Uncertainty analysis of photovoltaic power generation system and intelligent coupling prediction

Renewable Energy, Journal Year: 2024, Volume and Issue: 234, P. 121174 - 121174

Published: Aug. 14, 2024

Language: Английский

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Enhancing CO2 emissions prediction in Africa: A novel approach integrating enviroeconomic factors and nature-inspired neural network in the presence of unit root

Renewable Energy, Journal Year: 2024, Volume and Issue: unknown, P. 121561 - 121561

Published: Oct. 1, 2024

Language: Английский

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Carbon Emission Accounting Method for Coal-fired Power Units of Different Coal Types under Peak Shaving Conditions

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135314 - 135314

Published: Feb. 1, 2025

Language: Английский

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Deployment strategy of PV-ESS for industrial and commercial electricity users with consideration of carbon benefits

Journal of Renewable and Sustainable Energy, Journal Year: 2025, Volume and Issue: 17(2)

Published: March 1, 2025

As the global shift away from fossil fuels intensifies, distributed photovoltaics (PV) have emerged as most significant and swiftly expanding renewable energy source accessible to end-users due their convenience in flexible deployment. Coupled with steep decline storage costs, co-deployment of PV systems (PV-ESS) has become a preferred option for electricity users, especially large ones. The PV-ESS investment decision-making model is encountering new obstacles stemming gradual withdrawal governmental subsidies swift transition carbon markets. To address pressing requirement industrial commercial this paper introduces an improved capacity configuration that incorporates benefits into its considerations. First, we constructed cost-benefit analysis users investing PV-ESS. Second, proposed optimization maximizing annual returns objective function. Finally, validate model, conducted case studies across various typical scenarios explore optimal configurations returns. results indicate within existing market framework, achieving return on challenging. However, incorporating can significantly enhance system Specifically, emissions decrease by 23.84% under low price scenario 50.91% high scenario, while net present value increases 67.98% 941.96%, respectively. This study shed fresh insights policy-makers deployment

Language: Английский

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Characteristics of airflow motion and distribution of dust microparticles deposition in the flow field of photovoltaic panels

Renewable Energy, Journal Year: 2025, Volume and Issue: unknown, P. 122811 - 122811

Published: March 1, 2025

Language: Английский

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Assessing the Potential Impact of Aerosol Scenarios for Rooftop PV Regional Deployment

Renewable Energy, Journal Year: 2025, Volume and Issue: unknown, P. 122869 - 122869

Published: March 1, 2025

Language: Английский

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Long-term Power Generation Prediction in Photovoltaics Using Machine Learning-based Models

Romanian Journal of Information Science and Technology, Journal Year: 2025, Volume and Issue: 28(1), P. 39 - 50

Published: March 14, 2025

The research in the field of renewable energy has taken centre stage study reliable and effective photovoltaic (PV) systems. These systems are essential to a future powered by energy, where solar radiation is directly converted into electrical power. However, arrays have limited conversion efficiency. Hence, highly accurate forecasting strategies required mitigate impact this challenge. This focuses on proposing serial algorithms that combine machine learning global optimization solve stochastic problems. Gated Recurrent Unit (GRU) architecture, Support Vector Machine (SVM) for Regression (SVR) models Differential Evolution algorithm (DE) used developing forecast grid power generation across environmental variations. Initially, four GRU-SVR will be trained address prediction seasonal evolution. Afterwards, hybrid approach GRU-SVR-DE strategy defined integrate models, providing robust PV generation. In end, performances predictions analyzed demonstrate accuracy long-term forecasts.

Language: Английский

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Forecasting rooftop photovoltaic solar power using machine learning techniques

Energy Reports, Journal Year: 2025, Volume and Issue: 13, P. 3616 - 3630

Published: March 22, 2025

Language: Английский

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Mapping national-scale photovoltaic power stations using a novel enhanced photovoltaic index and evaluating carbon reduction benefits

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 318, P. 118894 - 118894

Published: Aug. 8, 2024

Language: Английский

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Technological innovation structure and driving factors of China’s photovoltaic industry: based on patent innovation network

International Journal of Low-Carbon Technologies, Journal Year: 2024, Volume and Issue: 19, P. 1596 - 1609

Published: Jan. 1, 2024

Abstract Photovoltaic (PV) industry is a strategic emerging in China, which provides risk resistance and autonomy for energy security by its technology innovation structure. The article conducts comparative study on the technological of PV between China major powers to master structure China’s industry. For this purpose, analyzes relative evolution data above profiles employing social network analysis (SNA). Multiple linear regression was applied analyze driving factors mechanism. results show that: (i) Compared with other economies, characterized hysteresis, rapid advancement, chain bias towards midstream downstream. (ii) connection whole gradually tends be direct diversified, but tightness integral decreasing. (iii) siliceous resource retention biggest force development industry, followed investment intensity research developement (R&D) corresponding Based findings, puts forward countermeasure recommendations.

Language: Английский

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