Photovoltaic systems operation and maintenance: A review and future directions

Abdulla, Hind; Sleptchenko, Andrei; Nayfeh, Ammar

doi:10.1016/j.rser.2024.114342

Cited by 11 publications

(3 citation statements)

References 169 publications

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“…To accurately describe the PV forecasting problem, recent studies [3] can be classified by considering (i) the forecasting horizon, (ii) the forecast result, and (iii) the type of algorithm, as schematically displayed in Figure 1.…”

Section: Overview Of Pvpf Methodsmentioning

confidence: 99%

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On Integrating Time-Series Modeling with Long Short-Term Memory and Bayesian Optimization: A Comparative Analysis for Photovoltaic Power Forecasting

Pacella,

Papa,

Papadia

2024

Applied Sciences

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The means of energy generation are rapidly progressing as production shifts from a centralized model to a fully decentralized one that relies on renewable energy sources. Energy generation is intermittent and difficult to control owing to the high variability in the weather parameters. Consequently, accurate forecasting has gained increased significance in ensuring a balance between energy supply and demand with maximum efficiency and sustainability. Despite numerous studies on this issue, large sample datasets and measurements of meteorological variables at plant sites are generally required to obtain a higher prediction accuracy. In practical applications, we often encounter the problem of insufficient sample data, which makes it challenging to accurately forecast energy production with limited data. The Holt–Winters exponential smoothing method is a statistical tool that is frequently employed to forecast periodic series, owing to its low demand for training data and high forecasting accuracy. However, this model has limitations, particularly when handling time-series analysis for long-horizon predictions. To overcome this shortcoming, this study proposes an integrated approach that combines the Holt–Winters exponential smoothing method with long short-term memory and Bayesian optimization to handle long-range dependencies. For illustrative purposes, this new method is applied to forecast rooftop photovoltaic production in a real-world case study, where it is assumed that measurements of meteorological variables (such as solar irradiance and temperature) at the plant site are not available. Through our analysis, we found that by utilizing these methods in combination, we can develop more accurate and reliable forecasting models that can inform decision-making and resource management in this field.

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Section: Overview Of Pvpf Methodsmentioning

confidence: 99%

“…A decrease in performance can significantly affect a project's overall profitability. Therefore, achieving maximum performance is essential for ensuring longterm profitability [3]. Considerable focus has been directed towards online predictive maintenance [4] and advanced energy forecasting methods for PV installations [5].…”

Section: Introductionmentioning

confidence: 99%

On Integrating Time-Series Modeling with Long Short-Term Memory and Bayesian Optimization: A Comparative Analysis for Photovoltaic Power Forecasting

Pacella,

Papa,

Papadia

2024

Applied Sciences

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“…Also, (12) proposed an assessment of the reliability and availability of a photovoltaic power plant. Later (13,14) proposed an evaluation of the reliability and availability of a photovoltaic power plant. Researchers also (15) discussed parallel system reliability modelling with maximum operation and repair durations.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Industrial Reliability: A Comparative Study of Hot and Cold Standby Configurations in Three-Unit Parallel Systems

Prawar

2024

jes

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This comparative research study explores the efficacy of two parallel systems, Model A employing a hot standby configuration and Model B utilizing a cold standby setup, each comprising three units operating based on demand. The investigation focuses on evaluating the reliability and cost-effectiveness of these systems, employing metrics such as Mean Time to System Failure, availability at full and reduced capacity, repairer busy periods, downtime, and profit analysis using Semi-Markov and regenerative point techniques. Results reveal that Model B (cold standby) outperforms Model A (hot standby) across multiple parameters, including Mean Time to System Failure, availability, repairer busy periods, downtime, and profitability. Specifically, Model B demonstrates higher values for Mean Time to System Failure, Availability at full and reduced capacity, Busy period for repairmen, and Profit in Rs. Consequently, it is inferred that Model B proves to be more reliable and efficient than Model A. These findings underscore the potential advantages of employing cold standby configurations in similar machinery setups, offering insights to enhance system performance and minimize downtime, thus suggesting avenues for optimizing cold standby parallel systems based on the study's outcomes.

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Integrating solar-driven biomass gasification and PV-electrolysis for sustainable fuel production: Thermodynamic performance, economic assessment, and CO2 emission analysis

Xin,

Zhang,

Chen

et al. 2024

Chemical Engineering Journal

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Photovoltaic systems operation and maintenance: A review and future directions

Cited by 11 publications

References 169 publications

On Integrating Time-Series Modeling with Long Short-Term Memory and Bayesian Optimization: A Comparative Analysis for Photovoltaic Power Forecasting

On Integrating Time-Series Modeling with Long Short-Term Memory and Bayesian Optimization: A Comparative Analysis for Photovoltaic Power Forecasting

Optimizing Industrial Reliability: A Comparative Study of Hot and Cold Standby Configurations in Three-Unit Parallel Systems

Integrating solar-driven biomass gasification and PV-electrolysis for sustainable fuel production: Thermodynamic performance, economic assessment, and CO2 emission analysis

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