Hybrid solar radiation forecasting model with temporal convolutional network using data decomposition and improved artificial ecosystem-based optimization …
Y Wang, C Zhang, Y Fu, L Suo, S Song, T Peng… - Energy, 2023 - Elsevier
Solar energy is highly economical and widespread in new energy applications, and
analyzing solar radiation information is an important part of solar photovoltaic power …
analyzing solar radiation information is an important part of solar photovoltaic power …
Solar radiation prediction based on convolution neural network and long short-term memory
T Zhu, Y Guo, Z Li, C Wang - Energies, 2021 - mdpi.com
Photovoltaic power generation is highly valued and has developed rapidly throughout the
world. However, the fluctuation of solar irradiance affects the stability of the photovoltaic …
world. However, the fluctuation of solar irradiance affects the stability of the photovoltaic …
An ensemble method to forecast 24-h ahead solar irradiance using wavelet decomposition and BiLSTM deep learning network
In recent years, the penetration of solar power at residential and utility levels has progressed
exponentially. However, due to its stochastic nature, the prediction of solar global horizontal …
exponentially. However, due to its stochastic nature, the prediction of solar global horizontal …
Short-term solar radiation forecasting with a novel image processing-based deep learning approach
In this study, an image processing-based deep learning approach for short-term forecast of
solar radiation has been developed. For this purpose, firstly, cloud movements occurred …
solar radiation has been developed. For this purpose, firstly, cloud movements occurred …
Short‐Term Solar Irradiance Prediction Based on Multichannel LSTM Neural Networks Using Edge‐Based IoT System
M Pi, N Jin, D Chen, B Lou - Wireless Communications and …, 2022 - Wiley Online Library
Most photovoltaic power generation methods use global level irradiance (GHI) as the main
input and output. However, randomness, instability, and intermittency are the main factors …
input and output. However, randomness, instability, and intermittency are the main factors …
Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks
Accurate and reliable solar irradiance forecasting can bring significant benefits for managing
electricity generation and distributing modern smart grid. However, the characteristics of …
electricity generation and distributing modern smart grid. However, the characteristics of …
Short-term solar radiation forecasting using hybrid deep residual learning and gated LSTM recurrent network with differential covariance matrix adaptation evolution …
Developing an accurate and robust prediction of long-term average global solar irradiation
plays a crucial role in industries such as renewable energy, agribusiness, and hydrology …
plays a crucial role in industries such as renewable energy, agribusiness, and hydrology …
Novel multi-time scale deep learning algorithm for solar irradiance forecasting
Solar irradiance forecasting is an inevitable and most significant process in grid-connected
photovoltaic systems. Solar power is highly non-linear, and thus to manage the grid …
photovoltaic systems. Solar power is highly non-linear, and thus to manage the grid …
Hybrid deep neural model for hourly solar irradiance forecasting
Owing to integrating photovoltaic solar systems into power networks, accurate prediction of
solar irradiance plays an increasingly significant role in electric energy planning and …
solar irradiance plays an increasingly significant role in electric energy planning and …
An artificial intelligence-based solar radiation prophesy model for green energy utilization in energy management system
Solar energy's probabilistic and changeable nature raises serious challenges about
ensuring dependable, cheap, and secure control of power energy networks through the …
ensuring dependable, cheap, and secure control of power energy networks through the …