A review of short‐term wind power probabilistic forecasting and a taxonomy focused on input data

Bazionis, Ioannis K.; Karafotis, Panagiotis A.; Georgilakis, Pavlos S.

doi:10.1049/rpg2.12330

Cited by 20 publications

(10 citation statements)

References 73 publications

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“…Parametric methods interpret the relationship between weather conditions and the predictive outcome of a forecast through historical data and data patterns. On the other hand, non‐parametric methods estimate the predictive error without the need of pre‐determining the distribution of the data [97] and that is an advantage. Regression models, ensemble methods, deep learning models, or hierarchical models have been developed over the past few years and are still being improved in order to provide accurate predictive results of non‐parametric methodologies.…”

Section: Discussion and Future Researchmentioning

confidence: 99%

“…Tables 5 and 6 present classifications based on the climatic zone on a global level and for European countries. Considering known locations of solar farms on a global level [96], the locations reviewed in our paper align with them and thus are logically included in this review paper. As a result, in order to present the results on a smaller scale, European countries were selected as an example due to the fact that they present a variety in terms of climatic zone appearance.…”

Section: Climatic Conditionsmentioning

confidence: 99%

“…To further provide information over the climatic conditions' effect on the SPF results, Figures 4 and 5 provide further information over the European continent. Temperate dry [27,83] Temperate humid [33,34,43,46,63,71,91] FIGURE 5 European photovoltaic power production map (2021) [96].…”

Section: Climatic Conditionsmentioning

confidence: 99%

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A taxonomy of short‐term solar power forecasting: Classifications focused on climatic conditions and input data

Bazionis,

Kousounadis‐Knousen,

Georgilakis

et al. 2023

IET Renewable Power Gen

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A review of the state‐of‐the‐art in short‐term Solar Power Forecasting (SPF) methodologies is presented in this paper. Over the last few years, developing and improving solar forecasting models has been the main focus of researchers, considering the need to efficiently increase their forecasting accuracy. Forecasting models aim to be used as an efficient tool to help with the stability and control of energy systems and electricity markets. Intending to further comprehend the factors affecting the quality of SPF models, this paper focuses on short‐term solar forecasting methodologies since they pose a crucial role in the daily operation and scheduling of power systems, since they focus on forecasting horizons typically ranging from 1 h to 1 day. The reviewed works are classified according to the climatic conditions, technical characteristics, and the forecasting errors of the different methodologies, providing readers with information over various different cases of SPF. Considering the need to improve the SPF efficiency, such classifications allow for important comparative conclusions to be drawn, depending on the location of each case and the meteorological data available. Future directions in the field of short‐term solar power forecasting are proposed considering the increasing development of SPF models’ architecture and their field of focus.

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Section: Discussion and Future Researchmentioning

confidence: 99%

Section: Climatic Conditionsmentioning

confidence: 99%

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A taxonomy of short‐term solar power forecasting: Classifications focused on climatic conditions and input data

Bazionis,

Kousounadis‐Knousen,

Georgilakis

et al. 2023

IET Renewable Power Gen

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“…Bazionis, I.K. et al [59] reviewed wind power generation forecasts using various parametric and nonparametric approaches. A classification of wind forecasting methods is given according to timescales, forecasting models, and output data.…”

Section: Introductionmentioning

confidence: 99%

A Review of Modern Wind Power Generation Forecasting Technologies

Tsai¹,

Hong²,

Lin³

et al. 2023

Preprint

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The prediction of wind power output is part of the basic work of power grid dispatching and energy distribution. At present, the output power prediction is mainly obtained by fitting and regressing the historical data. The medium- and long-term power prediction results exhibit large deviations due to the uncertainty of wind power generation. In order to meet the demand for accessing large-scale wind power into the electricity grid and to further improve the accuracy of short-term wind power prediction, it is necessary to develop models for accurate and precise short-term wind power prediction based on advanced algorithms for studying the output power of a wind power generation system. This paper summarizes the contribution of the current advanced wind power forecasting technology and delineates the key advantages and disadvantages of various wind power forecasting models. These models have different forecasting capabilities, update the weights of each model in real time, improve the comprehensive forecasting capability of the model, and have good application prospects in wind power generation forecasting. Furthermore, the case studies and examples in the literature for accurately predicting ultra-short-term and short-term wind power generation with uncertainty and randomness are reviewed and analyzed. Finally, we present prospects for future studies that can serve as useful directions for other researchers planning to conduct similar experiments and investigations.

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“…[4,5] focused on the recent probabilistic forecasting models aiming to predict wind power. The main focus of [4] was on short-term wind power probabilistic forecasting and compared the forecasting models developed by the previous literature. In contrast, ref.…”

Section: Introduction 1motivation and Contributionmentioning

confidence: 99%

Applications of Probabilistic Forecasting in Smart Grids: A Review

Khajeh

Laaksonen

2022

Applied Sciences

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This paper reviews the recent studies and works dealing with probabilistic forecasting models and their applications in smart grids. According to these studies, this paper tries to introduce a roadmap towards decision-making under uncertainty in a smart grid environment. In this way, it firstly discusses the common methods employed to predict the distribution of variables. Then, it reviews how the recent literature used these forecasting methods and for which uncertain parameters they wanted to obtain distributions. Unlike the existing reviews, this paper assesses several uncertain parameters for which probabilistic forecasting models have been developed. In the next stage, this paper provides an overview related to scenario generation of uncertain parameters using their distributions and how these scenarios are adopted for optimal decision-making. In this regard, this paper discusses three types of optimization problems aiming to capture uncertainties and reviews the related papers. Finally, we propose some future applications of probabilistic forecasting based on the flexibility challenges of power systems in the near future.

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A review of short‐term wind power probabilistic forecasting and a taxonomy focused on input data

Cited by 20 publications

References 73 publications

A taxonomy of short‐term solar power forecasting: Classifications focused on climatic conditions and input data

A taxonomy of short‐term solar power forecasting: Classifications focused on climatic conditions and input data

A Review of Modern Wind Power Generation Forecasting Technologies

Applications of Probabilistic Forecasting in Smart Grids: A Review

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