Artificial neural network predictor for grid-connected solar photovoltaic installations at atmospheric temperature

Ehsan, R. Muhammad; Simon, Sishaj P.; Venkateswaran, P. R.

doi:10.1109/icage.2014.7050142

Cited by 10 publications

(7 citation statements)

References 12 publications

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“…Photovoltaic modules are tested at a temperature of 25 °C (at STC), where the module's nameplate datasheet values are determined. Practically, the average annual surface temperatures in the tropical and temperate zones are considerably higher than the STC temperature (Ehsan et al 2014). Depending on their installed location, heat generated by rising temperatures can reduce the panel's output efficiency by 10-25% (Honsberg and Bowden 2019).…”

Section: Resultsmentioning

confidence: 99%

“…Experiments to study the effects of superhydrophobic nanocoatings on PV module temperatures are carried out in real-time conditions at Bharat Heavy Electricals Limited (BHEL) campus in Tiruchirappalli, Tamil Nadu, India [10° 48′ 18″ N, 78° 41′ 8″ E]. The location has an elevation of 88 m from sea level (Ehsan et al 2017). The climatological average annual temperature values of Tiruchirappalli are 27.73 °C (average), 22.86 °C (minimum), and 33.92 °C (maximum) (NASA 2021).…”

Section: Methodsmentioning

confidence: 99%

“…The climatological average annual temperature values of Tiruchirappalli are 27.73 °C (average), 22.86 °C (minimum), and 33.92 °C (maximum) (NASA 2021). With an average clearness index of 0.54, average daylight of 12.13 h and direct normal radiation of 4.77 kWh/m 2 /day, the location is ideal for large-scale solar power plants (Ehsan et al 2014). L1280S mono-crystalline silicon modules of 80 W p peak power manufactured by BHEL's Solar Business Division (SBD) are used for conducting all the experiments in this work to ensure uniformity in module design and construction.…”

Section: Methodsmentioning

confidence: 99%

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Effects of nanocoatings on the temperature-dependent cell parameters and power generation of photovoltaic panels

et al. 2022

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Operational requirements of photovoltaic (PV) modules result in their inherent exposure to harsh environmental conditions. The performance of solar cells decreases with increasing temperature, with both efficiency and power output getting affected. High ambient temperature coupled with irradiance absorption leads to an elevated photovoltaic cell operating temperature, adversely affecting the panels' lifespan. Superhydrophobic nanocoatings are the preferred solution to reduce the accumulation of dust (soiling) over the surface of the panels. This article aims to study the effects of nanocoatings on module operating temperature and temperature-dependent cell parameters, such as open-circuit voltage ( ), short-circuit current ( ) and power generation. The application of nanocoating over the surface of solar panels reduces the operating temperatures while improving power generation in a temperate location with high annual atmospheric temperatures.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Effects of nanocoatings on the temperature-dependent cell parameters and power generation of photovoltaic panels

et al. 2022

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“…Regarding the direct forecasting, in [51] a two-layer feedforward network trained with Levenberg-Marquardt algorithm is used to estimate the profile of the output power of a grid-connected 20kWp solar power plant in a reputed manufacturing industry located in Tiruchirappalli, India. An experimental database comprising the PV output power and atmospheric temperature time series was used for training the ANN.…”

Section: Related Workmentioning

confidence: 99%

Deep Neural Networks for Multivariate Prediction of Photovoltaic Power Time Series

et al. 2020

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The large-scale penetration of renewable energy sources is forcing the transition towards the future electricity networks modeled on the smart grid paradigm, where energy clusters call for new methodologies for the dynamic energy management of distributed energy resources and foster to form partnerships and overcome integration barriers. The prediction of energy production of renewable energy sources, in particular photovoltaic plants that suffer from being highly intermittent, is a fundamental tool in the modern management of electrical grids shifting from reactive to proactive, with also the help of advanced monitoring systems, data analytics and advanced demand side management programs. The gradual move towards a smart grid environment impacts not only the operating control/management of the grid, but also the electricity market. The focus of this paper is on advanced methods for predicting photovoltaic energy output that prove, through their accuracy and robustness, to be useful tools for an efficient system management, even at prosumer's level and for improving the resilience of smart grids. Four different deep neural models for the multivariate prediction of energy time series are proposed; all of them are based on the Long Short-Term Memory network, which is a type of recurrent neural network able to deal with long-term dependencies. Additionally, two of these models also use Convolutional Neural Networks to obtain higher levels of abstraction, since they allow to combine and filter different time series considering all the available information. The proposed models are applied to real-world energy problems to assess their performance and they are compared with respect to the classic univariate approach that is used as a reference benchmark. The significance of this work is to show that, once trained, the proposed deep neural networks ensure their applicability in real online scenarios characterized by high variability of data, without requiring retraining and end-user's tricks.

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“…For solar forecasting, very different methodologies are preferred, depending on the forecast horizon [28]. In the last decade, it has been widely recognized that Artificial Neural Networks (ANNs) are superior to traditional statistical models when the relationship between the output and input variables is implicit, complex and nonlinear [29]. Many interesting forecasting techniques are therefore based on the neural network theory.…”

Section: Solar Irradiation and Load Power Consumption Forecastingmentioning

confidence: 99%

Energy Storage Control for Dispatching Photovoltaic Power

Brenna

Foiadelli

Longo

et al. 2018

IEEE Trans. Smart Grid

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The strong growth of the solar power generation industry requires an increasing need to predict the profile of solar power production over a day and develop highly efficient and optimized stand-alone and grid-connected photovoltaic systems. Moreover, the opportunities offered by Battery Energy Storage Systems (BESSs) coupled with PV systems require an ability to forecast the load power to optimize the size of the entire system composed of PV panels and storage devices. This paper presents a sizing and control strategy of BESSs for dispatching a photovoltaic generation farm in the 1-hour ahead and day-ahead markets. The forecasting of the solar irradiation and load power consumption is performed by developing a predictive model based on a feedforward neural network trained with the Levenberg-Marquardt back-propagation learning algorithm

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Artificial neural network predictor for grid-connected solar photovoltaic installations at atmospheric temperature

Cited by 10 publications

References 12 publications

Effects of nanocoatings on the temperature-dependent cell parameters and power generation of photovoltaic panels

Effects of nanocoatings on the temperature-dependent cell parameters and power generation of photovoltaic panels

Deep Neural Networks for Multivariate Prediction of Photovoltaic Power Time Series

Energy Storage Control for Dispatching Photovoltaic Power

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