scholarly journals Prediction of Solar Power Using Near-Real Time Satellite Data

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5865
Author(s):  
Abhnil Amtesh Prasad ◽  
Merlinde Kay
Keyword(s):  
Real Time ◽  
Satellite Data ◽  
Solar Power ◽  
Short Term ◽  
Motion Vectors ◽  
Clear Sky ◽  
Cloud Motion ◽  
Visible Images ◽  
Grid Operators ◽  
Short Term Prediction

Solar energy production is affected by the attenuation of incoming irradiance from underlying clouds. Often, improvements in the short-term predictability of irradiance using satellite irradiance models can assist grid operators in managing intermittent solar-generated electricity. In this paper, we develop and test a satellite irradiance model with short-term prediction capabilities using cloud motion vectors. Near-real time visible images from Himawari-8 satellite are used to derive cloud motion vectors using optical flow estimation techniques. The cloud motion vectors are used for the advection of pixels at future time horizons for predictions of irradiance at the surface. Firstly, the pixels are converted to cloud index using the historical satellite data accounting for clear, cloudy and cloud shadow pixels. Secondly, the cloud index is mapped to the clear sky index using a historical fitting function from the respective sites. Thirdly, the predicated all-sky irradiance is derived by scaling the clear sky irradiance with a clear sky index. Finally, a power conversion model trained at each site converts irradiance to power. The prediction of solar power tested at four sites in Australia using a one-month benchmark period with 5 min ahead prediction showed that errors were less than 10% at almost 34–60% of predicted times, decreasing to 18–26% of times under live predictions, but it outperformed persistence by >50% of the days with errors <10% for all sites. Results show that increased latency in satellite images and errors resulting from the conversion of cloud index to irradiance and power can significantly affect the forecasts.

scholarly journals A Scalable Approach for Short-Term Predictions of Link Traffic Flow by Online Association of Clustering Profiles

2020 ◽  
Vol 21 (2) ◽  
pp. 119-124
Author(s):  
Alessandro Attanasi ◽  
Marco Pezzulla ◽  
Luca Simi ◽  
Lorenzo Meschini ◽  
Guido Gentile
Keyword(s):  
Real Time ◽  
Traffic Management ◽  
Time Window ◽  
Management Control ◽  
Spatial Correlations ◽  
Time Data ◽  
Short Term ◽  
Data Set ◽  
Affinity Propagation Algorithm ◽  
Short Term Prediction

AbstractShort-term prediction of traffic flows is an important topic for any traffic management control room. The large availability of real-time data raises not only the expectations for high accuracy of the forecast methodology, but also the requirements for fast computing performances. The proposed approach is based on a real-time association of the latest data received from a sensor to the representative daily profile of one among the clusters that are built offline based on an historical data set using Affinity Propagation algorithm. High scalability is achieved ignoring spatial correlations among different sensors, and for each of them an independent model is built-up. Therefore, each sensor has its own clusters of profiles with their representatives; during the short-term forecast operation the most similar representative is selected by looking at the last data received in a specified time window and the proposed forecast corresponds to the values of the cluster representative.

scholarly journals A new Lagrandian based short term prediction methodology for HF radar currents

2020 ◽  
Author(s):  
Lohitzune Solabarrieta ◽  
Ismael Hernandez-Carrasco ◽  
Anna Rubio ◽  
Alejandro Orfila ◽  
Michael Campbell ◽  
...  
Keyword(s):  
Real Time ◽  
Red Sea ◽  
Skill Score ◽  
Hf Radar ◽  
Surface Currents ◽  
Bay Of Biscay ◽  
Short Term ◽  
High Frequency Radar ◽  
Term Prediction ◽  
Short Term Prediction

Abstract. The use of High Frequency Radar (HFR) data is increasing worldwide for operational oceanography and data assimilation, as it provides real-time coastal surface currents at high temporal and spatial resolution. In this work, a Lagrangian based empirical real-time, Short-Term Prediction (L-STP) system is presented in order to provide short term forecasts of up to 48 hours of ocean currents from HFR data. The method is based on the finding of historical gridded analogues of Lagrangian trajectories obtained from HFR surface currents. Then, assuming that the present state will follow the same temporal evolution as did the historical analogue, we obtain a short-term prediction of the surface currents. The method is applied to two HFR systems covering two areas with different dynamical characteristics: the southeast Bay of Biscay and the central Red Sea. The L-STP improves on previous prediction systems implemented for the SE Bay of Biscay and provides good results for the Red Sea study area. A comparison of the L-STP methodology with predictions based on persistence and reference fields has been performed in order to quantify the error introduced by this Lagrangian approach. Furthermore, a temporal sensitivity analysis has been addressed to determine the limit of applicability of the methodology regarding the temporal horizon of Lagrangian prediction. A real-time skill-score has been developed using the results of this analysis which allows to identify periods when the short-term prediction performance is more likely to be low and persistence can be used as a better predictor for the future currents.

scholarly journals A New Approach for Satellite-Based Probabilistic Solar Forecasting with Cloud Motion Vectors

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4951
Author(s):  
Thomas Carrière ◽  
Rodrigo Amaro e Silva ◽  
Fuqiang Zhuang ◽  
Yves-Marie Saint-Drenan ◽  
Philippe Blanc
Keyword(s):  
Prediction Interval ◽  
Solar Forecasting ◽  
Forecast Horizon ◽  
Motion Vectors ◽  
Forecast Uncertainty ◽  
Average Width ◽  
Clear Sky ◽  
Cloud Motion ◽  
Proposed Model ◽  
The One

Probabilistic solar forecasting is an issue of growing relevance for the integration of photovoltaic (PV) energy. However, for short-term applications, estimating the forecast uncertainty is challenging and usually delegated to statistical models. To address this limitation, the present work proposes an approach which combines physical and statistical foundations and leverages on satellite-derived clear-sky index (kc) and cloud motion vectors (CMV), both traditionally used for deterministic forecasting. The forecast uncertainty is estimated by using the CMV in a different way than the one generally used by standard CMV-based forecasting approach and by implementing an ensemble approach based on a Gaussian noise-adding step to both the kc and the CMV estimations. Using 15-min average ground-measured Global Horizontal Irradiance (GHI) data for two locations in France as reference, the proposed model shows to largely surpass the baseline probabilistic forecast Complete History Persistence Ensemble (CH-PeEn), reducing the Continuous Ranked Probability Score (CRPS) between 37% and 62%, depending on the forecast horizon. Results also show that this is mainly driven by improving the model’s sharpness, which was measured using the Prediction Interval Normalized Average Width (PINAW) metric.

scholarly journals A new Lagrangian-based short-term prediction methodology for high-frequency (HF) radar currents

Ocean Science ◽  
2021 ◽  
Vol 17 (3) ◽  
pp. 755-768
Author(s):  
Lohitzune Solabarrieta ◽  
Ismael Hernández-Carrasco ◽  
Anna Rubio ◽  
Michael Campbell ◽  
Ganix Esnaola ◽  
...  
Keyword(s):  
Real Time ◽  
High Frequency ◽  
Skill Score ◽  
Hf Radar ◽  
Surface Currents ◽  
Short Term ◽  
High Frequency Radar ◽  
Term Prediction ◽  
Short Term Prediction ◽  
Temporal Horizon

Abstract. The use of high-frequency radar (HFR) data is increasing worldwide for different applications in the field of operational oceanography and data assimilation, as it provides real-time coastal surface currents at high temporal and spatial resolution. In this work, a Lagrangian-based, empirical, real-time, short-term prediction (L-STP) system is presented in order to provide short-term forecasts of up to 48 h of ocean currents. The method is based on finding historical analogs of Lagrangian trajectories obtained from HFR surface currents. Then, assuming that the present state will follow the same temporal evolution as the historical analog, we perform the forecast. The method is applied to two HFR systems covering two areas with different dynamical characteristics: the southeast Bay of Biscay and the central Red Sea. A comparison of the L-STP methodology with predictions based on persistence and reference fields is performed in order to quantify the error introduced by this approach. Furthermore, a sensitivity analysis has been conducted to determine the limit of applicability of the methodology regarding the temporal horizon of Lagrangian prediction. A real-time skill score has been developed using the results of this analysis, which allows for the identification of periods when the short-term prediction performance is more likely to be low, and persistence can be used as a better predictor for the future currents.

Ultra-short-term solar power forecasting based on a modified clear sky model

2020 ◽  
Author(s):  
Yuan Ma ◽  
Xuemin Zhang ◽  
Shengwei Mei ◽  
Zhao Zhen ◽  
Rui Gao ◽  
...  
Keyword(s):  
Solar Power ◽  
Short Term ◽  
Clear Sky ◽  
Solar Power Forecasting ◽  
Power Forecasting
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