Combined Ultra-Short-Term Photovoltaic Power Prediction Based on CEEMDAN Decomposition and RIME Optimized AM-TCN-BiLSTM

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 134847 - 134847

Published: Feb. 1, 2025

Language: Английский

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Probabilistic daily runoff forecasting in high-altitude cold regions using a hybrid model combining DBO and transformer variants

Journal of Hydrology Regional Studies, Journal Year: 2025, Volume and Issue: 59, P. 102311 - 102311

Published: March 17, 2025

Language: Английский

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A Multi‐Strategy Fusion for Mobile Robot Path Planning via Dung Beetle Optimization

Concurrency and Computation Practice and Experience, Journal Year: 2025, Volume and Issue: 37(9-11)

Published: April 11, 2025

ABSTRACT In recent years, robot path planning has become a critical aspect of autonomous navigation, especially in dynamic and complex environments where robots must operate efficiently safely. One the primary challenges this domain is achieving high convergence efficiency while avoiding local optimal solutions, which can hinder robot's ability to find best possible path. Additionally, ensuring that follows with minimal turns reduced length essential for enhancing operational reducing energy consumption. These even more pronounced high‐dimensional optimization tasks search space vast difficult navigate. article, multi‐strategy fusion enhanced dung beetle algorithm (MIDBO) introduced tackle key planning, such as slow problem optima, so on, MIDBO incorporates several innovations enhance performance robustness. First, Tent chaotic strategy used diversify initial solutions during population initialization, thereby mitigating risk optima improving global capability. Second, penalty term integrated into fitness function penalize excessive turning angles, aiming reduce frequency magnitude turns. This modification results smoother efficient paths lengths. Third, inertia weight adaptively updated by sine‐based mechanism, dynamically balances exploration exploitation, accelerates convergence, enhances stability. To further improve integrates Levy flight mechanism boost capability stealing phase, contributing practical planned robot. A series thorough reproducible experiments are performed using benchmark test functions evaluate comparison leading metaheuristic algorithms. The demonstrate achieves superior outcomes mean lengths 42.1068 44.4755, respectively, significantly outperforms other algorithms including IPSO (47.6244, 55.9375), original DBO ISSA 55.9375). also markedly reduces number average values 10 13.4, compared (11, 16.1), (12, 15.3), 16.4). Besides, consistent confirmed via stability analysis based on square error turn counts across independent trials. For tasks, 8 7 about top rankings 50‐ 100‐dimensional functions, specifically DBO, IPSO, 13, 18, 11 respectively. Therefore, findings validate competitive solution mobile navigation requirements.

Language: Английский

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Performance Evaluation of Photovoltaic Panels in Extreme Environments: A Machine Learning Approach on Horseshoe Island, Antarctica

Sustainability, Journal Year: 2024, Volume and Issue: 17(1), P. 174 - 174

Published: Dec. 29, 2024

Due to the supply problems of fossil-based energy sources, tendency towards alternative sources is relatively high. For this reason, use solar systems increasing today. This study combines experimental data and machine learning algorithms evaluate performance four different photovoltaic (PV) panel designs (monocrystalline, polycrystalline, flexible, transparent) under harsh environmental conditions on Horseshoe Island (Antarctica). In research, effects factors, such as radiation, temperature, humidity, wind speed, panels were analyzed. Electrical power output PV are analyzed using six models. Random forest (RF) CatBoost (CB) models showed highest accuracy reliability among these According results, Monocrystalline provided electrical (20.5 Watts average), Flexible efficiency (19.67%). However, was observed have higher surface temperatures compared other types. Furthermore, resulted in an average reduction 4.1 tons CO2 emissions per year, demonstrating positive impact renewable systems. Thanks study, research for temporary stations Antarctica will focus explainable interpretable artificial intelligence that provide understanding factors affecting panels. The results be important guide optimizing consumption, management, demand forecasting Antarctica.

Language: Английский

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