Use of satellite data to improve solar radiation forecasting with Bayesian Artificial Neural Networks

Aguiar, L. Mazorra; Pereira, Bruno; David, Mathieu; Díaz, F.; Lauret, Philippe

doi:10.1016/j.solener.2015.10.041

Cited by 91 publications

(12 citation statements)

References 27 publications

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“…The calculated inputs such as Cs have a unique role to increase the model performance as seen in M2 compared to M1 in both stations. This is in agreement to improved results in some limited studies which used Cs as inputs either for modelling or forecasting GHI [47,49,78,79].…”

Section: Discussionsupporting

confidence: 90%

“…Hence, the low performance of M2, M3 and M6 compared to better performance in M4 and M5 are related to the use of SDDs as new inputs with climate variables. This has been reported by studies which have used SDDs in foresting GHI [46,47]. The better performance of M7 and M8 compared to the previous M1-M6 is related to the use of Cs with SDDs in those models.…”

Section: Discussionmentioning

confidence: 57%

“…ANNs have also been used to forecast GHI with various data and over various time intervals. For example, HC3, climate variables and other inputs have been analysed in ANN models to forecast GHI in intra-day and 1-6 h ahead in Gran Canary, Spain in two different studies [46,47]. They demonstrated that the SDD improved the forecasting results.…”

Section: Introductionmentioning

confidence: 99%

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Modelling Hourly Global Horizontal Irradiance from Satellite-Derived Datasets and Climate Variables as New Inputs with Artificial Neural Networks

et al. 2019

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More accurate data of hourly Global Horizontal Irradiance (GHI) are required in the field of solar energy in areas with limited ground measurements. The aim of the research was to obtain more precise and accurate hourly GHI by using new input from Satellite-Derived Datasets (SDDs) with new input combinations of clear sky (Cs) and top-of-atmosphere (TOA) irradiance on the horizontal surface and with observed climate variables, namely Sunshine Duration (SD), Air Temperature (AT), Relative Humidity (RH) and Wind Speed (WS). The variables were placed in ten different sets as models in an artificial neural network with the Levenberg–Marquardt training algorithm to obtain results from training, validation and test data. It was applied at two station types in northeast Iraq. The test data results with observed input variables (correlation coefficient (r) = 0.755, Root Mean Square Error (RMSE) = 33.7% and bias = 0.3%) are improved with new input combinations for all variables (r = 0.983, RMSE = 9.5% and bias = 0.0%) at four automatic stations. Similarly, they improved at five tower stations with no recorded SD (from: r = 0.601, RMSE = 41% and bias = 0.7% to: r = 0.976, RMSE = 11.2% and bias = 0.0%). The estimation of hourly GHI is slightly enhanced by using the new inputs.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 57%

Section: Introductionmentioning

confidence: 99%

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Modelling Hourly Global Horizontal Irradiance from Satellite-Derived Datasets and Climate Variables as New Inputs with Artificial Neural Networks

et al. 2019

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“…This method finds application in the training of neural networks as well as linear regression fitting. In [53] the researchers have used artificial neural networks (ANNs) to forecast the hourly GHI from 1 h to 6 h ahead. Authors of [54] have also used ARD as a regression technique.…”

Section: Methods For Solar Irradiance Forecastingmentioning

confidence: 99%

Solar irradiance resource and forecasting: a comprehensive review

Kumar

Yagli

Kashyap³

et al. 2020

IET Renewable Power Generation

106

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With the increase in demand for energy, penetration of alternative sources of energy in the power grid has increased. Photovoltaic (PV) energy is the most common and popular form of energy sources which is widely integrated into the existing grid. As solar energy is intermittent in nature, to ensure uninterrupted and reliable power supply to the prosumers, it is essential to forecast the solar irradiance. Accurate solar forecasting is necessary to facilitate large-scale modelling and deployment of PV plants without disrupting the quality and reliability of the power grid as well as to manage the power demand and supply. There are various methods to predict the solar irradiance such as numerical weather prediction methods, satellite-based approaches, cloud-image based methodologies, data-driven methods, and sensor-network based approaches. This study gives an overall review of the different resources and methods used for forecasting solar irradiance in different time horizons and also gives an extensive review of the sensor networks that are used for determining solar irradiance. The various error metrics and accessible data sets available for the sensor networks are also discussed that can be used for validation purposes.

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“…Neural networks were trained using local weather measurements of different cases and gathered parameters from the past [3]. While in [4], the authors improved the accuracy rate of forecasting using data from the satellites. In [5], development of a new method called CIADCast is performed, which is used for short term forecasting of solar radiation…”

Section: Introductionmentioning

confidence: 99%

The Impact of PV Power Plants Implementation on Electricity Cost and Shadow Price Minimization

Nazih*,

El-Khattam,

Abdel-Salam

2020

IJRTE

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The increase reliance on competitive electricity market has led to widespread research to reallocate energy sources and minimize the price of energy and the services related to it. The main issues that faces the design of any energy market, is thehigh cost of generation and the high shadow pricesthat highly impacts the consumers.Also,achieving the supply-demand balance and minimization of the transmission congestion is a vital goal while planning. In this paper, a transparent and open competitive market is attained. In order to control the electricity market and reduce the market clearing price, this study proposed introducing renewable energy power plants which has lower electricity generation cost in comparison with the conventional power plants.Minimization of the shadow price is achieved by dividing the electricity grid into multiple regions. Every region has a different shadow price depending on the load demand and the power plants available to supply the demand at this region. Where,the market clearing price of each region is set as the price of generation of the highest power plant sharing in supplying the load demand at this region. This methodology is applied on the Egyptian unified power network.Sizing and allocation of the renewable energy power plantsis studied carefully from the technical and economical point of view to maximize the benefit and minimize the overall cost function and shadow price

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Use of satellite data to improve solar radiation forecasting with Bayesian Artificial Neural Networks

Cited by 91 publications

References 27 publications

Modelling Hourly Global Horizontal Irradiance from Satellite-Derived Datasets and Climate Variables as New Inputs with Artificial Neural Networks

Modelling Hourly Global Horizontal Irradiance from Satellite-Derived Datasets and Climate Variables as New Inputs with Artificial Neural Networks

Solar irradiance resource and forecasting: a comprehensive review

The Impact of PV Power Plants Implementation on Electricity Cost and Shadow Price Minimization

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