Artificial Intelligence Techniques in Solar Energy Applications

Kalogirou, Soteris A.; Şencan, Arzu

doi:10.5772/10343

Cited by 33 publications

(15 citation statements)

References 57 publications

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“…Comprehensive overviews of applications of ANNs for thermal engineering and especially renewable energy systems are presented in Kalogirou (2000Kalogirou ( , 2001, Kalogirou and Sencan (2010), Yang (2008) and Mohanraj et al (2012). Following is a list of the most relevant works in the field of ANN related to the subject described in the present paper:…”

Section: Application Of Ann In the Field Of Modelling Sorption Chillersmentioning

confidence: 99%

“…When the training has reached a satisfactory level the network holds the weights constant. Now the weights contain meaningful and important information, whereas before the training their values are random and have no meaning (Kalogirou and Sencan, 2010). After the successfully training step the trained ANN model can be used to predict the output quantities as a function of the input quantities.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

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Artificial Neural Network modelling of sorption chillers

2014

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Section: Application Of Ann In the Field Of Modelling Sorption Chillersmentioning

confidence: 99%

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Artificial Neural Network modelling of sorption chillers

2014

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“…Over the last two decades, an extensive number of studies using ANNs in energy systems have been published [18][19][20][21][22][23][24], comprising recent investigations by the authors [25,26]. The development of accurate ANN models depends on a range of different factors and algorithms; therefore, a number of challenging efforts regarding the performance predictive methodology must be addressed.…”

Section: Introductionmentioning

confidence: 99%

Simulation Study on the Effect of Reduced Inputs of Artificial Neural Networks on the Predictive Performance of the Solar Energy System

et al. 2017

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Abstract:In recent years, there has been a strong growth in solar power generation industries. The need for highly efficient and optimised solar thermal energy systems, stand-alone or grid connected photovoltaic systems, has substantially increased. This requires the development of efficient and reliable performance prediction capabilities of solar heat and power production over the day. This contribution investigates the effect of the number of input variables on both the accuracy and the reliability of the artificial neural network (ANN) method for predicting the performance parameters of a solar energy system. This paper describes the ANN models and the optimisation process in detail for predicting performance. Comparison with experimental data from a solar energy system tested in Ottawa, Canada during two years under different weather conditions demonstrates the good prediction accuracy attainable with each of the models using reduced input variables. However, it is likely true that the degree of model accuracy would gradually decrease with reduced inputs. Overall, the results of this study demonstrate that the ANN technique is an effective approach for predicting the performance of highly non-linear energy systems. The suitability of the modelling approach using ANNs as a practical engineering tool in renewable energy system performance analysis and prediction is clearly demonstrated.

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“…The process of training is the modification of connection weights until it satisfies users' needs. During the training process, weights are adjusted in order to acquire the desired output [33]. …”

Section: Multilayer Feed-forward Neural Network (Mlfns)mentioning

confidence: 99%

Novel Method for Measuring the Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters Based on Artificial Neural Networks and Support Vector Machine

Liu

Zhang

et al. 2015

Energies

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Abstract:The determinations of heat collection rate and heat loss coefficient are crucial for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, the direct determination requires complex detection devices and a series of standard experiments, which also wastes too much time and manpower. To address this problem, we propose machine learning models including artificial neural networks (ANNs) and support vector machines (SVM) to predict the heat collection rate and heat loss coefficient without a direct determination. Parameters that can be easily obtained by "portable test instruments" were set as independent variables, including tube length, number of tubes, tube center distance, heat water mass in tank, collector area, final temperature and angle between tubes and ground, while the heat collection rate and heat loss coefficient determined by the detection device were set as dependent variables respectively. Nine hundred fifteen samples from in-service water-in-glass evacuated tube solar water heaters were used for OPEN ACCESSEnergies 2015, 8 8815 training and testing the models. Results show that the multilayer feed-forward neural network (MLFN) with 3 nodes is the best model for the prediction of heat collection rate and the general regression neural network (GRNN) is the best model for the prediction of heat loss coefficient due to their low root mean square (RMS) errors, short training times, and high prediction accuracies (under the tolerances of 30%, 20%, and 10%, respectively).

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Artificial Intelligence Techniques in Solar Energy Applications

Cited by 33 publications

References 57 publications

Artificial Neural Network modelling of sorption chillers

Artificial Neural Network modelling of sorption chillers

Simulation Study on the Effect of Reduced Inputs of Artificial Neural Networks on the Predictive Performance of the Solar Energy System

Novel Method for Measuring the Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters Based on Artificial Neural Networks and Support Vector Machine

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