A review of wind speed and wind power forecasting with deep neural networks

Y Wang, R Zou, F Liu, L Zhang, Q Liu - Applied energy, 2021 - Elsevier
The use of wind power, a pollution-free and renewable form of energy, to generate electricity
has attracted increasing attention. However, intermittent electricity generation resulting from …

Carbon emission prediction models: A review

Y **, A Sharifi, Z Li, S Chen, S Zeng, S Zhao - Science of The Total …, 2024 - Elsevier
Amidst growing concerns over the greenhouse effect, especially its consequential impacts,
establishing effective Carbon Emission Prediction Models (CEPMs) to comprehend and …

Dispersed foraging slime mould algorithm: Continuous and binary variants for global optimization and wrapper-based feature selection

J Hu, W Gui, AA Heidari, Z Cai, G Liang, H Chen… - Knowledge-Based …, 2022 - Elsevier
The slime mould algorithm (SMA) is a logical swarm-based stochastic optimizer that is easy
to understand and has a strong optimization capability. However, the SMA is not suitable for …

Machine learning driven smart electric power systems: Current trends and new perspectives

MS Ibrahim, W Dong, Q Yang - Applied Energy, 2020 - Elsevier
The current power systems are undergoing a rapid transition towards their more active,
flexible, and intelligent counterpart smart grid, which brings about tremendous challenges in …

Mean–variance portfolio optimization using machine learning-based stock price prediction

W Chen, H Zhang, MK Mehlawat, L Jia - Applied soft computing, 2021 - Elsevier
The success of portfolio construction depends primarily on the future performance of stock
markets. Recent developments in machine learning have brought significant opportunities to …

Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization

RM Adnan, RR Mostafa, O Kisi, ZM Yaseen… - Knowledge-Based …, 2021 - Elsevier
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 …

A wind speed forecasting system for the construction of a smart grid with two-stage data processing based on improved ELM and deep learning strategies

J Wang, X Niu, L Zhang, Z Liu, X Huang - Expert systems with applications, 2024 - Elsevier
The operation and scheduling management of smart grids are important aspects, and wind
speed forecasting modules are indispensable in wind power system management …

A convolutional Transformer-based truncated Gaussian density network with data denoising for wind speed forecasting

Y Wang, H Xu, M Song, F Zhang, Y Li, S Zhou, L Zhang - Applied Energy, 2023 - Elsevier
Wind speed forecasting plays an important role in the stable operation of wind energy power
systems. However, accurate and reliable wind speed forecasting faces four challenges: how …

Weather impact on solar farm performance: a comparative analysis of machine learning techniques

A Gopi, P Sharma, K Sudhakar, WK Ngui… - Sustainability, 2022 - mdpi.com
Forecasting the performance and energy yield of photovoltaic (PV) farms is crucial for
establishing the economic sustainability of a newly installed system. The present study aims …

Boosting whale optimization with evolution strategy and Gaussian random walks: An image segmentation method

AG Hussien, AA Heidari, X Ye, G Liang, H Chen… - Engineering with …, 2023 - Springer
Stochastic optimization has been found in many applications, especially for several local
optima problems, because of their ability to explore and exploit various zones of the feature …