Forecasting Photovoltaic Power Generation Using Satellite Images

Yu, Dukhwan; Lee, Seowoo; Lee, Sang‐Won; Choi, Wonik; Liu, Ling

doi:10.3390/en13246603

Cited by 24 publications

(14 citation statements)

References 30 publications

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“…WMAPE is reported to be among 20% and 48% according to the season in exam, while in our case it is always below 20%. Moreover, in [44] another ANN-based PV power prediction method is shown. In particular, the ANN is trained with satellite images.…”

Section: Validation and Resultsmentioning

confidence: 99%

Power Forecasting of a Photovoltaic Plant Located in ENEA Casaccia Research Center

et al. 2021

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This work proposes an Artificial Neural Network (ANN) able to provide an accurate forecasting of power produced by photovoltaic (PV) plants. The ANN is customized on the basis of the particular season of the year. An accurate analysis of input variables, i.e., solar irradiance, temperature and air humidity, carried out by means of Pearson Correlation, has allowed to select, day by day, the most suitable set of inputs and ANN architecture also to reduce the necessity of large computational resource. Thus, features are added to the ANN as needed, avoiding waste of computational resources. The method has been validated through data collected from a PV plant installed in ENEA (National agency for new technologies, energy and sustainable economic development) Research Center, located in Casaccia, Rome (Italy). The developed strategy is able to furnish accurate predictions even in the case of strong irregularities of solar irradiance, providing accurate results in rapidly changing scenarios.

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Section: Validation and Resultsmentioning

confidence: 99%

Power Forecasting of a Photovoltaic Plant Located in ENEA Casaccia Research Center

et al. 2021

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“…However, under certain weather conditions such as overcast, the surface irradiance fluctuates dramatically in minute time scale because of the influence of moving clouds [137][138][139][140][141]. At this time, there is almost no correlation between the irradiance fluctuation and the historical irradiance data [142,143]. Therefore, the above phenomena pose a challenge to the extraction and forecasting of minute level meteorological features.…”

Section: The Forecasting and Generation Methods Of The Pv Systemmentioning

confidence: 99%

The Hidden-Layers Topology Analysis of Deep Learning Models in Survey for Forecasting and Generation of theWind Power and Photovoltaic Energy

Xu¹,

Shao²,

Deng³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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As wind and photovoltaic energy become more prevalent, the optimization of power systems is becoming increasingly crucial. The current state of research in renewable generation and power forecasting technology, such as wind and photovoltaic power (PV), is described in this paper, with a focus on the ensemble sequential LSTMs approach with optimized hidden-layers topology for short-term multivariable wind power forecasting. The methods for forecasting wind power and PV production. The physical model, statistical learning method, and machine learning approaches based on historical data are all evaluated for the forecasting of wind power and PV production. Moreover, the experiments demonstrated that cloud map identification has a significant impact on PV generation. With a focus on the impact of photovoltaic and wind power generation systems on power grid operation and its causes, this paper summarizes the classification of wind power and PV generation systems, as well as the benefits and drawbacks of PV systems and wind power forecasting methods based on various typologies and analysis methods.

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“…Many authors agree on the benefit of using meteorological satellite information for the intra-day horizon of the PV power forecast, such as Barbieri et al [12], Carriere et al [40], Kühnert et al [44]. The recent work of Yu [45] showed the performance improvement for a PV power generation prediction system based on the Eidetic three-dimensional (E3D)-long short-term memory (LSTM)-E3D-LSTM-model, when the future cloud amount from satellite data is included in the model with respect to the same model without cloud information inferred from satellite.…”

Section: Satellite Datamentioning

confidence: 97%

Exploitation of a New Short-Term Multimodel Photovoltaic Power Forecasting Method in the Very Short-Term Horizon to Derive a Multi-Time Scale Forecasting System

Collino

Ronzio

2021

Energies

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The relentless spread of photovoltaic production drives searches of smart approaches to mitigate unbalances in power demand and supply, instability on the grid and ensuring stable revenues to the producer. Because of the development of energy markets with multiple time sessions, there is a growing need of power forecasting for multiple time steps, from fifteen minutes up to days ahead. To address this issue, in this study both a short-term-horizon of three days and a very-short-term-horizon of three hours photovoltaic production forecasting methods are presented. The short-term is based on a multimodel approach and referred to several configurations of the Analog Ensemble method, using the weather forecast of four numerical weather prediction models. The very-short-term consists of an Auto-Regressive Integrated Moving Average Model with eXogenous input (ARIMAX) that uses the short-term power forecast and the irradiance from satellite elaborations as exogenous variables. The methods, applied for one year to four small-scale grid-connected plants in Italy, have obtained promising improvements with respect to refence methods. The time horizon after which the short-term was able to outperform the very-short-term has also been analyzed. The study also revealed the usefulness of satellite data on cloudiness to properly interpret the results of the performance analysis.

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Forecasting Photovoltaic Power Generation Using Satellite Images

Cited by 24 publications

References 30 publications

Power Forecasting of a Photovoltaic Plant Located in ENEA Casaccia Research Center

Power Forecasting of a Photovoltaic Plant Located in ENEA Casaccia Research Center

The Hidden-Layers Topology Analysis of Deep Learning Models in Survey for Forecasting and Generation of theWind Power and Photovoltaic Energy

Exploitation of a New Short-Term Multimodel Photovoltaic Power Forecasting Method in the Very Short-Term Horizon to Derive a Multi-Time Scale Forecasting System

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