Synergistic Sizing and Energy Management Strategy of Combined Offshore Wind with Solar Floating PV System for Green Hydrogen and Electricity Co-Production Using Multi-Objective Dung Beetle Optimization

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104399 - 104399

Published: Feb. 1, 2025

Language: Английский

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A novel ultra-short-term wind power forecasting method based on TCN and Informer models

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 120, P. 109632 - 109632

Published: Sept. 23, 2024

Language: Английский

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Prediction of Dielectric Loss Factor of Wood in Radio Frequency Heating and Drying Based on IPOA-BP Modeling

Forests, Journal Year: 2024, Volume and Issue: 15(7), P. 1187 - 1187

Published: July 9, 2024

In this paper, an Improved Pelican Optimization Algorithm (IPOA) was proposed to optimize a BP neural network model predict the dielectric loss factor of wood in RF heating and drying process. The trained optimized using MATLAB 2022b software, prediction results with POA-BP IPOA-BP models were compared. show that IPOA-optimized is more accurate than traditional model. After IPOA optimization used wood, value increased by 4.3%, MAE decreased 68%, RMSE 67%. provided study under different macroscopic conditions radio frequency can be realized without need for highly costly prolonged experimental studies.

Language: Английский

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Short-term wind power prediction based on IBOA-AdaBoost-RVM

Journal of King Saud University - Science, Journal Year: 2024, Volume and Issue: 36(11), P. 103550 - 103550

Published: Nov. 22, 2024

Language: Английский

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Analysis of different neural network models based on variational modal decomposition and dung beetle optimizer algorithm for the prediction of air-conditioning energy consumption in multifunctional complex large public buildings

Energy and Buildings, Journal Year: 2025, Volume and Issue: 334, P. 115518 - 115518

Published: Feb. 24, 2025

Language: Английский

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Multi-step ahead wind power forecasting based on multi-feature wavelet decomposition and convolution-gated recurrent unit model

Electrical Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: March 12, 2025

Language: Английский

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An intelligent safety assessment system for pump station equipment

Journal of Physics Conference Series, Journal Year: 2025, Volume and Issue: 2993(1), P. 012057 - 012057

Published: April 1, 2025

Abstract As a key water-control project, pumping stations play crucial role in irrigation, drainage, water transfer, urban supply, and sewage discharge. With the station’s long-term operation, equipment will experience varying degrees of degradation even failure. Once failure occurs, it can cause unit to shut down affect life safety pump station operation maintenance staff. Currently, most have established online monitoring systems for equipment. Still, they cannot comprehensively evaluate detect abnormalities advance early warning. This article proposes an intelligent assessment system response this issue. The evaluates by building applications such as analysis warning, health status evaluation, fault diagnosis, remaining prediction components. It ensures safe

Language: Английский

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Enhanced Air Quality Prediction Using a Coupled DVMD Informer-CNN-LSTM Model Optimized with Dung Beetle Algorithm

Entropy, Journal Year: 2024, Volume and Issue: 26(7), P. 534 - 534

Published: June 21, 2024

Accurate prediction of air quality is crucial for assessing the state atmospheric environment, especially considering nonlinearity, volatility, and abrupt changes in data. This paper introduces an index (AQI) model based on Dung Beetle Algorithm (DBO) aimed at overcoming limitations traditional models, such as inadequate access to data features, challenges parameter setting, accuracy constraints. The proposed optimizes parameters Variational Mode Decomposition (VMD) integrates Informer adaptive sequential with Convolutional Neural Network-Long Short Term Memory (CNN-LSTM). Initially, correlation coefficient method utilized identify key impact features from multivariate weather meteorological Subsequently, penalty factors number variational modes VMD are optimized using DBO. develop a variationally constrained decompose sequence. categorized approximate entropy, high-frequency fed into model, while low-frequency CNN-LSTM model. predicted values subsystems then combined reconstructed obtain AQI results. Evaluation actual monitoring Beijing demonstrates that coupling this superior other optimization models. Mean Absolute Error (MAE) decreases by 13.59%, Root-Mean-Square (RMSE) 7.04%, R-square (R

Language: Английский

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Non-stationary GNNCrossformer: Transformer with graph information for non-stationary multivariate Spatio-Temporal wind power data forecasting

Applied Energy, Journal Year: 2024, Volume and Issue: 377, P. 124492 - 124492

Published: Sept. 26, 2024

Language: Английский

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Prediction of Biochar Adsorption of Uranium in Wastewater and Inversion of Key Influencing Parameters Based on Ensemble Learning

Toxics, Journal Year: 2024, Volume and Issue: 12(10), P. 698 - 698

Published: Sept. 26, 2024

With the rapid development of industrialization, problem heavy metal wastewater treatment has become increasingly serious, posing a serious threat to environment and human health. Biochar shows great potential for application in field treatment; however, biochars prepared from different biomass sources experimental conditions have physicochemical properties, resulting differences their adsorption capacity uranium, which limits wide treatment. Therefore, there is an urgent need deeply explore optimize key parameter settings biochar significantly improve its capacity. This paper combines nonlinear mapping capability SCN ensemble learning advantage Adaboost algorithm based on existing data The accuracy model evaluated by metrics such as coefficient determination (R

Language: Английский

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