The value of solar forecasts and the cost of their errors: A review

Gandhi, Oktoviano; Zhang, Wenjie; Kumar, Dhivya Sampath; Rodríguez-Gallegos, Carlos D.; Yagli, Gokhan Mert; Yang, Dazhi; Reindl, Thomas; Srinivasan, Dipti

doi:10.1016/j.rser.2023.113915

Cited by 13 publications

(5 citation statements)

References 115 publications

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“…Future studies might pay attention to hybrid models, the analysis of their structure validity, and their classification. It is imperative that actions be taken to translate progress in solar energy forecasting into universality and its practical application by network operators at all levels and segments, as it is insufficient [18]. It is also worth reviewing solar energy patents and projects implemented by startups.…”

Section: Discussionmentioning

confidence: 99%

“…In general, forecasts should be consistent, high quality, and beneficial to the users, and the adequacy of forecasts cannot be fully described by a single measure of error [15]. The accuracy of the forecast translates directly into its value for electricity market participants, although the economic value of solar forecasts is seldom quantified [18].…”

Section: Scope Of Review Reviewsmentioning

confidence: 99%

“…The rise in solar energy production has resulted in increased demand for enhanced solar energy forecasting methods [17]. The penetration of solar energy raises the cost of decisions based on incorrect forecasts at each power system level (transmission, distribution, microgrid, and household), as the costs of forecast errors might reach 75% of the levelized cost of electricity from a typical PV system [18]. The importance of solar energy forecasting is reflected in numerous publications.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Scope Of Review Reviewsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Chodakowska,

Nazarko,

Nazarko

et al. 2024

Energies

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“…Some studies have used meteorological data to calculate solar irradiance and predict the future PV power output based on PV power generation models, which are indirect prediction methods. A recent study [14] comprehensively reviewed the value of solar forecasting in the implementation of PVPF methods. Solar forecasting can be classified into two categories, deterministic and probabilistic, based on their uncertainty representations.…”

Section: Overview Of Pvpf Methodsmentioning

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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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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“…However, the conclusion from previous research might be used as inspiration. Antonanzas et al (2016) [62] informed about the need for analysis of the economic consequences of forecasts regarding solar energy, Gandhi et al (2024) [73] took up the challenge of comprehensively reviewing the value of solar forecasts and the cost of errors.…”

Section: Typology Of the Scope Of Solar Forecasting Reviewsmentioning

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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The value of solar forecasts and the cost of their errors: A review

Cited by 13 publications

References 115 publications

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

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

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

Solar Radiation Forecasting — State of Art and Methodological Discussion

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