Photovoltaic power forecasting: A hybrid deep learning model incorporating transfer learning strategy
Y Tang, K Yang, S Zhang, Z Zhang - Renewable and Sustainable Energy …, 2022 - Elsevier
Accurate forecasting of photovoltaic power is essential in the integration, operation, and
scheduling of hybrid grid systems. In particular, modeling for newly built photovoltaic sites is …
scheduling of hybrid grid systems. In particular, modeling for newly built photovoltaic sites is …
Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization
Accurate runoff estimation is crucial for optimal reservoir operation and irrigation purposes.
In this study, a novel hybrid method is proposed for monthly runoff prediction in Mangla …
In this study, a novel hybrid method is proposed for monthly runoff prediction in Mangla …
Efficient bootstrap stacking ensemble learning model applied to wind power generation forecasting
MHDM Ribeiro, RG da Silva, SR Moreno… - International Journal of …, 2022 - Elsevier
The use of wind energy plays a vital role in society owing to its economic and environmental
importance. Knowing the wind power generation within a specific time window is useful for …
importance. Knowing the wind power generation within a specific time window is useful for …
Multi-timescale photovoltaic power forecasting using an improved Stacking ensemble algorithm based LSTM-Informer model
As more and more photovoltaic (PV) systems are integrated into the grid, the intelligent
operation of the grid system is facing significant challenges. Therefore, accurately …
operation of the grid system is facing significant challenges. Therefore, accurately …
An evolutionary robust solar radiation prediction model based on WT-CEEMDAN and IASO-optimized outlier robust extreme learning machine
C Zhang, L Hua, C Ji, MS Nazir, T Peng - Applied Energy, 2022 - Elsevier
As a kind of clean energy, solar energy occupies a pivotal position in energy applications.
Accurate and reliable solar radiation prediction is critical to the application of solar energy. In …
Accurate and reliable solar radiation prediction is critical to the application of solar energy. In …
Boosting solar radiation predictions with global climate models, observational predictors and hybrid deep-machine learning algorithms
This paper presents a new hybrid approach for Global Solar Radiation (GSR) prediction
problems, based on deep learning approaches. Predictive models are useful ploys in solar …
problems, based on deep learning approaches. Predictive models are useful ploys in solar …
An improved Wavenet network for multi-step-ahead wind energy forecasting
Y Wang, T Chen, S Zhou, F Zhang, R Zou… - Energy Conversion and …, 2023 - Elsevier
Accurate multi-step-ahead wind speed (WS) and wind power (WP) forecasting are critical to
the scheduling, planning, and maintenance of wind farms. Previous forecasting methods …
the scheduling, planning, and maintenance of wind farms. Previous forecasting methods …
Hybrid convolutional neural network-multilayer perceptron model for solar radiation prediction
Urgent transition from the dependence on fossil fuels towards renewable energies requires
more solar photovoltaic power to be connected to the electricity grids, with reliable supply …
more solar photovoltaic power to be connected to the electricity grids, with reliable supply …
[HTML][HTML] Traffic flow prediction model based on improved variational mode decomposition and error correction
G Li, H Deng, H Yang - Alexandria Engineering Journal, 2023 - Elsevier
With the aggravation of traffic congestion, traffic flow data (TFD) prediction is very important
for traffic managers to control traffic congestion and for traffic participants to plan their trips …
for traffic managers to control traffic congestion and for traffic participants to plan their trips …
A new traffic flow prediction model based on cosine similarity variational mode decomposition, extreme learning machine and iterative error compensation strategy
H Yang, Y Cheng, G Li - Engineering Applications of Artificial Intelligence, 2022 - Elsevier
Traffic flow data (TFD) prediction is a hot research area in intelligent transportation system.
TFD is non-stationary and nonlinear, so it has become a challenge to predict it accurately. In …
TFD is non-stationary and nonlinear, so it has become a challenge to predict it accurately. In …