Optimal capacity and cost analysis of hybrid energy storage system in standalone DC microgrid

T Boonraksa, W Pinthurat, P Wongdet… - IEEE …, 2023 - ieeexplore.ieee.org
DC microgrid systems have been increasingly employed in recent years to address the need
for reducing fossil fuel use in electricity generation. Distributed generations (DGs), primarily …

Fortify the investment performance of crude oil market by integrating sentiment analysis and an interval-based trading strategy

K Yang, Z Cheng, M Li, S Wang, Y Wei - Applied Energy, 2024 - Elsevier
To mitigate the impact of market uncertainty on trading investments, this paper proposes a
forecasting and investing framework for crude oil market by integrating interval models and …

RNN-AFOX: adaptive FOX-inspired-based technique for automated tuning of recurrent neural network hyper-parameters

H ALRahhal, R Jamous - Artificial Intelligence Review, 2023 - Springer
The energy markets, particularly oil and gas, have been significantly affected by the
outbreak of the COVID-19 pandemic in terms of price and availability. In addition to the …

Utilizing heuristic strategies for predicting the backbreak occurrences in open-pit mines, Gol Gohar Mine, Iran

P Sorabi, M Ataei, MRA Jazi, H Dehghani, J Shakeri… - Soft Computing, 2024 - Springer
Backbreak (BB) is a detrimental outcome of blasting activities in mineral extraction
processes within mines. It involves the development of fractures and cracks at considerable …

The optimal interval combination prediction model based on vectorial angle cosine and a new aggregation operator for social security level prediction

K Peng, C Kang, X Ru, L Zhou - Journal of Forecasting, 2024 - Wiley Online Library
This paper puts forwards the induced generalized ordered weighted multiple continuous
ordered weighted geometric averaging (IGOWMC‐OWGA) operator, which overcomes the …

Forecasting Crude Oil Prices using a Hybrid Model Combining Long Short-Term Memory Neural Networks and Markov Switching Model

V Shahbazbegian, H Hosseininesaz… - … on Future Energy …, 2023 - ieeexplore.ieee.org
Given the significant impact of crude oil prices on the global economy, accurately predicting
their fluctuations is essential for effective decision-making in the energy sector. Therefore …

Topology Approach for Crude Oil Price Forecasting of Particle Swarm Optimization and Long Short-Term Memory.

M Yusoff, D Ehsan, MY Sharif… - … Journal of Advanced …, 2024 - search.ebscohost.com
Forecasting crude oil prices hold significant importance in finance, energy, and economics,
given its extensive impact on worldwide markets and socio-economic equilibrium. Using …

Predicting successful trading in the West Texas Intermediate crude oil cash market with machine learning nature-inspired swarm-based approaches

EZ Bojnourdi, A Mansoori, S Jowkar… - Frontiers in Applied …, 2024 - frontiersin.org
The subject of predicting global crude oil prices is well recognized in academic circles. The
notion of hybrid modeling suggests that the integration of several methodologies has the …

Prediction of the Axial Bearing Compressive Capacities of CFST Columns Based on Machine Learning Methods

Y Lusong, Z Yuxing, W Li, P Qiren, W Yiyang - International Journal of …, 2024 - Springer
Concrete-filled steel tubes (CFSTs) are widely used in engineering structures due to their
excellent mechanical properties and economic benefits. This study focused on the …

Application of Artificial Intelligence Techniques for Predicting the Back-break in Blasting Operation

P Sorabi, M Ataei, MRA Jazi, H Dehghani, J Shakeri… - 2023 - researchsquare.com
One of the adverse consequences of the blasting in the mineral extraction process in mines
is back-break (BB) so that development of many fractures and cracks at large distances …