Potential assessment of the TVF-EMD algorithm in forecasting hourly global solar radiation: Review and case studies

Guermoui, Mawloud; Gairaa, Kacem; Ferkous, Khaled; Santos, Domingos S. de O.; Arrif, Toufik; Belaid, Abdelfetah

doi:10.1016/j.jclepro.2022.135680

Cited by 24 publications

(4 citation statements)

References 42 publications

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“…Moreover, as mentioned above, articles that aim to improve/develop forecasting methods often include an in-depth literature review [30,40]. The state-of-the-art provides the background for the proposed forecast models [41,42]. A solar techniques review might also precede a discussion on power systems security, scheduling and operations [43].…”

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

“…Moreover, as mentioned above, articles that aim to improve/develop forecasting methods often include an in-depth literature review [34,44]. The state-of-the-art provides the background for the proposed forecast models [45,46]. A review of solar techniques might also precede a discussion on power system security, scheduling, and operations [47].…”

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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“…Wavelet Transform (WT) 26 , Empirical Mode Decomposition (EMD) 27 , Ensemble Empirical Mode Decomposition (EEMD) 28 , Multivariate Empirical Mode Decomposition (MEMD) 29 , Ensemble Empirical Mode Decomposition (EEMD) 30 , a modified variant of the conventional EMD (CEEMDAN) 31 , 32 and Iterative Filtering decomposition method (IF) 2 . In this paper, five different decomposition learning approaches were independently investigated before being combined for short-term PV power forecasting, using one dimensional CNN model as an essence regressor owing to its capacity to extract more relevant features from the supplied input data.…”

Section: Introductionmentioning

confidence: 99%

An analysis of case studies for advancing photovoltaic power forecasting through multi-scale fusion techniques

Guermoui,

Fezzani,

Mohamed

et al. 2024

Sci Rep

Self Cite

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Integration renewable energy sources into current power generation systems necessitates accurate forecasting to optimize and preserve supply–demand restrictions in the electrical grids. Due to the highly random nature of environmental conditions, accurate prediction of PV power has limitations, particularly on long and short periods. Thus, this research provides a new hybrid model for forecasting short PV power based on the fusing of multi-frequency information of different decomposition techniques that will allow a forecaster to provide reliable forecasts. We evaluate and provide insights into the performance of five multi-scale decomposition algorithms combined with a deep convolution neural network (CNN). Additionally, we compare the suggested combination approach's performance to that of existing forecast models. An exhaustive assessment is carried out using three grid-connected PV power plants in Algeria with a total installed capacity of 73.1 MW. The developed fusing strategy displayed an outstanding forecasting performance. The comparative analysis of the proposed combination method with the stand-alone forecast model and other hybridization techniques proves its superiority in terms of forecasting precision, with an RMSE varying in the range of [0.454–1.54] for the three studied PV stations.

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Potential assessment of the TVF-EMD algorithm in forecasting hourly global solar radiation: Review and case studies

Cited by 24 publications

References 42 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

An analysis of case studies for advancing photovoltaic power forecasting through multi-scale fusion techniques

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