Comparison of numerical weather prediction solar irradiance forecasts in the US, Canada and Europe

Perez, Richard; Lorenz, Elke; Pelland, Sophie; Beauharnois, Mark; Knowe, Glenn Van; Hemker, Karl; Heinemann, Detlev; Remund, Jan; Müller, Stefan; Traunmüller, Wolfgang; Steinmauer, Gerald; Pozo, David; Ruiz‐Arias, José A.; Lara-Fanego, V.; Santigosa, Lourdes Ramirez; Gaston-Romero, Martin; Pomares, Luis Martín

doi:10.1016/j.solener.2013.05.005

Cited by 319 publications

(135 citation statements)

References 11 publications

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“…NWP systems have been utilized for forecasting applications for many years and perform best for the time horizons from 6 h to 2 weeks [30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

et al. 2017

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Accurate solar photovoltaic (PV) power forecasting is an essential tool for mitigating the negative effects caused by the uncertainty of PV output power in systems with high penetration levels of solar PV generation. Weather classification based modeling is an effective way to increase the accuracy of day-ahead short-term (DAST) solar PV power forecasting because PV output power is strongly dependent on the specific weather conditions in a given time period. However, the accuracy of daily weather classification relies on both the applied classifiers and the training data. This paper aims to reveal how these two factors impact the classification performance and to delineate the relation between classification accuracy and sample dataset scale. Two commonly used classification methods, K-nearest neighbors (KNN) and support vector machines (SVM) are applied to classify the daily local weather types for DAST solar PV power forecasting using the operation data from a grid-connected PV plant in Hohhot, Inner Mongolia, China. We assessed the performance of SVM and KNN approaches, and then investigated the influences of sample scale, the number of categories, and the data distribution in different categories on the daily weather classification results. The simulation results illustrate that SVM performs well with small sample scale, while KNN is more sensitive to the length of the training dataset and can achieve higher accuracy than SVM with sufficient samples.

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“…NWP systems have been utilized for forecasting applications for many years and perform best for the time horizons from 6 h to 2 weeks [30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

et al. 2017

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“…Although the large variety of cloud characteristics (opacity, motion, height, spatial distribution) makes these cloud-induced fluctuations difficult to predict, solar irradiance forecasting techniques have been successfully developed (a comprehensive overview is given in Inman et al, 2013, andHeinemann, 2012). The spectrum comprises numerical weather models (NWPs) (Perez et al, 2013), satellite-based forecasts using cloud motion vectors (Kühnert et al, 2013;Lorenz et al, 2004;Hammer et al, 1999), statistical methods based on machine learning (Wolff et al, 2013) and time series analysis (Reikard, 2009) predominantly developed for intra-day and day-ahead forecasts. For very short term forecasts with time horizons of up to 30 min, both NWP and satellite image-based models lack spatial and temporal resolution regarding cloud-induced small-scale variability (Inman et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Sky imagers have been used for years for monitoring cloud cover characteristics (Pfister et al, 2003;Long et al, 2006;Cazorla et al, 2008) and aerosol properties Cazorla, 2010).…”

Section: Introductionmentioning

confidence: 99%

Evaluating the spatio-temporal performance of sky-imager-based solar irradiance analysis and forecasts

et al. 2016

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Abstract. Clouds are the dominant source of small-scale variability in surface solar radiation and uncertainty in its prediction. However, the increasing share of solar energy in the worldwide electric power supply increases the need for accurate solar radiation forecasts.In this work, we present results of a very short term global horizontal irradiance (GHI) forecast experiment based on hemispheric sky images. A 2-month data set with images from one sky imager and high-resolution GHI measurements from 99 pyranometers distributed over 10 km by 12 km is used for validation. We developed a multi-step model and processed GHI forecasts up to 25 min with an update interval of 15 s. A cloud type classification is used to separate the time series into different cloud scenarios.Overall, the sky-imager-based forecasts do not outperform the reference persistence forecasts. Nevertheless, we find that analysis and forecast performance depends strongly on the predominant cloud conditions. Especially convective type clouds lead to high temporal and spatial GHI variability. For cumulus cloud conditions, the analysis error is found to be lower than that introduced by a single pyranometer if it is used representatively for the whole area in distances from the camera larger than 1-2 km. Moreover, forecast skill is much higher for these conditions compared to overcast or clear sky situations causing low GHI variability, which is easier to predict by persistence. In order to generalize the cloud-induced forecast error, we identify a variability threshold indicating conditions with positive forecast skill.

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“…up to 6 hours forecasting horizon, were also derived from the NWPM. This because, although satellite-images-based methodologies are the reference ones for short term solar radiation forecasting, NWPMs for solar radiation are able also to provide forecast in this time horizon and it is not clear at which forecasting horizon one the method outperforms the other (Pérez et al, 2013 ).…”

Section: Dni Forecasting Based On Nwpmsmentioning

confidence: 99%

Solar thermal energy predictability for the grid (STEP4Grid)

Fernández-León¹,

Pacheco²,

Bolinaga³

et al. 2016

AIP Conference Proceedings

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Abstract. There is a growing concern about the importance of the improvement of efficiency, the dispatchability of thermosolar plants and the predictability of the energy production for electrical markets. In the current research, a new developed system denominated STEP4Grid is presented and their products are analyzed. Currently it is on operation in the thermosolar plant of Solúcar in Sanlúcar la Mayor, Seville, Spain. Forecasting Direct Normal Irradiance (DNI) and Forecasting Gross Production (FGP) have been provided by the system. This product generates different time horizon forecasts combining all-sky cameras, satellite and Numerical Weather Prediction Model (NWPM) forecasts. The sensors network installed all over the plant provides continuous meteorological and non-meteorological data, which act as an input for the energy production model. The whole system is viewable by plant operators with the help of a layout system. For the May and June of 2015 database, the FGP based on satellite and Numerical Weather Prediction Models (NWPM) DNI predictions have an rMAE for an hour-ahead horizon of 16 % (May) and 17 % (June) respectively. For all the horizons, the FGP increases their deviations the further it is from the real-time and the profile is similar to the evolution of DNI forecasting rMAE.

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Comparison of numerical weather prediction solar irradiance forecasts in the US, Canada and Europe

Cited by 319 publications

References 11 publications

Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

Evaluating the spatio-temporal performance of sky-imager-based solar irradiance analysis and forecasts

Solar thermal energy predictability for the grid (STEP4Grid)

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