A Physics-based Smart Persistence model for Intra-hour forecasting of solar radiation (PSPI) using GHI measurements and a cloud retrieval technique

Kumler, Andrew; Xie, Yu; Zhang, Yingchen

doi:10.1016/j.solener.2018.11.046

Cited by 44 publications

(22 citation statements)

References 32 publications

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“…As shown in the literature [37], when the forecast horizon is reduced to few minutes ahead, the most promising techniques rely on statistical analysis and image processing techniques. The final purpose is to provide a useful tool to assess the PV power nowcast to be later included in a power and/or energy management system.…”

Section: Nowcastingmentioning

confidence: 99%

Implementation of Different PV Forecast Approaches in a MultiGood MicroGrid: Modeling and Experimental Results

et al. 2021

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Microgrids represent a flexible way to integrate renewable energy sources with programmable generators and storage systems. In this regard, a synergic integration of those sources is crucial to minimize the operating cost of the microgrid by efficient storage management and generation scheduling. The forecasts of renewable generation can be used to attain optimal management of the controllable units by predictive optimization algorithms. This paper introduces the implementation of a two-layer hierarchical energy management system for islanded photovoltaic microgrids. The first layer evaluates the optimal unit commitment, according to the photovoltaic forecasts, while the second layer deals with the power-sharing in real time, following as close as possible the daily schedule provided by the upper layer while balancing the forecast errors. The energy management system is experimentally tested at the Multi-Good MicroGrid Laboratory under three different photovoltaic forecast models: (i) day-ahead model, (ii) intraday corrections and (iii) nowcasting technique. The experimental study demonstrates the capability of the proposed management system to operate an islanded microgrid in safe conditions, even with inaccurate day-ahead photovoltaic forecasts.

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Section: Nowcastingmentioning

confidence: 99%

Implementation of Different PV Forecast Approaches in a MultiGood MicroGrid: Modeling and Experimental Results

et al. 2021

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“…As shown in literature [25], when greatly reducing the forecast horizon to several minutes ahead, the most effective techniques are image-based or statistical. The first one, though providing more promising results, is highly expensive from a computational point of view and requires a higher initial investment with respect to the latter due to the additional cost of the instrumentation.…”

Section: Nowcastingmentioning

confidence: 99%

“…In the very short term, this method achieves very good results, especially in stationary conditions and presents the advantage of a negligible computational burden. Nevertheless, the validity of persistence loses effectiveness as the forecast horizon increases [25].…”

Section: Introductionmentioning

confidence: 99%

PV Plant Power Nowcasting: A Real Case Comparative Study With an Open Access Dataset

et al. 2020

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Energy systems around the world are undergoing substantial changes, with an increasing penetration of Renewable Energy Sources. For this reason, the availability of a pool of suitable forecasting models specific for the needed time horizon and task is becoming crucial in the grid operation. In addition, nowcasting techniques aiming at provideing the power forecast for the immediate future, are more often investigated due to the spread of micro-grids and the need of facing changing electrical market environments. In this paper a novel comprehensive methodology aiming at computing the PV power forecast on different time horizons and resolutions is introduced. Moving from the 24-hours ahead prediction provided by the Physical Hybrid Artificial Neural Network (PHANN), a technique to refine the power forecast for the following 3 hours with an hourly granularity is analyzed, leveraging on newer information available during the operations. Moreover, in order to provide the power forecast for the following 30 minutes on a minutely basis, an innovative modification of a statistical technique is proposed, the robust persistence. The proposed comprehensive approach allowed to greatly reduce the overall error committed when compared with the benchmark models. Finally, the proposed methodology is validated and tested on a freely available database consisting on different parameters recorded at both the meteorological and photovoltaic test facility at SolarTech LAB , Politecnico di Milano, Milan.

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“…-MBE (Mean Bias Error): provides information on the long-term performance of expressions, offering information regarding more or underestimation of estimated data, given by Eq. (7) [22][23]:…”

Section: Statistical Indicators Statistical Indicatorsmentioning

confidence: 99%

Performance of Angstrom Model under Algerian Climate

Baci¹,

Salmi²,

Hima³

et al. 2020

MMEP

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This study was conducted to estimate the monthly global solar irradiation in the Algerian territory using the Angström model, for seven sites in Algeria, namely: Algiers, Ain Bessem, Constantine, Oran, Djelfa, Mascara and Tamanrasset. The models are based on global solar irradiation in the horizontal surface as well as sunshine hours. To evaluate the results obtained by the different sites we calculated the following relative average algebraic differences: MAE, MBE and RMSE. The results show a remarkable agreement between the measured and computed values, especially in Tamanrasset site where the slope is in the order of 1. Obtained estimates can be generalized on the whole Algerian territory to provide ease way for researchers to get solar irradiation. However, optimization efforts have to be made to reduce mean error. Future works can be made based on our estimates to study effect of temperature and climatic parameters on the model. This work aim is achieved providing a data generator (software).(1) G0 is calculated using Eq. (2) [15]: Mathematical Modelling of Engineering Problems

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A Physics-based Smart Persistence model for Intra-hour forecasting of solar radiation (PSPI) using GHI measurements and a cloud retrieval technique

Cited by 44 publications

References 32 publications

Implementation of Different PV Forecast Approaches in a MultiGood MicroGrid: Modeling and Experimental Results

Implementation of Different PV Forecast Approaches in a MultiGood MicroGrid: Modeling and Experimental Results

PV Plant Power Nowcasting: A Real Case Comparative Study With an Open Access Dataset

Performance of Angstrom Model under Algerian Climate

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