Assessment of machine learning techniques for deterministic and probabilistic intra-hour solar forecasts

Pedro, Hugo T.C.; Coimbra, Carlos F.M.; David, Mathieu; Lauret, Philippe

doi:10.1016/j.renene.2018.02.006

Cited by 105 publications

(51 citation statements)

References 38 publications

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“…Other studies aim at the forecasting of solar irradiance rather than the PV power directly. In this setting, forecast skills up to 0.3 [39] and 0.15 [40] were reported for intra-hour predictions.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Data-Driven Techniques for Nowcasting Applied to an Industrial-Scale Photovoltaic Plant

et al. 2019

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The inherently non-dispatchable nature of renewable sources, such as solar photovoltaic, is regarded as one of the main challenges hindering their massive integration in existing electric grids. Accurate forecasting of the power output of the solar plant might therefore play a key role towards this goal. In this paper, we compare several machine learning and deep learning algorithms for intra-hour forecasting of the output power of a 1 MW photovoltaic plant, using meteorological data acquired in the field. With the best performing algorithms, our data-driven workflow provided prediction performance that compares well with the present state of the art and could be applied in an industrial setting.

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Section: Discussionmentioning

confidence: 99%

Comparison of Data-Driven Techniques for Nowcasting Applied to an Industrial-Scale Photovoltaic Plant

et al. 2019

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“…A related study is by Pedro et al (2018) based on 5 minutes GHI and direct normal irradiance (DNI) data obtained from Folsom, CA, USA. With the purpose of building an intra-hour forecasting model, they use two machine learning algorithms, i.e.…”

Section: Discussionmentioning

confidence: 99%

Periodic autoregressive forecasting of global solar irradiation without knowledge-based model implementation

Voyant¹,

Gooijer

Notton³

2018

Solar Energy

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Reliable forecasting methods increase the integration level of stochastic production and reduce cost of intermittence of photovoltaic production. This paper proposes a solar forecasting model for short time horizons, i.e. one to six hours ahead. In this time-range, machine learning methods have proven their efficiency. But their application requires that the solar irradiation time series is stationary which can be realized by calculating the clear sky global horizontal solar irradiance index (CSI), depending on certain meteorological parameters. This step is delicate and often generates additional uncertainty if conditions underlying the calculation of the CSI are not well-defined and/or unknown. As a novel alternative, we introduce a so-called periodic autoregressive (PAR) model. We discuss the computation of post-sample point forecasts and forecast intervals. We show the forecasting accuracy of the model via a real data set, i.e., the global horizontal solar irradiation (GHI) measured at two meteorological stations located at Corsica Island, France. In particular, and as opposed to methods based on CSI, a PAR model helps to improve forecast accuracy, especially for short forecast horizons. In all the cases, PAR is more appropriate than persistence, and smart persistence. Moreover, smart persistence based on the typical meteorological year gives more reliable results than when based on CSI.

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“…Over the past ten years, our lab has collected over several tens of terabytes of data, which have enabled a multitude of published solar forecasting studies. [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] For this data release, our choice of data was driven by a combination of factors. First, the data should be from areas of interest for solar forecasting, i.e., areas with large amounts of pre-existing or planned solar power generation.…”

Section: Data Sourcesmentioning

confidence: 99%

“…The data-driven approach relies on the extraction of image features that are then used as predictors in machine learning algorithms. 17,26 This strategy has seen an increase in popularity in recent times due to the maturity of tools such as convolution neural networks. 27 As mentioned above, the absence of a common dataset for different developers makes it difficult to properly evaluate competing skyimage algorithms.…”

Section: B Sky Imagesmentioning

confidence: 99%

A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods

Pedro

Larson

Coimbra

2019

Journal of Renewable and Sustainable Energy

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We describe and release a comprehensive solar irradiance, imaging, and forecasting dataset. Our goal with this release is to provide standardized solar and meteorological datasets to the research community for the accelerated development and benchmarking of forecasting methods. The data consist of three years (2014-2016) of quality-controlled, 1-min resolution global horizontal irradiance and direct normal irradiance ground measurements in California. In addition, we provide overlapping data from commonly used exogenous variables, including sky images, satellite imagery, and Numerical Weather Prediction forecasts. We also include sample codes of baseline models for benchmarking of more elaborated models.

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Assessment of machine learning techniques for deterministic and probabilistic intra-hour solar forecasts

Cited by 105 publications

References 38 publications

Comparison of Data-Driven Techniques for Nowcasting Applied to an Industrial-Scale Photovoltaic Plant

Comparison of Data-Driven Techniques for Nowcasting Applied to an Industrial-Scale Photovoltaic Plant

Periodic autoregressive forecasting of global solar irradiation without knowledge-based model implementation

A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods

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