Research on the Generation Method Of Missing Hourly Solar Radiation Data Based on Multiple Neural Network Algorithm DOI
Honglian Li,

Xi He,

Yao Hu

et al.

Published: Jan. 1, 2023

Solar radiation is an essential meteorological parameter for building energy efficiency analysis, and the quality of data directly affects analysis results. This paper investigates estimation hourly solar based on generation typical year(TMY) using various real parameters limited data. The focus this to use two types neural network algorithms improve accuracy applicability, solve problem acquisition in non-radiation areas. First, select city station three methods generate TMY. Then, models, BP Neural Network (BP)，Convolutional (CNN) are used estimate verify Finally, by constructing a photovoltaic-integrated office model, model verified consumption simulation photovoltaic (PV) power simulation. results show that can well data, which provides new idea study areas where missing.

Language: Английский

Improving ultra-short-term photovoltaic power forecasting using a novel sky-image-based framework considering spatial-temporal feature interaction DOI
Haixiang Zang, Dianhao Chen, Jingxuan Liu

et al.

Energy, Journal Year: 2024, Volume and Issue: 293, P. 130538 - 130538

Published: Feb. 3, 2024

Language: Английский

Citations

23
A Comprehensive Survey on Aquila Optimizer DOI Open Access
Buddhadev Sasmal, Abdelazim G. Hussien, Arunita Das

et al.

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 30(7), P. 4449 - 4476

Published: June 7, 2023

Language: Английский

Citations

36
A district-scale spatial distribution evaluation method of rooftop solar energy potential based on deep learning DOI
Guannan Li, Zixi Wang,

Chengliang Xu

et al.

Solar Energy, Journal Year: 2023, Volume and Issue: 268, P. 112282 - 112282

Published: Dec. 27, 2023

Language: Английский

Citations

17
Energy processes prediction by a convolutional radial basis function network DOI
José de Jesús Rubio, D.L Quiroz García, Humberto Sossa

et al.

Energy, Journal Year: 2023, Volume and Issue: 284, P. 128470 - 128470

Published: July 25, 2023

Language: Английский

Citations

14
Using the Grey Wolf Aquila Synergistic Algorithm for Design Problems in Structural Engineering DOI Creative Commons
Megha Varshney, Pravesh Kumar, Musrrat Ali

et al.

Biomimetics, Journal Year: 2024, Volume and Issue: 9(1), P. 54 - 54

Published: Jan. 18, 2024

The Aquila Optimizer (AO) is a metaheuristic algorithm that inspired by the hunting behavior of bird. AO approach has been proven to perform effectively on range benchmark optimization issues. However, may suffer from limited exploration ability in specific situations. To increase algorithm, this work offers hybrid employs alpha position Grey Wolf (GWO) drive search process algorithm. At same time, we applied quasi-opposition-based learning (QOBL) strategy each phase This develops quasi-oppositional solutions current solutions. are then utilized direct GWO method also notable for its resistance noise. means it can even when objective function noisy. other hand, be sensitive By integrating into strengthen robustness noise, and hence, improve performance real-world In order evaluate effectiveness technique, was benchmarked 23 well-known test functions CEC2017 compared with popular algorithms. findings demonstrate our proposed excellent efficacy. Finally, five practical engineering issues, results showed technique suitable tough problems uncertain spaces.

Language: Английский

Citations

5
Dynamic Random Walk and Dynamic Opposition Learning for Improving Aquila Optimizer: Solving Constrained Engineering Design Problems DOI Creative Commons
Megha Varshney, Pravesh Kumar, Musrrat Ali

et al.

Biomimetics, Journal Year: 2024, Volume and Issue: 9(4), P. 215 - 215

Published: April 4, 2024

One of the most important tasks in handling real-world global optimization problems is to achieve a balance between exploration and exploitation any nature-inspired method. As result, search agents an algorithm constantly strive investigate unexplored regions space. Aquila Optimizer (AO) recent addition field metaheuristics that finds solution problem using hunting behavior Aquila. However, some cases, AO skips true solutions trapped at sub-optimal solutions. These lead premature convergence (stagnation), which harmful determining optima. Therefore, solve above-mentioned problem, present study aims establish comparatively better synergy escape from local stagnation AO. In this direction, firstly, ability improved by integrating Dynamic Random Walk (DRW), and, secondly, maintained through Oppositional Learning (DOL). Due its dynamic space low complexity, DOL-inspired DRW technique more computationally efficient has higher potential for best optimum. This allows be even further prevents convergence. The proposed named DAO. A well-known set CEC2017 CEC2019 benchmark functions as well three engineering are used performance evaluation. superior DAO demonstrated examination numerical data produced comparison with existing metaheuristic algorithms.

Language: Английский

Citations

5
Short-Term Photovoltaic Power Prediction Based on Multi-Stage Temporal Feature Learning DOI Open Access
Qiang Wang, Hao Cheng,

Wenrui Zhang

et al.

Energy Engineering, Journal Year: 2025, Volume and Issue: 0(0), P. 1 - 10

Published: Jan. 1, 2025

Harnessing solar power is essential for addressing the dual challenges of global warming and depletion traditional energy sources.However, fluctuations intermittency photovoltaic (PV) pose its extensive incorporation into grids.Thus, enhancing precision PV prediction particularly important.Although existing studies have made progress in short-term prediction, issues persist, underutilization temporal features neglect correlations between satellite cloud images data.These factors hinder improvements performance.To overcome these challenges, this paper proposes a novel method based on multi-stage feature learning.First, improved LSTM SA-ConvLSTM are employed to extract spatial-temporal images, respectively.Subsequently, hybrid attention mechanism proposed identify interplay two modalities, capacity focus most relevant features.Finally, Transformer model applied further capture patterns long-term dependencies within multi-modal information.The also compares with various competitive methods.The experimental results demonstrate that outperforms methods terms accuracy reliability prediction.

Language: Английский

Citations

0
Global Horizontal Irradiance Prediction Model Based on Mixed Spatial Information and Aerosol Classification DOI Creative Commons

Xiuyan Gao,

Yujun Hou,

Suning Li

et al.

Energy Science & Engineering, Journal Year: 2025, Volume and Issue: 13(5), P. 2220 - 2230

Published: March 3, 2025

ABSTRACT Reliable and accurate predictions of solar radiation are essential for the supervision operation photovoltaic power generation systems. As primary media involved in atmospheric transfer, aerosols significantly influence global horizontal irradiance (GHI). The composition, shape, number density distribution vary greatly, resulting significant differences their optical properties, which turn affect different ways. This study aims to explore impact types on predicting GHI. First, we expanded data within a fixed region by incorporating spatial information supplement timescale data. Furthermore, used Informer model forecast GHI regions, inputting historical aerosol depth (AOD), meteorological parameters, Finally, an classification classify regions calculated types. findings suggest that impacts predictive performance When continental subcontinental dominated, improved. biomass‐burning dominate, accuracy reduced.

Language: Английский

Citations

0
Multi-Strategy Improved Aquila Optimizer Algorithm and Its Application in Railway Freight Volume Prediction DOI Open Access
Lei Bai, Z. Y. Pei, Jia-Sheng Wang

et al.

Electronics, Journal Year: 2025, Volume and Issue: 14(8), P. 1621 - 1621

Published: April 17, 2025

This study proposes a multi-strategy improved Aquila optimizer (MIAO) to address the key limitations of original (AO). First, phasor operator is introduced eliminate excessive control parameters in X2 phase, transforming it into an adaptive parameter-free process. Second, flow direction enhances X3 phase by improving population diversity and local exploitation. The MIAO algorithm applied optimize Long Short-Term Memory (LSTM) hyperparameters, forming MIAO_LSTM model for monthly railway freight forecasting. Comprehensive evaluations on 15 benchmark functions show MIAO’s superior performance over SOA, PSO, SSA, AO. Using data (2005–2021), achieves lower MAE, MSE, RMSE compared traditional LSTM hybrid models (SSA_LSTM, PSO_LSTM, etc.). Further, Grey Relational Analysis selects high-correlation features (≥0.8) boost accuracy. results validate MIAO_LSTM’s effectiveness practical predictions.

Language: Английский

Citations

0
A Comprehensive Survey of Aquila Optimizer: Theory, Variants, Hybridization, and Applications DOI
Sylia Mekhmoukh Taleb, Elham Tahsin Yasin,

Amylia Ait Saadi

et al.

Archives of Computational Methods in Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: May 7, 2025

Language: Английский

Citations

0