An operational bidding framework for aggregated electric vehicles on the electricity spot market

Visser, Lennard; Kootte, M.E.; Ferreira, Ana; Sicurani, O.; Pauwels, Eric J.; Vuik, C.; Sark, W.G.J.H.M. van; AlSkaif, Tarek

doi:10.1016/j.apenergy.2021.118280

Cited by 14 publications

(7 citation statements)

References 26 publications

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“…AI techniques, including deep learning architectures, supervised learning models, regression models, support vector regression, ensemble learning, and physical-based techniques, have been extensively employed in this field [33,44,70]. These approaches have been implemented in numerous countries, such as the United Kingdom, the Netherlands, the United States of America, Spain, China, and others [16,19,36].…”

Section: Discussionmentioning

confidence: 99%

“…However, it is important to note that the accuracy of solar energy forecasting varies across different countries due to the influence of models, methods, and dominant weather patterns specific to each region. The unique weather characteristics of each country result in varying accuracy for different forecast horizons [36]. For example, in the UK, with its cloud-dominant weather, a 10 min solar energy forecast achieves an accuracy with an RMSE of 115.6 W/m 2 [33].…”

Section: Discussionmentioning

confidence: 99%

“…The wide range of different applications that have been applied successfully by researchers highlights the versatility of AI techniques. For example, AI has been used to develop energy bidding tools [36]; perform day-ahead solar forecasting [37], wind speed forecasting [35], solar radiation estimation [2,8,13,35], the monitoring of fields of PV systems [38], fault detection, and the diagnosis of wind energy systems [5]; and demand load predictions [3].…”

Section: Artificial Intelligence and Solar Forecastingmentioning

confidence: 99%

“…Notably, deep learning architectures based on ECLIPSE (Envisioning Cloud-Induced Perturbations in Solar Energy) have been investigated [33], showcasing the potential of advanced Neural Network structures. Additionally, supervised learning models have been explored to forecast solar energy production [36], leveraging historical data and relevant features. Furthermore, regression, support vector regression, ensemble learning, deep learning, and physical-based techniques have been investigated to model solar energy patterns [44].…”

Section: Prevalence Of Artificial Intelligencementioning

confidence: 99%

“…In the United Kingdom (UK), known for its cloud-dominant weather, a 10 min solar energy forecast achieves an accuracy with an RMSE of 115.6 W/m 2 [33]. The Netherlands, characterized by its cloudy weather, demonstrates a forecasting skill of 19.9% with an average of 160 sunny days throughout the year [36]. The United States of America (USA) provides a solar energy forecast at 30 min intervals.…”

Section: Local Weather Conditionsmentioning

confidence: 99%

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A Review of Solar Forecasting Techniques and the Role of Artificial Intelligence

Barhmi,

Heynen,

Golroodbari

et al. 2024

Solar

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Solar energy forecasting is essential for the effective integration of solar power into electricity grids and the optimal management of renewable energy resources. Distinguishing itself from the existing literature, this review study provides a nuanced contribution by centering on advancements in forecasting techniques. While preceding reviews have examined factors such as meteorological input parameters, time horizons, the preprocessing methodology, optimization, and sample size, our study uniquely delves into a diverse spectrum of time horizons, spanning ultrashort intervals (1 min to 1 h) to more extended durations (up to 24 h). This temporal diversity equips decision makers in the renewable energy sector with tools for enhanced resource allocation and refined operational planning. Our investigation highlights the prominence of Artificial Intelligence (AI) techniques, specifically focusing on Neural Networks in solar energy forecasting, and we review supervised learning, regression, ensembles, and physics-based methods. This showcases a multifaceted approach to address the intricate challenges associated with solar energy predictions. The integration of Satellite Imagery, weather predictions, and historical data further augments precision in forecasting. In assessing forecasting models, our study describes various error metrics. While the existing literature discusses the importance of metrics, our emphasis lies on the significance of standardized datasets and benchmark methods to ensure accurate evaluations and facilitate meaningful comparisons with naive forecasts. This study stands as a significant advancement in the field, fostering the development of accurate models crucial for effective renewable energy planning and emphasizing the imperative for standardization, thus addressing key gaps in the existing research landscape.

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