Benchmarking of solar irradiance nowcast performance derived from all-sky imagers

Logothetis, Stavros-Andreas; Salamalikis, Vasileios; Wilbert, Stefan; Remund, Jan; Zarzalejo, Luis F.; Xie, Yu; Nouri, Bijan; Ntavelis, Evangelos; Nou, Julien; Hendrikx, Niels; Visser, Lennard; Sengupta, Manajit; Pó, Mário; Chauvin, Rémi; Grieu, Stéphane; Blum, Niklas; Sark, W.G.J.H.M. van; Kazantzidis, Andreas

doi:10.1016/j.renene.2022.08.127

Cited by 22 publications

(18 citation statements)

References 82 publications

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“…All our models are validated on the benchmark dataset from ref. [25], briefly described in Section 2.4. Thereby, a direct comparison to the other models from this benchmark, particularly the predecessor of the hybrid (DL) model can be made.…”

Section: Methodsmentioning

confidence: 99%

“…SP irradiance (test) dataset. [25] A larger share of clear sky conditions is included as completed days were selected.…”

Section: Sp Modelmentioning

confidence: 99%

“…For an expressive comparison to other methods, a common database is therefore required. A recent benchmark study [ 25 ] has compared several state‐of‐the‐art ASI nowcasting systems using selected dates of versatile cloud conditions. Evaluating most common error metrics for solar forecasting, the study can serve as baseline for new developments.…”

Section: Introductionmentioning

confidence: 99%

“…Both approaches show significant enhancements in nowcast accuracy compared to previous results shown in the benchmark study. [ 25 ]…”

Section: Introductionmentioning

confidence: 99%

“…Figure 2. Distribution of DNI variability classes for: a) training dataset before and after filtering out data of low variability conditions andb) benchmark (test) dataset [25]. A larger share of clear sky conditions is included as completed days were selected.…”

mentioning

confidence: 99%

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Combining Deep Learning and Physical Models: A Benchmark Study on All‐Sky Imager‐Based Solar Nowcasting Systems

Fabel,

Nouri,

Wilbert

et al. 2024

Solar RRL

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Intermittent solar irradiance due to passing clouds poses challenges for integrating solar energy into existing infrastructure. By making use of intra‐hour nowcasts (very short‐term forecasts), changing conditions of solar irradiance can be anticipated. All‐sky imagers, capturing sky conditions at high spatial and temporal resolution, can be the basis of such nowcasting systems. In this work, a deep learning (DL) model for solar irradiance nowcasts based on the transformer architecture is presented. The model is trained end‐to‐end using sequences of sky images and irradiance measurements as input to generate point‐forecasts up to 20 min ahead. Further, the effect of integrating this model into a hybrid system, consisting of a physics‐based model and smart persistence, is examined. A comparison between the DL and two hybrid models (with and without the DL model) is conducted on a benchmark dataset. Forecast accuracy for deterministic point‐forecasts is analyzed under different conditions using standard error metrics like root‐mean‐square error (RMSE) and forecast skill (FS). Furthermore, spatial and temporal aggregation effects are investigated. In addition, probabilistic nowcasts for each model are computed via a quantile approach. Overall, the DL model outperforms both hybrid models under the majority of conditions and aggregation effects.This article is protected by copyright. All rights reserved.

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Section: Methodsmentioning

confidence: 99%

“…SP irradiance (test) dataset. [25] A larger share of clear sky conditions is included as completed days were selected.…”

Section: Sp Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Both approaches show significant enhancements in nowcast accuracy compared to previous results shown in the benchmark study. [ 25 ]…”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Combining Deep Learning and Physical Models: A Benchmark Study on All‐Sky Imager‐Based Solar Nowcasting Systems

Fabel,

Nouri,

Wilbert

et al. 2024

Solar RRL

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Ramp Rate Metric Suitable for Solar Forecasting

Nouri,

Fabel,

Blum

et al. 2024

Solar RRL

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Solar irradiance forecasting plays a crucial role in integrating large quantities of intermittent solar power. Forecasting systems are commonly evaluated using metrics like root‐mean‐ square error (RMSE) and skill scores. However, these metrics aggregated over larger data sets do not adequately assess the prediction of ramp events, which are critical for many applications. This article introduces a novel, simple, and adaptable ramp rate metric that analyzes ramp events between successive lead times within forecasts. A case study on ramp rate mitigation in PV systems benchmarks suitable ramp thresholds for various solar irradiance components. The capabilities and limitations of deterministic and probabilistic forecasts from two all‐sky imager‐based models are evaluated for ramp prediction. A state‐of‐the‐art data‐driven vision transformer End2End model excels in RMSE and skill scores but performs poorly in ramp prediction. Conversely, a novel generative forecasting model combined with a convolutional neural network‐based irradiance model shows superior ramp prediction, achieving an F1 score of ≥0.7 for critical ramp events. This study underscores the importance of suitable ramp rate metrics and highlights the potential of generative models for enhancing ramp forecasting.

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Deep learning for intra-hour solar forecasting with fusion of features extracted from infrared sky images

Terrén-Serrano

Martínez‐Ramón

2023

Information Fusion

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Benchmarking of solar irradiance nowcast performance derived from all-sky imagers

Cited by 22 publications

References 82 publications

Combining Deep Learning and Physical Models: A Benchmark Study on All‐Sky Imager‐Based Solar Nowcasting Systems

Combining Deep Learning and Physical Models: A Benchmark Study on All‐Sky Imager‐Based Solar Nowcasting Systems

Ramp Rate Metric Suitable for Solar Forecasting

Deep learning for intra-hour solar forecasting with fusion of features extracted from infrared sky images

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