Development of ANN Based Model for Solar Potential Assessment Using Various Meteorological Parameters

Kumar, Sanjay; Kaur, Tarlochan

doi:10.1016/j.egypro.2016.11.227

Cited by 63 publications

(24 citation statements)

References 8 publications

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“…The ANN is a computational model that can represent complex linear or nonlinear data associations. ANN is simulating the computation of the biological brain, which attempts to mimic intelligent human behavior into machines based on symbolic rules . The ANN has unique features like parallelism computing, training, and learning data that made it a proper tool for many applications such as data classification, association, and recognition.…”

Section: The Study Methodologymentioning

confidence: 99%

“…Simulations attempt to describe and discuss the characteristics and the behavior of any physical system using a simulation system that emulates the functioning of the real system under artificial conditions. Many researchers used similar system modeling using artificial neural network (ANN); in Table , some of the most important of these studies are listed …”

Section: Introductionmentioning

confidence: 99%

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Mathematical and neural network models for predicting the electrical performance of a PV/T system

et al. 2019

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Summary There are many photovoltaic/thermal (PV/T) systems' designs that are used mainly to reduce the temperature of the PV cell by using a thermal medium to cool the photovoltaic module. In this study, a PV/T system uses nano‐phase change material (PCM) and nanofluid cooling system was adopted. Three cooling models were compared using nanofluid (SiC‐water) and nano‐PCM to improve the performance and productivity of the PV/T system. Three mathematical models were developed for linear prediction, and their results were compared with the predicted artificial neural network results, results were verified, and experimental results were appropriate. Three common evaluation criteria were adopted to compare that the results of proposed forecasting models with other models developed in many research studies are done, including the R2, mean square error (MSE), and root‐mean‐square error (RMSE). Besides, different experiments were implemented using varying number of hidden layers to ensure that the proposed neural network models achieved the best results. The best neural prediction models deployed in this study resulted in good R2 score of 0.81 and MSE of 0.0361 and RMSE and RMSE rate is 0.371. Mathematical models have proven their high potential to easily determine the future outcomes with the preferable circumstances for any PV/T system in a precise way to reduce the error rate to the lowest level.

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Section: The Study Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mathematical and neural network models for predicting the electrical performance of a PV/T system

et al. 2019

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“…Several lines of research for solar resource prediction have been observed with the use of ANN due to the low error rate (up to 20%) [10], [11], [12]. Specific types of ANN are used, such as:…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

MLP Back Propagation Artificial Neural Network for Solar Resource Forecasting in Equatorial Areas

Lima¹,

Carvalho²,

Braga³

et al. 2018

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Renewable energy (RE) resources such as solar are increasingly being used worldwide. Solar resource shows high availability, but presents an intermittent characteristic, causing oscillations in the electricity production. Intermittence is one of the main barriers for the use of solar plants in a system that needs to balance demand and electricity production. Aiming to contribute to a larger use of the solar resource in the world energy matrix, we propose a solar irradiance prediction methodology, developed from data collected in Fortaleza-CE (latitude: −03° 43′, longitude: −38° 32′). Predictions were developed using Multilayer Perceptron (MLP) Back propagation Artificial Neural Network (ANN) with the advance of 1 hour. In the best ANN performance, 41.9% of the predictions obtained up to 5% of error, 58.7% obtained errors lower than 10% and 68.6% obtained errors lower than 15%. MAPE (mean absolute percentage error) of 6.11% was found, which can be considered good, since errors found in previous works reached 20%.

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“…The magnitude of power generated by a solar photovoltaic (PV) system and conversion efficiency (solar air heater, solar water heater, solar concentrator) is largely a function of the global solar radiation (GSR) [3]. However, stochastic components of solar energy variability depend on the cloud coverage characteristics, as well as factors including the aerosol, dust particles, smoke and airborne pollutants that are largely difficult to measure on an ongoing basis and, therefore, must be derived from remotely-sensed data products.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Neural Networks Trained with MODIS Satellite-Derived Predictors for Long-Term Global Solar Radiation Prediction

et al. 2019

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Solar energy predictive models designed to emulate the long-term (e.g., monthly) global solar radiation (GSR) trained with satellite-derived predictors can be employed as decision tenets in the exploration, installation and management of solar energy production systems in remote and inaccessible solar-powered sites. In spite of a plethora of models designed for GSR prediction, deep learning, representing a state-of-the-art intelligent tool, remains an attractive approach for renewable energy exploration, monitoring and forecasting. In this paper, algorithms based on deep belief networks and deep neural networks are designed to predict long-term GSR. Deep learning algorithms trained with publicly-accessible Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data are tested in Australia’s solar cities to predict the monthly GSR: single hidden layer and ensemble models. The monthly-scale MODIS-derived predictors (2003–2018) are adopted, with 15 diverse feature selection approaches including a Gaussian Emulation Machine for sensitivity analysis used to select optimal MODIS-predictor variables to simulate GSR against ground-truth values. Several statistical score metrics are adopted to comprehensively verify surface GSR simulations to ascertain the practicality of deep belief and deep neural networks. In the testing phase, deep learning models generate significantly lower absolute percentage bias (≤3%) and high Kling–Gupta efficiency (≥97.5%) values compared to the single hidden layer and ensemble model. This study ascertains that the optimal MODIS input variables employed in GSR prediction for solar energy applications can be relatively different for diverse sites, advocating a need for feature selection prior to the modelling of GSR. The proposed deep learning approach can be adopted to identify solar energy potential proactively in locations where it is impossible to install an environmental monitoring data acquisition instrument. Hence, MODIS and other related satellite-derived predictors can be incorporated for solar energy prediction as a strategy for long-term renewable energy exploration.

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Development of ANN Based Model for Solar Potential Assessment Using Various Meteorological Parameters

Cited by 63 publications

References 8 publications

Mathematical and neural network models for predicting the electrical performance of a PV/T system

Mathematical and neural network models for predicting the electrical performance of a PV/T system

MLP Back Propagation Artificial Neural Network for Solar Resource Forecasting in Equatorial Areas

Deep Learning Neural Networks Trained with MODIS Satellite-Derived Predictors for Long-Term Global Solar Radiation Prediction

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