Grid integration challenges and solution strategies for solar PV systems: a review
World leaders and scientists have been putting immense efforts into strengthening energy
security and reducing greenhouse gas (GHG) emissions by meeting growing energy …
security and reducing greenhouse gas (GHG) emissions by meeting growing energy …
Deep learning models for solar irradiance forecasting: A comprehensive review
The growing human population in this modern society hugely depends on the energy to
fulfill their day-to-day needs and activities. Renewable energy sources, especially solar …
fulfill their day-to-day needs and activities. Renewable energy sources, especially solar …
[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 …
A review on renewable energy and electricity requirement forecasting models for smart grid and buildings
The benefits of renewable energy are that it is sustainable and is low in environmental
pollution. Growing load requirement, global warming, and energy crisis need energy …
pollution. Growing load requirement, global warming, and energy crisis need energy …
Taxonomy research of artificial intelligence for deterministic solar power forecasting
With the world-wide deployment of solar energy for a sustainable and renewable future, the
stochastic and volatile nature of solar power pose significant challenges to the reliable …
stochastic and volatile nature of solar power pose significant challenges to the reliable …
Extreme gradient boosting and deep neural network based ensemble learning approach to forecast hourly solar irradiance
Prediction of solar irradiance is an essential requirement for reliable planning and efficient
designing of solar energy systems. Thus, in present work, a new ensemble model, which …
designing of solar energy systems. Thus, in present work, a new ensemble model, which …
Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques
The modernisation of the world has significantly reduced the prime sources of energy such
as coal, diesel and gas. Thus, alternative energy sources based on renewable energy have …
as coal, diesel and gas. Thus, alternative energy sources based on renewable energy have …
Machine learning prediction of compressive strength for phase change materials integrated cementitious composites
Incorporating phase change materials (PCMs) into cementitious composites has recently
attracted paramount interest. While it can enhance thermal characteristics and energy …
attracted paramount interest. While it can enhance thermal characteristics and energy …
A machine learning-based gradient boosting regression approach for wind power production forecasting: A step towards smart grid environments
In the last few years, several countries have accomplished their determined renewable
energy targets to achieve their future energy requirements with the foremost aim to …
energy targets to achieve their future energy requirements with the foremost aim to …
Day-ahead photovoltaic power forecasting approach based on deep convolutional neural networks and meta learning
The outputs of photovoltaic (PV) power are random and uncertain due to the variations of
meteorological elements, which may disturb the safety and stability of power system …
meteorological elements, which may disturb the safety and stability of power system …