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 …
A review on global solar radiation prediction with machine learning models in a comprehensive perspective
Y Zhou, Y Liu, D Wang, X Liu, Y Wang - Energy Conversion and …, 2021 - Elsevier
Global solar radiation information is the basis for many solar energy utilizations as well as
for economic and environmental considerations. However, because solar-radiation …
for economic and environmental considerations. However, because solar-radiation …
Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks
Streamflow (Q flow) prediction is one of the essential steps for the reliable and robust water
resources planning and management. It is highly vital for hydropower operation, agricultural …
resources planning and management. It is highly vital for hydropower operation, agricultural …
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 …
How solar radiation forecasting impacts the utilization of solar energy: A critical review
The demand for energy generation from solar energy resource has been exponentially
increasing in recent years. It is integral for a grid operator to maintain the balance between …
increasing in recent years. It is integral for a grid operator to maintain the balance between …
Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms
This paper designs a hybridized deep learning framework that integrates the Convolutional
Neural Network for pattern recognition with the Long Short-Term Memory Network for half …
Neural Network for pattern recognition with the Long Short-Term Memory Network for half …
Solar power forecasting using CNN-LSTM hybrid model
Photovoltaic (PV) technology converts solar energy into electrical energy, and the PV
industry is an essential renewable energy industry. However, the amount of power …
industry is an essential renewable energy industry. However, the amount of power …
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 …
Estimation of SPEI meteorological drought using machine learning algorithms
Accurate estimation of drought events is vital for the mitigation of their adverse
consequences on water resources, agriculture and ecosystems. Machine learning …
consequences on water resources, agriculture and ecosystems. Machine learning …