Variational bayesian network with information interpretability filtering for air quality forecasting

XB Jin, ZY Wang, WT Gong, JL Kong, YT Bai, TL Su… - Mathematics, 2023 - mdpi.com
Air quality plays a vital role in people's health, and air quality forecasting can assist in
decision making for government planning and sustainable development. In contrast, it is …

Short-term load forecasting with an improved dynamic decomposition-reconstruction-ensemble approach

D Yang, J Guo, Y Li, S Sun, S Wang - Energy, 2023 - Elsevier
Short-term load forecasting has evolved into an important aspect of power system in safe
operation and rational dispatching. However, given the load series' instability and volatility …

An innovative combined model based on multi-objective optimization approach for forecasting short-term wind speed: A case study in China

J Li, J Wang, H Zhang, Z Li - Renewable Energy, 2022 - Elsevier
Wind speed forecasting plays a crucial role in enhancing the operating efficiency of wind
power systems for generating electric power. Currently, a substantial number of approaches …

Hybrid boosting algorithms and artificial neural network for wind speed prediction

AT Dosdoğru, AÄB İpek - International Journal of Hydrogen Energy, 2022 - Elsevier
Energy sources are an important foundation for national economic growth. The future of
energy sources depend on the energy controls. The reserves of fossil energy have declined …

A novel deep learning-based evolutionary model with potential attention and memory decay-enhancement strategy for short-term wind power point-interval forecasting

ZF Liu, YY Liu, XR Chen, SR Zhang, XF Luo, LL Li… - Applied Energy, 2024 - Elsevier
Wind power generation plays a crucial role in promoting the transformation and
advancement of the power industry and fostering sustainable development in society …

A self-organizing modular neural network based on empirical mode decomposition with sliding window for time series prediction

X Guo, W Li, J Qiao - Applied Soft Computing, 2023 - Elsevier
Time series is mostly with a chaotic nature and non-stationary characteristic in real-word,
which makes it difficult to be modeled and predicted accurately. To solve this problem, we …

DynamicNet: A time-variant ODE network for multi-step wind speed prediction

R Ye, X Li, Y Ye, B Zhang - Neural Networks, 2022 - Elsevier
Wind power is a new type of green energy. Though it is economical to access and gather
such energy, effectively matching the energy with consumers' demand is difficult, because of …

Enhancing the accuracy of wind speed estimation model using an efficient hybrid deep learning algorithm

SK Singh, SK Jha, R Gupta - Sustainable Energy Technologies and …, 2024 - Elsevier
Estimation of wind speed holds great significance for efficient operation of wind farms and
grid stability. Randomness of wind speed poses a great challenge for accurate estimation of …

Short-term probabilistic predictions of wind multi-parameter based on one-dimensional convolutional neural network with attention mechanism and multivariate copula …

X Zhao, M Bai, X Yang, J Liu, D Yu, J Chang - Energy, 2021 - Elsevier
Wind speed forecast can effectively guide power grid to schedule adjustable sources to
smooth wind uncertainty and ensure system stability. But due to the limited regulating range …

Short-term wind speed forecasting based on hybrid MODWT-ARIMA-Markov model

MU Yousuf, I Al-Bahadly, E Avci - IEEE Access, 2021 - ieeexplore.ieee.org
Markov chains (MC) are statistical models used to predict very short to short-term wind
speed accurately. Such models are generally trained with a single moving window …