Intra-Hour Pv Power Forecasting Based on Sky Imagery

Paulescu, Marius; Dughir, Ciprian; Stefu, Nicoleta; Sabadus, Andreea; Calinoiu, Delia; Bădescu, Viorel

doi:10.2139/ssrn.4199421

Cited by 2 publications

(3 citation statements)

References 33 publications

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“…The algorithm is tailored to avoid spurious results due to the Sun artefact. The downside, as detailed in Ref [10],…”

Section: Case Studymentioning

confidence: 99%

“…In the current implementation of PV2-state, SSN is forecasted by applying the sky imagery-based procedure from [9]. Reference [10] reports the general performance of the PV2-state model equipped with the sky imagery module. Differently, this study analyzes the phenomenological causes that generate erroneous forecasts for SSN, and consequently, large errors in the forecast of PV power.…”

Section: Introductionmentioning

confidence: 99%

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Short-Term PV Power Forecasting Based on Sky Imagery. A Case Study at the West University of Timisoara

Dughir

Sabadus

Stefu

et al. 2022

Annals of West University of Timisoara - Physics

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This study deals with the performance of PV2-state model in intra-hour forecasting of photovoltaic (PV) power. The PV2-state model links an empirical model for estimating the PV power delivered by a PV system under clear-sky with a model for forecasting the relative position of the Sun and clouds. Sunshine number (SSN), a binary quantifier showing if the Sun shines or not, is used as a measure for the Sun position with respect to clouds. A physics-based approach to intra-hour forecasting, processing cloud field information from an all-sky imager, is applied to predict SSN. The quality of SSN prediction conditions the overall quality of PV2-state forecasts. The PV2-state performance was evaluated against a challenging database (high variability in the state-of-the-sky, thin cloud cover, broken cloud field, isolated passing clouds) comprising radiometric data and sky-images collected on the Solar Platform of the West University of Timisoara, Romania. The investigation was performed from two perspectives: general model accuracy and, as a novelty, identification of characteristic elements in the state-of-the-sky which fault the SSN prediction. The outcome of such analysis represents the basis of further research aiming to increase the performance in PV power forecasting.

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“…The algorithm is tailored to avoid spurious results due to the Sun artefact. The downside, as detailed in Ref [10],…”

Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Short-Term PV Power Forecasting Based on Sky Imagery. A Case Study at the West University of Timisoara

Dughir

Sabadus

Stefu

et al. 2022

Annals of West University of Timisoara - Physics

Self Cite

View full text Add to dashboard Cite

show abstract

“…In [25,26], missing power data are reconstructed based on space-time correlation, and an irradiance encryption model is established by using a 3D convolutional neural network to achieve full grid coverage of power and meteorological data. In [27], an improved version of the PV2-state model is introduced for intra-hour PV power prediction. Reference [28] proposes a new model for predicting photovoltaic power generation using LSTM-TCN.…”

Section: Introductionmentioning

confidence: 99%

PV Power Forecasting in the Hexi Region of Gansu Province Based on AP Clustering and LSTNet

Li,

Yang,

Gou

2024

International Transactions on Electrical Energy Systems

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Accurate PV power forecasting is becoming a mandatory task to integrate the PV system into the power grid, schedule it, and ensure the safety of the power grid. In this paper, a novel model for PV power prediction using AP-LSTNet has been proposed. It consists of a combination of affinity propagation clustering and long-term and short-term time series network models. First, the affinity propagation algorithm is used to divide the regionally distributed photovoltaic station clusters into different seasons. The Pearson correlation coefficient is used to determine the strong correlation between meteorological factors of photovoltaic power, and the bilinear interpolation method is used to encrypt the meteorological data of the corresponding photovoltaic station cluster. Furthermore, LSTNet is used to mine the long-term and short-term temporal and spatial dependence of photovoltaic power, and meteorological factor series and linear components of auto-regression are superimposed to realize the simultaneous prediction of multiple photovoltaic stations in the group. Finally, PV power plants in five cities, Wuwei, Jinchang, Zhangye, Jiuquan, and Jiayuguan in the Hexi region of Gansu Province, China, will be selected to test the proposed model. The experimental comparison shows that the prediction model achieves high prediction accuracy and robustness.

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Intra-Hour Pv Power Forecasting Based on Sky Imagery

Cited by 2 publications

References 33 publications

Short-Term PV Power Forecasting Based on Sky Imagery. A Case Study at the West University of Timisoara

Short-Term PV Power Forecasting Based on Sky Imagery. A Case Study at the West University of Timisoara

PV Power Forecasting in the Hexi Region of Gansu Province Based on AP Clustering and LSTNet

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