Probabilistic Solar Power Forecasting: A Review and Comparison

Panamtash, Hossein; Mahdavi, Shahrzad; Zhou, Qun

doi:10.1109/naps50074.2021.9449746

Cited by 9 publications

(5 citation statements)

References 27 publications

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“…Analyses of reviews conducted in recent years are more comprehensive and stick to the methodology, but this is not the rule, especially in the case of conference presentations, e.g. [31][32][33]. The actual analysis covers the last 10 years.…”

Section: Methodsmentioning

confidence: 99%

Solar Radiation Forecasting — State of Art and Methodological Discussion

Chodakowska,

Nazarko,

Nazarko

et al. 2024

Preprint

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Effective solar forecasting has become a critical topic in the scholarly literature in recent years due to the rapid growth of photovoltaic energy production worldwide and the inherent variability of this source of energy. Due to the technological and econonomic limitations of energy storage solutions, using other, mosty conventional, sources to cover energy shortfalls and at the same time utilising solar surpluses production becomes necessary. The need to optimize energy systems, ensuring power continuity, and balancing energy supply and demand has led to the development of various forecasting methods and approaches based on meteorological data or photovoltaic plant characteristics. This article presents the results of a meta-review of the solar forecasting literature, including the current state of knowledge and methodological discussion. It presents a comprehensive set of forecasting methods, evaluates current classifications, and proposes a new synthetic typology. The article emphasizes the increasing role of artificial intelligence (AI) and machine learning (ML) techniques in improving forecast accuracy, alongside traditional statistical and physical models. It explores the challenges of hybrid and ensemble models, which combine multiple forecasting approaches to enhance performance. The paper addresses the emerging trends in solar forecasting research, such as the integration of big data and advanced computational tools. Additionally, from a methodological perspective, the article outlines a rigorous approach to the meta-review research procedure, addresses the scientific challenges associated with conducting bibliometric research, and highlights best practices and principles. This includes defining research questions, selecting eligibility criteria, literature search, data extraction, synthesis, and assessing bias and quality. The article contributes to the solar forecasting field by providing up-to-date knowledge, along with insights on the emerging trends, future research directions, and anticipating implications for the theory and practicce.

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Section: Methodsmentioning

confidence: 99%

Solar Radiation Forecasting — State of Art and Methodological Discussion

Chodakowska,

Nazarko,

Nazarko

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…It should be noted that the first works were not a typical review but rather a presentation and discussion of methods with examples [32,33], and the literature review aims to provide background to select methods for testing and comparison [34]. Analyses of reviews conducted in recent years are more comprehensive and stick to the methodology, but this is not the rule, especially in the case of conference presentations, e.g., [35][36][37]. The actual analysis covers the last 10 years.…”

Section: Methodsmentioning

confidence: 99%

Solar Radiation Forecasting: A Systematic Meta-Review of Current Methods and Emerging Trends

Chodakowska,

Nazarko,

Nazarko

et al. 2024

Energies

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Effective solar forecasting has become a critical topic in the scholarly literature in recent years due to the rapid growth of photovoltaic energy production worldwide and the inherent variability of this source of energy. The need to optimise energy systems, ensure power continuity, and balance energy supply and demand is driving the continuous development of forecasting methods and approaches based on meteorological data or photovoltaic plant characteristics. This article presents the results of a meta-review of the solar forecasting literature, including the current state of knowledge and methodological discussion. It presents a comprehensive set of forecasting methods, evaluates current classifications, and proposes a new synthetic typology. The article emphasises the increasing role of artificial intelligence (AI) and machine learning (ML) techniques in improving forecast accuracy, alongside traditional statistical and physical models. It explores the challenges of hybrid and ensemble models, which combine multiple forecasting approaches to enhance performance. The paper addresses emerging trends in solar forecasting research, such as the integration of big data and advanced computational tools. Additionally, from a methodological perspective, the article outlines a rigorous approach to the meta-review research procedure, addresses the scientific challenges associated with conducting bibliometric research, and highlights best practices and principles. The article’s relevance consists of providing up-to-date knowledge on solar forecasting, along with insights on emerging trends, future research directions, and anticipating implications for theory and practice.

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“…This paper discusses basic probabilistic models [134] such as Gaussian distribution, quantile regression, etc. Gaussian distribution [135] depends on training and testing datasets to forecast solar power with allowable variance. In this case, the probability integral transform evaluates the probability distribution.…”

Section: Probabilistic Modelsmentioning

confidence: 99%

Systematic Review on Impact of Different Irradiance Forecasting Techniques for Solar Energy Prediction

et al. 2022

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As non-renewable energy sources are in the verge of exhaustion, the entire world turns towards renewable sources to fill its energy demand. In the near future, solar energy will be a major contributor of renewable energy, but the integration of unreliable solar energy sources directly into the grid makes the existing system complex. To reduce the complexity, a microgrid system is a better solution. Solar energy forecasting models improve the reliability of the solar plant in microgrid operations. Uncertainty in solar energy prediction is the challenge in generating reliable energy. Employing, understanding, training, and evaluating several forecasting models with available meteorological data will ensure the selection of an appropriate forecast model for any particular location. New strategies and approaches emerge day by day to increase the model accuracy, with an ultimate objective of minimizing uncertainty in forecasting. Conventional methods include a lot of differential mathematical calculations. Large data availability at solar stations make use of various Artificial Intelligence (AI) techniques for computing, forecasting, and predicting solar radiation energy. The recent evolution of ensemble and hybrid models predicts solar radiation accurately compared to all the models. This paper reviews various models in solar irradiance and power estimation which are tabulated by classification types mentioned.

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Probabilistic Solar Power Forecasting: A Review and Comparison

Cited by 9 publications

References 27 publications

Solar Radiation Forecasting — State of Art and Methodological Discussion

Solar Radiation Forecasting — State of Art and Methodological Discussion

Solar Radiation Forecasting: A Systematic Meta-Review of Current Methods and Emerging Trends

Systematic Review on Impact of Different Irradiance Forecasting Techniques for Solar Energy Prediction

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