deployed in …
Regularization (BR) and Scaled Conjugate Gradient (SCG) algorithms are deployed in …
Ultra-short-term exogenous forecasting of photovoltaic power production using genetically optimized non-linear auto-regressive recurrent neural networks
Accurate and credible ultra-short-term photovoltaic (PV) power production prediction is very
important in short-term resource planning, electric power dispatching, and operational …
important in short-term resource planning, electric power dispatching, and operational …
Stochastic optimal scheduling of distributed energy resources with renewables considering economic and environmental aspects
M Di Somma, G Graditi, E Heydarian-Forushani… - Renewable energy, 2018 - Elsevier
A distributed energy resource (DER) system is a multi-input and multi-output energy system
consisting of small-scale technologies, which provide electricity and thermal energy close to …
consisting of small-scale technologies, which provide electricity and thermal energy close to …
A comprehensive review: study of artificial intelligence optimization technique applications in a hybrid microgrid at times of fault outbreaks
The use of fossil-fueled power stations to generate electricity has had a damaging effect
over the years, necessitating the need for alternative energy sources. Microgrids consisting …
over the years, necessitating the need for alternative energy sources. Microgrids consisting …
Load peak shaving and power smoothing of a distribution grid with high renewable energy penetration
High penetration of renewable energy poses a significant challenge in operation of power
system. A potential solution for this problem is utilizing Battery Energy Storage System …
system. A potential solution for this problem is utilizing Battery Energy Storage System …