Prediction of monthly mean daily global solar radiation using Artificial Neural Network

Sivamadhavi, V.; Rajalakshmi, S.

doi:10.1007/s12040-012-0235-1

Cited by 30 publications

(12 citation statements)

References 17 publications

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“…It was claimed that it is the most accurate algorithm to be used and yields the best results compare to other algorithms [9,10]. The number of hidden layer is set to one in order to avoid complexity during the process.…”

Section: Artificial Neural Network Methodsmentioning

confidence: 99%

Solar Energy Potential Estimation in Perak Using Clearness Index and Artificial Neural Network

Assadi

Razak

Habib

2014

MATEC Web of Conferences

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In this paper solar energy potential has been estimated by two methods which are clearness index and artificial network (ANN) methods. The selected region is Seri Iskandar, Perak (4°24´latitude, 100°58´E longitude, 24 m altitude). Experimental data (monthly average daily radiation on horizontal surface) was obtained from UTP solar research site in UTP campus. The data include the period of 2010 to 2012 and were used for testing the artificial neural network model and also for determination of clearness index. Also the experimental data of the three meteorological, Ipoh, Bayan Lepas & KLIA were used in calculating the clearness index and for training the neural network. Result shows that clearness index for Seri Iskandar is 0.52, the highest radiation is on February (20.45 MJ/m 2 /day), annual average is 18.25 MJ/m 2 /day and clearness index is more accurate than ANN when there is limited data supply. In general, Perak states show strong potential for solar energy application.

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Section: Artificial Neural Network Methodsmentioning

confidence: 99%

Solar Energy Potential Estimation in Perak Using Clearness Index and Artificial Neural Network

Assadi

Razak

Habib

2014

MATEC Web of Conferences

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“…ere is a continuous cycle between the process of information forward propagation and the process of error backpropagation, which will stop when the squared error of the network reaches minimum [14]. Standard backpropagation algorithm is widely used [15][16][17].…”

Section: Improved Backpropagation (Bp) Neural Networkmentioning

confidence: 99%

Model Building for Regional Ecological Risk Prediction and Evaluation of Prediction Accuracy

Shao¹,

Li²,

Liu³

et al. 2021

Advances in Civil Engineering

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The regional ecological risk model is built to predict the regional ecological risk level more accurately by using principal component analysis and optimizing standard BP neural network. Taking Xiangxi Tujia and Miao Autonomous Prefecture as an example, twelve primary factors affecting regional risk are selected. The sample data are processed by principal component analysis. The obtained main components are then used as input factors of the improved BP neural network, and the level of ecological risk is used as output factor. The results indicate that the error between the expected output and the actual output is 4.36% in 2016, 1.08% in 2017, and 5.18% in 2018, respectively, with all controlled within 6%. Compared with the prediction accuracy made by standard BP neural network without principal component analysis, the prediction accuracy made by improved BP neural network with principal component analysis is greatly improved. This comprehensive prediction model provides a better evaluation method for prediction of ecological risk level.

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“…Neural networks are more efficacious than mathematical models in data prediction. In fact, they can predict various components of solar radiation using some meteorological parameters as input [35][36][37][38]. Therefore, their use in many applications has gained increasing popularity as they proved to be one of the best tools for non-linear mapping.…”

Section: Deep Learning Analysismentioning

confidence: 99%

Deep learning-based estimation of PV power plant potential under climate change: a case study of El Akarit, Tunisia

2020

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Background Several climatologists and experts in the renewable energy field agree that GHI and DNI calculation models must be revised because of the increasingly unpredictable and powerful climatic disturbances. The construction of analytical mathematical models for the prediction of these disturbances is almost impossible because the physical phenomena relating to the climate are often complex. We raise the question over the current and future PV system’s sustainable energy production and whether climate disturbances will be affecting this sustainability and resulting in supply decline. Methods In this paper, we tried to use deep learning as a tool to predict the evolution of the future production of any geographic site. This approach can allow for improvements in decision-making concerning the implantation of solar PV or CSP plants. To reach this aim, we have deployed the databases of NASA and the Tunisian National Institute of Meteorology relating to the climatic parameters of the case study region of El Akarit, Gabes, Tunisia. In spite of the colossal amount of processed data that dates back to 1985, the use of deep learning algorithms allowed for the validation of the previously made estimates of the energy potential in the studied region. Results The calculation results suggested an increase in production as it was confirmed by the 2019 measures. The findings obtained from the case study region were reliable and seemed to be very promising. The results obtained using deep learning algorithms were similar to those produced by conventional calculation methods. However, while conventional approaches based on measurements obtained using hardware solutions (ground sensors) are expensive and very difficult to implement, the suggested new approach is cheaper and more convenient. Conclusions In the existence of a protracted controversy over the hypothetical effects of climate change, making advances in artificial intelligence and using new deep learning algorithms are critical procedures to strengthening conventional assessment tools of the production sites of photovoltaic energy and CSP plants.

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Prediction of monthly mean daily global solar radiation using Artificial Neural Network

Cited by 30 publications

References 17 publications

Solar Energy Potential Estimation in Perak Using Clearness Index and Artificial Neural Network

Solar Energy Potential Estimation in Perak Using Clearness Index and Artificial Neural Network

Model Building for Regional Ecological Risk Prediction and Evaluation of Prediction Accuracy

Deep learning-based estimation of PV power plant potential under climate change: a case study of El Akarit, Tunisia

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