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  • Open Access

    REVIEW

    Research Progress of Photovoltaic Power Prediction Technology Based on Artificial Intelligence Methods

    Daixuan Zhou1, Yujin Liu1, Xu Wang2, Fuxing Wang1, Yan Jia2,*

    Energy Engineering, Vol.121, No.12, pp. 3573-3616, 2024, DOI:10.32604/ee.2024.055853 - 22 November 2024

    Abstract With the increasing proportion of renewable energy in China’s energy structure, among which photovoltaic power generation is also developing rapidly. As the photovoltaic (PV) power output is highly unstable and subject to a variety of factors, it brings great challenges to the stable operation and dispatch of the power grid. Therefore, accurate short-term PV power prediction is of great significance to ensure the safe grid connection of PV energy. Currently, the short-term prediction of PV power has received extensive attention and research, but the accuracy and precision of the prediction have to be further improved. More > Graphic Abstract

    Research Progress of Photovoltaic Power Prediction Technology Based on Artificial Intelligence Methods

  • Open Access

    ARTICLE

    Impact of Different Rooftop Coverings on Photovoltaic Panel Temperature

    Aws Al-Akam1,*, Ahmed A. Abduljabbar2, Ali Jaber Abdulhamed1

    Energy Engineering, Vol.121, No.12, pp. 3761-3777, 2024, DOI:10.32604/ee.2024.055198 - 22 November 2024

    Abstract Photovoltaic (PV) panels are essential to the global transition towards sustainable energy, offering a clean, renewable source that reduces reliance on fossil fuels and mitigates climate change. High temperatures can significantly affect the performance of photovoltaic (PV) panels by reducing their efficiency and power output. This paper explores the consequential effect of various rooftop coverings on the thermal performance of photovoltaic (PV) panels. It investigates the relationship between the type of rooftop covering materials and the efficiency of PV panels, considering the thermal performance and its implications for enhancing their overall performance and sustainability. The… More >

  • Open Access

    ARTICLE

    Photovoltaic Power Generation Power Prediction under Major Extreme Weather Based on VMD-KELM

    Yuxuan Zhao1,2,*, Bo Wang1, Shu Wang1, Wenjun Xu2, Gang Ma2

    Energy Engineering, Vol.121, No.12, pp. 3711-3733, 2024, DOI:10.32604/ee.2024.054032 - 22 November 2024

    Abstract The output of photovoltaic power stations is significantly affected by environmental factors, leading to intermittent and fluctuating power generation. With the increasing frequency of extreme weather events due to global warming, photovoltaic power stations may experience drastic reductions in power generation or even complete shutdowns during such conditions. The integration of these stations on a large scale into the power grid could potentially pose challenges to system stability. To address this issue, in this study, we propose a network architecture based on VMD-KELM for predicting the power output of photovoltaic power plants during severe weather… More >

  • Open Access

    ARTICLE

    A Combined Method of Temporal Convolutional Mechanism and Wavelet Decomposition for State Estimation of Photovoltaic Power Plants

    Shaoxiong Wu1, Ruoxin Li1, Xiaofeng Tao1, Hailong Wu1,*, Ping Miao1, Yang Lu1, Yanyan Lu1, Qi Liu2, Li Pan2

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3063-3077, 2024, DOI:10.32604/cmc.2024.055381 - 18 November 2024

    Abstract Time series prediction has always been an important problem in the field of machine learning. Among them, power load forecasting plays a crucial role in identifying the behavior of photovoltaic power plants and regulating their control strategies. Traditional power load forecasting often has poor feature extraction performance for long time series. In this paper, a new deep learning framework Residual Stacked Temporal Long Short-Term Memory (RST-LSTM) is proposed, which combines wavelet decomposition and time convolutional memory network to solve the problem of feature extraction for long sequences. The network framework of RST-LSTM consists of two More >

  • Open Access

    ARTICLE

    A Novel Integrated Photovoltaic System with a Three-Dimensional Pulsating Heat Pipe

    Mahyar Kargaran*, Hamid Reza Goshayeshi, Ali Reza Alizadeh Jajarm

    Frontiers in Heat and Mass Transfer, Vol.22, No.5, pp. 1461-1476, 2024, DOI:10.32604/fhmt.2024.056284 - 30 October 2024

    Abstract Solar energy is a valuable renewable energy source, and photovoltaic (PV) systems are a practical approach to harnessing this energy. Nevertheless, low energy efficiency is considered a major setback of the system. Moreover, high cell temperature and reflection of solar irradiance from the panel are considered chief culprits in this regard. Employing pulsating heat pipes (PHPs) is an innovative and useful approach to improving solar panel performance. This study presents the results of the power performance of a PV panel attached to a newly designed spiral pulsating heat pipe, while graphene oxide nanofluid with three More >

  • Open Access

    ARTICLE

    Multi-Lever Early Warning for Wind and Photovoltaic Power Ramp Events Based on Neural Network and Fuzzy Logic

    Huan Ma1, Linlin Ma2, Zengwei Wang3,*, Zhendong Li3, Yuanzhen Zhu1, Yutian Liu3

    Energy Engineering, Vol.121, No.11, pp. 3133-3160, 2024, DOI:10.32604/ee.2024.055051 - 21 October 2024

    Abstract With the increasing penetration of renewable energy in power system, renewable energy power ramp events (REPREs), dominated by wind power and photovoltaic power, pose significant threats to the secure and stable operation of power systems. This paper presents an early warning method for REPREs based on long short-term memory (LSTM) network and fuzzy logic. First, the warning levels of REPREs are defined by assessing the control costs of various power control measures. Then, the next 4-h power support capability of external grid is estimated by a tie line power prediction model, which is constructed based More > Graphic Abstract

    Multi-Lever Early Warning for Wind and Photovoltaic Power Ramp Events Based on Neural Network and Fuzzy Logic

  • Open Access

    ARTICLE

    Short-Term Prediction of Photovoltaic Power Based on DBSCAN-SVM Data Cleaning and PSO-LSTM Model

    Yujin Liu1, Zhenkai Zhang1, Li Ma1, Yan Jia1,2,*, Weihua Yin3, Zhifeng Liu3

    Energy Engineering, Vol.121, No.10, pp. 3019-3035, 2024, DOI:10.32604/ee.2024.052594 - 11 September 2024

    Abstract Accurate short-term photovoltaic (PV) power prediction helps to improve the economic efficiency of power stations and is of great significance to the arrangement of grid scheduling plans. In order to improve the accuracy of PV power prediction further, this paper proposes a data cleaning method combining density clustering and support vector machine. It constructs a short-term PV power prediction model based on particle swarm optimization (PSO) optimized Long Short-Term Memory (LSTM) network. Firstly, the input features are determined using Pearson’s correlation coefficient. The feature information is clustered using density-based spatial clustering of applications with noise More >

  • Open Access

    ARTICLE

    A Distributed Photovoltaics Ordering Grid-Connected Method for Analyzing Voltage Impact in Radial Distribution Networks

    Cuiping Li1, Kunqi Gao1, Can Chen2, Junhui Li1,*, Xiaoxiao Wang2, Yinchi Shao2, Xingxu Zhu1

    Energy Engineering, Vol.121, No.10, pp. 2937-2959, 2024, DOI:10.32604/ee.2024.052167 - 11 September 2024

    Abstract In recent years, distributed photovoltaics (DPV) has ushered in a good development situation due to the advantages of pollution-free power generation, full utilization of the ground or roof of the installation site, and balancing a large number of loads nearby. However, under the background of a large-scale DPV grid-connected to the county distribution network, an effective analysis method is needed to analyze its impact on the voltage of the distribution network in the early development stage of DPV. Therefore, a DPV orderly grid-connected method based on photovoltaics grid-connected order degree (PGOD) is proposed. This method… More >

  • Open Access

    ARTICLE

    Stackelberg Game-Based Optimal Dispatch for PEDF Park and Power Grid Interaction under Multiple Incentive Mechanisms

    Weidong Chen1,2,*, Yun Zhao3, Xiaorui Wu1,2, Ziwen Cai3, Min Guo1,2, Yuxin Lu3

    Energy Engineering, Vol.121, No.10, pp. 3075-3093, 2024, DOI:10.32604/ee.2024.051404 - 11 September 2024

    Abstract The integration of photovoltaic, energy storage, direct current, and flexible load (PEDF) technologies in building power systems is an important means to address the energy crisis and promote the development of green buildings. The friendly interaction between the PEDF systems and the power grid can promote the utilization of renewable energy and enhance the stability of the power grid. For this purpose, this work introduces a framework of multiple incentive mechanisms for a PEDF park, a building energy system that implements PEDF technologies. The incentive mechanisms proposed in this paper include both economic and noneconomic… More >

  • Open Access

    ARTICLE

    Reducing Condensation Inside the Photovoltaic (PV) Inverter according to the Effect of Diffusion as a Process of Vapor Transport

    Amal El Berry, Marwa M. Ibrahim*, A. A. Elfeky, Mohamed F. Nasr

    Frontiers in Heat and Mass Transfer, Vol.22, No.4, pp. 1189-1207, 2024, DOI:10.32604/fhmt.2024.050684 - 30 August 2024

    Abstract A photovoltaic (PV) inverter is a vital component of a photovoltaic (PV) solar system. Photovoltaic (PV) inverter failure can mean a solar system that is no longer functioning. When electronic devices such as photovoltaic (PV) inverter devices are subjected to vapor condensation, a risk could occur. Given the amount of moisture in the air, saturation occurs when the temperature drops to the dew point, and condensation may form on surfaces. Numerical simulation with “COMSOL Software” is important for obtaining knowledge relevant to preventing condensation by using two steps. At first, the assumption was that the… More >

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