Short-term solar forecasting based on sky images

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Methodology Cloud detectionImage projection Irradiance Cloud Motion Summary 1/ 17 Short-term solar forecasting based on sky images Potential and challenges Thomas Schmidt1 Detlev Heinemann1 Elke Lorenz2 1 Carl-von-Ossietzky University of Oldenburg Institute of Physics, University of Oldenburg, Energy Meteorology Group E-Mail t.schmidt@uni-oldenburg.de 2 Fraunhofer Institute für solare Energiesysteme ISE Heidenhofstraße 2, 79110 Freiburg T. Schmidt: Potential and challenges of sky imager based forecasting 6 th PV Performance Modeling and Monitoring Workshop, 24 th October 2016

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Methodology Cloud detectionImage projection Irradiance Cloud Motion Summary 2/ 17 Motivation Forecast methods 1 Day 1 Hour 15 Minutes 1 Minute 10 Seconds 1 Second Point Meter 1 km 10 km 1000 km Sky imager Statistical methods Numerical weather prediction Satellite traditional methods lack of spatial and temporal resolution for small-scale applications statistical methods / timeseries analysis cannot predict changes in cloud state T. Schmidt: Potential and challenges of sky imager based forecasting 6 th PV Performance Modeling and Monitoring Workshop, 24 th October 2016

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Methodology Cloud detectionImage projection Irradiance Cloud Motion Summary 2/ 17 Motivation Forecast methods 1 Day 1 Hour 15 Minutes 1 Minute 10 Seconds 1 Second Point Meter 1 km 10 km 1000 km Sky imager Statistical methods Numerical weather prediction Satellite for small-scale applications power ﬂuctuations / ramps have to be addressed requires high temporal and spatial resolution -> demand for accurate and reliable forecasts T. Schmidt: Potential and challenges of sky imager based forecasting 6 th PV Performance Modeling and Monitoring Workshop, 24 th October 2016

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Methodology Cloud detectionImage projection Irradiance Cloud Motion Summary 3/ 17 Sky imager forecast model Camera image Cloud detection Shadow projection Clear Sky Reference Cloud positionCloud height Solar geometry Lens function Image analysis K-NN model Measurements Clear sky irradiance Global horizontal irradiance Image features historical Real time Real time Irradiance modeling POA Irradiance Module temperature Plant meta data Power output PV Power Cloud motion Global horizontal irradiance Forecast 1. Image analysis 2. Cloud detection 3. Cloud projection 4. Shadow projection 5. Irradiance modeling 6. Cloud Motion -> Forecast 7. PV power modeling T. Schmidt: Potential and challenges of sky imager based forecasting 6 th PV Performance Modeling and Monitoring Workshop, 24 th October 2016

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Methodology Cloud detectionImage projection Irradiance Cloud Motion Summary 4/ 17 Cloud detection Camera image Cloud detection Shadow projection Clear Sky Reference Cloud positionCloud height Solar geometry Lens function Image analysis Cloud detection based on binary segmentation (cloud/sky) does not account for different optical properties (e.g. transmissivity) inhomogeneous brightness distribution -> misclassiﬁcations in circumsolar area are likely T. Schmidt: Potential and challenges of sky imager based forecasting 6 th PV Performance Modeling and Monitoring Workshop, 24 th October 2016

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Methodology Cloud detectionImage projection Irradiance Cloud Motion Summary 5/ 17 Cloud projection projection based on camera model (ﬁsh eye lens distortion, camera orientation) cloud distance d to camera is a function of CBH and pixels incidence angle: d = CBH ∗ tan(θ) perspective errors increase to the border of the image resolution decreases to the border of the image T. Schmidt: Potential and challenges of sky imager based forecasting 6 th PV Performance Modeling and Monitoring Workshop, 24 th October 2016

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Methodology Cloud detectionImage projection Irradiance Cloud Motion Summary 5/ 17 Cloud projection projection based on camera model (ﬁsh eye lens distortion, camera orientation) cloud distance d to camera is a function of CBH and pixels incidence angle: d = CBH ∗ tan(θ) perspective errors increase to the border of the image resolution decreases to the border of the image T. Schmidt: Potential and challenges of sky imager based forecasting 6 th PV Performance Modeling and Monitoring Workshop, 24 th October 2016

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Methodology Cloud detectionImage projection Irradiance Cloud Motion Summary 5/ 17 Cloud projection CBH: 1500 m projection based on camera model (ﬁsh eye lens distortion, camera orientation) cloud distance d to camera is a function of CBH and pixels incidence angle: d = CBH ∗ tan(θ) perspective errors increase to the border of the image resolution decreases to the border of the image T. Schmidt: Potential and challenges of sky imager based forecasting 6 th PV Performance Modeling and Monitoring Workshop, 24 th October 2016

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Methodology Cloud detectionImage projection Irradiance Cloud Motion Summary 5/ 17 Cloud projection CBH: 1500 m projection based on camera model (ﬁsh eye lens distortion, camera orientation) cloud distance d to camera is a function of CBH and pixels incidence angle: d = CBH ∗ tan(θ) perspective errors increase to the border of the image resolution decreases to the border of the image T. Schmidt: Potential and challenges of sky imager based forecasting 6 th PV Performance Modeling and Monitoring Workshop, 24 th October 2016

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Methodology Cloud detectionImage projection Irradiance Cloud Motion Summary 6/ 17 Cloud projection II Camera model results for Vivotek FE8172V in Oldenburg left: ﬁeld of view radius up to 30 km depending on CBH right: pixel resolution decreases rapidly T. Schmidt: Potential and challenges of sky imager based forecasting 6 th PV Performance Modeling and Monitoring Workshop, 24 th October 2016

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Methodology Cloud detectionImage projection Irradiance Cloud Motion Summary 7/ 17 Shadow projection Camera image Cloud detection Shadow projection Clear Sky Reference Cloud positionCloud height Solar geometry Lens function Image analysis shadow projection includes sun position and ray tracing is applied accurate shadow projection depends strongly on correct CBH estimation depending on sun position and CBH the covered area varies throughout the day T. Schmidt: Potential and challenges of sky imager based forecasting 6 th PV Performance Modeling and Monitoring Workshop, 24 th October 2016

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Methodology Cloud detectionImage projection Irradiance Cloud Motion Summary 8/ 17 Shadow projection II α Sky Imager h1 +Δhh1 θθ Δd = shadow displacement error Δd = Δh x ( tan(α) + tan(θ) ) Cloud layer displaced cloud layer Δh = cloud base height error Δd θ = sun zenith angle α = camera incidence angle Fig.1: Illustration of shadow projection with two different CBH Fig.2: Results from camera model -> Good CBH estimations necessary (ceilometers, triangulation, ...) T. Schmidt: Potential and challenges of sky imager based forecasting 6 th PV Performance Modeling and Monitoring Workshop, 24 th October 2016

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Methodology Cloud detectionImage projection Irradiance Cloud Motion Summary 8/ 17 Shadow projection II α Sky Imager h1 +Δhh1 θθ Δd = shadow displacement error Δd = Δh x ( tan(α) + tan(θ) ) Cloud layer displaced cloud layer Δh = cloud base height error Δd θ = sun zenith angle α = camera incidence angle Fig.1: Illustration of shadow projection with two different CBH Fig.2: Results from camera model -> Good CBH estimations necessary (ceilometers, triangulation, ...) T. Schmidt: Potential and challenges of sky imager based forecasting 6 th PV Performance Modeling and Monitoring Workshop, 24 th October 2016

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Methodology Cloud detectionImage projection Irradiance Cloud Motion Summary 9/ 17 Shadow projection III Fig.3: Shadow projection for Oldenburg with CBH = 1500m Fig.4: Shadow projection for Oldenburg with CBH = 1000m T. Schmidt: Potential and challenges of sky imager based forecasting 6 th PV Performance Modeling and Monitoring Workshop, 24 th October 2016

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Methodology Cloud detectionImage projection Irradiance Cloud Motion Summary 10/ 17 Multiple sky imager in City of Ulm Where to install cameras if a large area shoud be covered? -> compute shadow projection for different camera conﬁguration, CBH and sun positions 1500 m CBH at 12 UTC T. Schmidt: Potential and challenges of sky imager based forecasting 6 th PV Performance Modeling and Monitoring Workshop, 24 th October 2016

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Methodology Cloud detectionImage projection Irradiance Cloud Motion Summary 10/ 17 Multiple sky imager in City of Ulm Where to install cameras if a large area shoud be covered? -> compute shadow projection for different camera conﬁguration, CBH and sun positions 1500 m CBH at 12 UTC 750 m CBH at 12 UTC T. Schmidt: Potential and challenges of sky imager based forecasting 6 th PV Performance Modeling and Monitoring Workshop, 24 th October 2016

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Methodology Cloud detectionImage projection Irradiance Cloud Motion Summary 10/ 17 Multiple sky imager in City of Ulm Where to install cameras if a large area shoud be covered? -> compute shadow projection for different camera conﬁguration, CBH and sun positions 1500 m CBH at 17 UTC 750 m CBH at 12 UTC T. Schmidt: Potential and challenges of sky imager based forecasting 6 th PV Performance Modeling and Monitoring Workshop, 24 th October 2016

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Methodology Cloud detectionImage projection Irradiance Cloud Motion Summary 10/ 17 Multiple sky imager in City of Ulm Where to install cameras if a large area shoud be covered? -> compute shadow projection for different camera conﬁguration, CBH and sun positions coverage and overlapping depends strongly on CBH and daytime 1500 m CBH at 17 UTC 750 m CBH at 17 UTC T. Schmidt: Potential and challenges of sky imager based forecasting 6 th PV Performance Modeling and Monitoring Workshop, 24 th October 2016

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Methodology Cloud detectionImage projection Irradiance Cloud Motion Summary 11/ 17 Irradiance Aim: Compute surface solar irradiance from shadow information Different approaches used: statistical (adapting/learning from historic to near-real time measurements) modeling -> radiative transfer using cloud properties and position here: a binary mapping (shadow/no shadow) based on historical data is used GHI = DNIbinary ∗ cos(θ) + DHIconstant -> errors are introduced if irradiance does not follow binary approach (on/off) T. Schmidt: Potential and challenges of sky imager based forecasting 6 th PV Performance Modeling and Monitoring Workshop, 24 th October 2016

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Methodology Cloud detectionImage projection Irradiance Cloud Motion Summary 11/ 17 Irradiance Aim: Compute surface solar irradiance from shadow information Different approaches used: statistical (adapting/learning from historic to near-real time measurements) modeling -> radiative transfer using cloud properties and position here: a binary mapping (shadow/no shadow) based on historical data is used GHI = DNIbinary ∗ cos(θ) + DHIconstant -> errors are introduced if irradiance does not follow binary approach (on/off) 09:40:00 09:45:00 09:50:00 09:55:00 Time in UTC 0 200 400 600 800 1000 IrradianceinW/m2 University of Oldenburg - 2014-07-31 Diffuse Direct Fig.: Example timeseries (1Hz resolution) of DNI and DHI measurements T. Schmidt: Potential and challenges of sky imager based forecasting 6 th PV Performance Modeling and Monitoring Workshop, 24 th October 2016

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Methodology Cloud detectionImage projection Irradiance Cloud Motion Summary 12/ 17 Cloud motion Cloud motion Global horizontal irradiance Forecast Shadow projection Irradiance Fig.1: Forecast scheme 2013-04-19 12:05:45 UTC Fig.2: Cloud motion Cloud forecasts based on cloud motion of frozen cloud ﬁeld Cloud motion vectors (CMV) are derived from subsequent images Block matching + Cross correlation Particle image velocimetry (PIV) Optical Flow ... Optical Flow computed for a number of pixels, then averaged to global motion Assumption: homogeneous single cloud layer motion + no development T. Schmidt: Potential and challenges of sky imager based forecasting 6 th PV Performance Modeling and Monitoring Workshop, 24 th October 2016

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Methodology Cloud detectionImage projection Irradiance Cloud Motion Summary 13/ 17 Cloud motion II curved CMV estimated on raw images are transformed to straight CMV on projected cloud map Cloud motion example (left: raw, center: binary cloud decision, right: projected cloud map) T. Schmidt: Potential and challenges of sky imager based forecasting 6 th PV Performance Modeling and Monitoring Workshop, 24 th October 2016

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Methodology Cloud detectionImage projection Irradiance Cloud Motion Summary 14/ 17 Example Forecast 25 minute ahead forecasting with 1 s / 10 m resolution location shown 7 km north from camera position atmospheric situation single opaque cloud layer, homogeneous flow defined cloud edges Potential: clouds and cloud gaps can be predicted if model simplifications hold true Challenge: timing errors from CBH and CMV uncertainties Challenge: irradiance level errors from binary shadow -> irradiance mapping T. Schmidt: Potential and challenges of sky imager based forecasting 6 th PV Performance Modeling and Monitoring Workshop, 24 th October 2016

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Methodology Cloud detectionImage projection Irradiance Cloud Motion Summary 15/ 17 Potential and challenges Potential image based cloud/irradiance/power forecasts can predict cloud/cloud gap arrival large areas can be covered with single cameras cameras can be combined to cover whole cities Challenges complex atmospheric conditions require more complex modeling ﬁsh eye lenses introduce perspective errors and reduced resolution at image borders covered area varies with varying CBH and sun position accuracy depends on accuracy in cloud detection, CMV and CBH information and irradiance modeling T. Schmidt: Potential and challenges of sky imager based forecasting 6 th PV Performance Modeling and Monitoring Workshop, 24 th October 2016

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Methodology Cloud detectionImage projection Irradiance Cloud Motion Summary 15/ 17 Potential and challenges Potential image based cloud/irradiance/power forecasts can predict cloud/cloud gap arrival large areas can be covered with single cameras cameras can be combined to cover whole cities Challenges complex atmospheric conditions require more complex modeling ﬁsh eye lenses introduce perspective errors and reduced resolution at image borders covered area varies with varying CBH and sun position accuracy depends on accuracy in cloud detection, CMV and CBH information and irradiance modeling T. Schmidt: Potential and challenges of sky imager based forecasting 6 th PV Performance Modeling and Monitoring Workshop, 24 th October 2016

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Methodology Cloud detectionImage projection Irradiance Cloud Motion Summary 16/ 17 References Schmidt, T., Kalisch, J., Lorenz, E., Heinemann, D.: “Evaluating the spatio-temporal performance of sky imager based solar irradiacne analysis and forecasts.” Atmospheric Chemistry and Physics 16 (5): 3399–3412, 2016 Schmidt, T., Kalisch, J., Lorenz, E., Heinemann, D.: “Retrieving direct and diffuse radiation with the use of sky imager pictures.“ presentation at EGU General Assembly 2015, Vienna, Austria, 2015 Schmidt, T., Kalisch, J., Lorenz, E., Heinemann, D., Becker, G.: “Kürzestfristvorhersagen für eine 1-MW PV Anlage basierend auf Wolkenkamerabildern.”, Tagungsband des 31.Symposium Photovoltatische Solarenergie, Bad Staffelstein, 2016 Peters, D., R. Völker, T. Kilper, M. Calais, T. Schmidt, C. Carter, K. von Maydell, and C. Agert.: “Model-Based Design and Simulation of Control Strategies to Maximize the PV Hosting Capacity in Isolated Diesel Networks - Using Solar Short-Term Forecasts for Predictive Control of Diesel Generation.”, 2016, Proceedings of 32nd European Photovoltaic Solar Energy Conference and Exhibition. Anagnostos D. G., Schmidt T., Goverde H., Kalisch J., Catthoor F., Soudris D.: “PV Energy Yield Nowcasting Combining Sky Imaging with Simulation Models’.’, European Photovoltaic Solar Energy Conference (PVSEC), Hamburg, 14.-18. September 2015. T. Schmidt: Potential and challenges of sky imager based forecasting 6 th PV Performance Modeling and Monitoring Workshop, 24 th October 2016

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Methodology Cloud detectionImage projection Irradiance Cloud Motion Summary 17/ 17 Thank you for the attention! Questions, comments? T. Schmidt: Potential and challenges of sky imager based forecasting 6 th PV Performance Modeling and Monitoring Workshop, 24 th October 2016

Short-term solar forecasting based on sky images

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