25 years of particle swarm optimization: Flourishing voyage of two decades
From the past few decades many nature inspired algorithms have been developed and
gaining more popularity because of their effectiveness in solving problems of distinct …
gaining more popularity because of their effectiveness in solving problems of distinct …
A review of deep learning models for time series prediction
Z Han, J Zhao, H Leung, KF Ma… - IEEE Sensors Journal, 2019 - ieeexplore.ieee.org
In order to approximate the underlying process of temporal data, time series prediction has
been a hot research topic for decades. Developing predictive models plays an important role …
been a hot research topic for decades. Developing predictive models plays an important role …
A multi-factor driven spatiotemporal wind power prediction model based on ensemble deep graph attention reinforcement learning networks
Y Chengqing, Y Guangxi, Y Chengming, Z Yu, M Xiwei - Energy, 2023 - Elsevier
Spatiotemporal wind power prediction technology could provide technical support for wind
farm energy regulation and dynamic planning. In the paper, a novel ensemble deep graph …
farm energy regulation and dynamic planning. In the paper, a novel ensemble deep graph …
Interval forecasting for urban water demand using PSO optimized KDE distribution and LSTM neural networks
B Du, S Huang, J Guo, H Tang, L Wang, S Zhou - Applied Soft Computing, 2022 - Elsevier
The current literature on water demand forecasting mostly focuses on giving accurate point
predictions of water demand. However, the water demand point forecasting will encounter …
predictions of water demand. However, the water demand point forecasting will encounter …
[HTML][HTML] Relation between prognostics predictor evaluation metrics and local interpretability SHAP values
Maintenance decisions in domains such as aeronautics are becoming increasingly
dependent on being able to predict the failure of components and systems. When data …
dependent on being able to predict the failure of components and systems. When data …
Wind speed forecasting based on variational mode decomposition and improved echo state network
Accurate wind speed forecasting is conducive to power system operation, peak regulation,
security analysis, and energy trading. This study proposes a hybrid model named VMD-DE …
security analysis, and energy trading. This study proposes a hybrid model named VMD-DE …
Comprehensive taxonomies of nature-and bio-inspired optimization: Inspiration versus algorithmic behavior, critical analysis recommendations
In recent algorithmic family simulates different biological processes observed in Nature in
order to efficiently address complex optimization problems. In the last years the number of …
order to efficiently address complex optimization problems. In the last years the number of …
Dynamic ensemble deep echo state network for significant wave height forecasting
Forecasts of the wave heights can assist in the data-driven control of wave energy systems.
However, the dynamic properties and extreme fluctuations of the historical observations …
However, the dynamic properties and extreme fluctuations of the historical observations …
Hybrid structures in time series modeling and forecasting: A review
Z Hajirahimi, M Khashei - Engineering Applications of Artificial Intelligence, 2019 - Elsevier
The key factor in selecting appropriate forecasting model is accuracy. Given the deficiencies
of single models in processing various patterns and relationships latent in data, hybrid …
of single models in processing various patterns and relationships latent in data, hybrid …
Two-phase deep learning model for short-term wind direction forecasting
Z Tang, G Zhao, T Ouyang - Renewable Energy, 2021 - Elsevier
Accurate and reliable wind direction prediction is important for improving wind power
conversion efficiency and operation safety. In this paper, a two-phase deep learning model …
conversion efficiency and operation safety. In this paper, a two-phase deep learning model …