Ensemble forecasting of solar irradiance by applying a mesoscale meteorological model

Liu, Yuanyuan; Shimada, Shinichi; Yoshino, Jun; Kobayashi, Tomonao; Miwa, Yasushi; Furuta, Kiyotaka

doi:10.1016/j.solener.2016.07.043

Cited by 27 publications

(10 citation statements)

References 16 publications

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“…(emails: xwegnon.agoua, robin.girard, georges.kariniotakis each with @minesparistech.fr) and uncertainty in the forecasts. Some of these probabilistic approaches are based on Numerical Weather Prediction (NWP) models or sky imaging and provide ensemble forecasts of the future PV generation [7]- [9]. Analog ensembles [10], regression trees [11], [12] and k-nearest neighbors (kNN) [13]- [15] are also found in the related literature on probabilistic PV forecasting.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Models for Spatio-Temporal Photovoltaic Power Forecasting

Agoua

Girard

Kariniotakis

2019

IEEE Trans. Sustain. Energy

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Photovoltaic (PV) power generation is characterized by significant variability. Accurate PV forecasts are a prerequisite to securely and economically operating electricity networks, especially in the case of large-scale penetration. In this paper, we propose a probabilistic spatio-temporal model for the PV power production that exploits production information from neighboring plants. The model provides the complete future probability density function of PV production for very short-term horizons (0-6 hours). The method is based on quantile regression and a L1 penalization technique for automatic selection of the input variables. The proposed modeling chain is simple, making the model fast and scalable to direct on-line application. The performance of the proposed approach is evaluated using a real-world test case, with a high number of geographically distributed PV installations and by comparison with state-ofthe-art probabilistic methods.

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Section: Introductionmentioning

confidence: 99%

Probabilistic Models for Spatio-Temporal Photovoltaic Power Forecasting

Agoua

Girard

Kariniotakis

2019

IEEE Trans. Sustain. Energy

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“…It is observed that ensemble forecasting has been well-established in the NWP solar forecasting community (e.g., Liu et al, 2016;Sperati et al, 2016;Thorey et al, 2015;Zamo et al, 2014a,b). However, despite several attempts, most ensemble learning technologies in the machine learning community have not been transferred to solar forecasting.…”

Section: Future Trends On Combining and Adjusting Forecastsmentioning

confidence: 99%

History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining

Yang¹,

Kleissl

Gueymard³

et al. 2018

Solar Energy

396

164

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Text mining is an emerging topic that advances the review of academic literature. This paper presents a preliminary study on how to review solar irradiance and photovoltaic (PV) power forecasting (both topics combined as "solar forecasting" for short) using text mining, which serves as the first part of a forthcoming series of text mining applications in solar forecasting. This study contains three main contributions: (1) establishing the technological infrastructure (authors, journals & conferences, publications, and organizations) of solar forecasting via the top 1000 papers returned by a Google Scholar search; (2) consolidating the frequently-used abbreviations in solar forecasting by mining the full texts of 249 ScienceDirect publications; and (3) identifying key innovations in recent advances in solar forecasting (e.g., shadow camera, forecast reconciliation). As most of the steps involved in the above analysis are automated via an application programming interface, the presented method can be transferred to other solar engineering topics, or any other scientific domain, by means of changing the search word. The authors acknowledge that text mining, at its present stage, serves as a complement to, but not a replacement of, conventional review papers.

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“…Although early methods were deterministic, probabilistic approaches are increasingly popular since they provide additional information about the distribution of future production and thus about uncertainty in the forecasts. Some of these probabilistic approaches are based on Numerical Weather Predictions (NWP) issued by meteorological models or sky imaging, and provide ensemble forecasts of the future PV generation [4][5][6]. Analog ensembles [7], regression trees [8,9] and k-nearest neighbors (kNN) [10,11] are also found in the related literature on probabilistic PV forecasting.…”

Section: Introductionmentioning

confidence: 99%

Photovoltaic Power Forecasting: Assessment of the Impact of Multiple Sources of Spatio-Temporal Data on Forecast Accuracy

2021

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The efficient integration of photovoltaic (PV) production in energy systems is conditioned by the capacity to anticipate its variability, that is, the capacity to provide accurate forecasts. From the classical forecasting methods in the state of the art dealing with a single power plant, the focus has moved in recent years to spatio-temporal approaches, where geographically dispersed data are used as input to improve forecasts of a site for the horizons up to 6 h ahead. These spatio-temporal approaches provide different performances according to the data sources available but the question of the impact of each source on the actual forecasting performance is still not evaluated. In this paper, we propose a flexible spatio-temporal model to generate PV production forecasts for horizons up to 6 h ahead and we use this model to evaluate the effect of different spatial and temporal data sources on the accuracy of the forecasts. The sources considered are measurements from neighboring PV plants, local meteorological stations, Numerical Weather Predictions, and satellite images. The evaluation of the performance is carried out using a real-world test case featuring a high number of 136 PV plants. The forecasting error has been evaluated for each data source using the Mean Absolute Error and Root Mean Square Error. The results show that neighboring PV plants help to achieve around 10% reduction in forecasting error for the first three hours, followed by satellite images which help to gain an additional 3% all over the horizons up to 6 h ahead. The NWP data show no improvement for horizons up to 6 h but is essential for greater horizons.

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Ensemble forecasting of solar irradiance by applying a mesoscale meteorological model

Cited by 27 publications

References 16 publications

Probabilistic Models for Spatio-Temporal Photovoltaic Power Forecasting

Probabilistic Models for Spatio-Temporal Photovoltaic Power Forecasting

History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining

Photovoltaic Power Forecasting: Assessment of the Impact of Multiple Sources of Spatio-Temporal Data on Forecast Accuracy

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