Prediction of hourly solar radiation in Tamil Nadu using ANN model with different learning algorithms

Geetha, A.; Santhakumar, J.; Sundaram, K. Mohana; Usha, S.; Thentral, T. M. Thamizh; Boopathi, C. S.; Ramya, R.; Sathyamurthy, Ravishankar

doi:10.1016/j.egyr.2021.11.190

Cited by 40 publications

(12 citation statements)

References 13 publications

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“…An artificial neural network (ANN) is an empirical model that mimics the function of a human neural network. There is often a highly nonlinear mapping relationship between its input and output, and it is generally difficult to write its expression, so it is called "black box" [28].The characteristic of ANN is that it can store information or knowledge distribution in a large number of neurons or the whole system. It has the potential of self-learning and self-organization [65].…”

Section: Artificial Neural Network (Ann) Theorymentioning

confidence: 99%

“…So in this study, the back propagation algorithm (BP algorithm) is used, and the BP neural network model has the advantages of fast calculation speed and efficient solution. It can better simulate the process of comprehensive evaluation by evaluation experts [28,37]. In order to simulate the process of comprehensive evaluation by experts, considering the input, hidden and output layers, a BP neural network is designed.…”

Section: Artificial Neural Network (Ann) Theorymentioning

confidence: 99%

“…The neurons of each hidden layer basically perform two tasks: (1) the weighted sum of all process inputs, and (2) the weighted sum of the nonlinear transformation of the neuron transfer function produces the output of each neuron. Common transfer functions include hyperbolic tangent S-type (Tansig), S-type (Logsig), positive linear (Poslin), and purelin linear (Purelin) transfer functions, as shown in Formulas ( 11)-( 14) [28,67]. The output layer is used for use error estimates to predict outcomes.…”

Section: Artificial Neural Network (Ann) Theorymentioning

confidence: 99%

“…According to the characteristics of each index, the input factor is graded, the input value of the neural network is determined by the expert evaluation method, and the expected output value of the artificial neural network is obtained by the FCE method. After grading each factor, each score value is normalized into the score value in the (0,1) domain to meet the requirements of the BP neural network range [28,66].…”

Section: Artificial Neural Network (Ann) Theorymentioning

confidence: 99%

“…Artificial neural network (ANN) has the ability to learn to predict behaviors and patterns from a limited set of correct data to make statistics and predict future develop-ments [27,28]. There are many types of ANN algorithms, such as BP, RBF, MLP, GMDH, GA etc.…”

Section: Introductionmentioning

confidence: 99%

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An Intelligent Site Selection Model for Hydrogen Refueling Stations Based on Fuzzy Comprehensive Evaluation and Artificial Neural Network—A Case Study of Shanghai

Zhou

Qin

et al. 2022

Energies

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With the gradual popularization of hydrogen fuel cell vehicles (HFCVs), the construction and planning of hydrogen refueling stations (HRSs) are increasingly important. Taking operational HRSs in China’s coastal and major cities as examples, we consider the main factors affecting the site selection of HRSs in China from the three aspects of economy, technology and society to establish a site selection evaluation system for hydrogen refueling stations and determine the weight of each index through the analytic hierarchy process (AHP). Then, combined with fuzzy comprehensive evaluation (FCE) method and artificial neural network model (ANN), FCE method is used to evaluate HRS in operation in China's coastal areas and major cities, and we used the resulting data obtained from the comprehensive evaluation as the training data to train the neural network. So, an intelligent site selection model for HRSs based on fuzzy comprehensive evaluation and artificial neural network model (FCE-ANN) is proposed. The planned HRSs in Shanghai are evaluated, and an optimal site selection of the HRS is obtained. The results show that the optimal HRSs site selected by the FCE-ANN model is consistent with the site selection obtained by the FCE method, and the accuracy of the FCE-ANN model is verified. The findings of this study may provide some guidelines for policy makers in planning the hydrogen refueling stations.

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Section: Artificial Neural Network (Ann) Theorymentioning

confidence: 99%

Section: Artificial Neural Network (Ann) Theorymentioning

confidence: 99%

Section: Artificial Neural Network (Ann) Theorymentioning

confidence: 99%

Section: Artificial Neural Network (Ann) Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Intelligent Site Selection Model for Hydrogen Refueling Stations Based on Fuzzy Comprehensive Evaluation and Artificial Neural Network—A Case Study of Shanghai

Zhou

Qin

et al. 2022

Energies

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A novel soft sensing method using intelligent modeling method for solar irradiance and temperature in distributed PV power plant

Zhu,

Jiang,

Zhou

et al. 2024

Energy Science & Engineering

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Distributed photovoltaic (PV) power plants often lack solar irradiance monitoring devices, significantly hindering crucial functions such as power forecasting, fault diagnosis, and performance calculation for distributed PV. To address this issue, a real‐time method for soft sensing solar irradiance was proposed in distributed PV. First, we investigated the typical relationship between solar irradiance, ambient temperature, and the electrical characteristics of PV cells. Based on this relationship, we utilized the small sample modeling technique of the Genetic Algorithm‐Support Vector Machine to calculate the ambient temperature. Subsequently, a solar irradiance calculation model based on the backpropagation neural network was developed, taking the PV array voltage, current, calculated ambient temperature, and power as inputs. This approach enables for the estimation of solar irradiance in distributed PV power plants through a simple and efficient calculation process. To demonstrate the reliability and flexibility of the algorithm, we conducted testing with data under various input conditions, such as different power plant configurations, and seasons, coefficient of determination for the proposed model reached 0.95. Overall, the novelty of the proposed method offers a practical solution for soft sensing of solar irradiance in PV power plants, enabling accurate performance analysis and effective operation management without hardware investment.

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Feasibility assessment and prioritization of renewable energy resources: towards a energy transition for the society and the environment—a case study approach

Percy,

Edwin

2023

Environ Dev Sustain

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Prediction of hourly solar radiation in Tamil Nadu using ANN model with different learning algorithms

Cited by 40 publications

References 13 publications

An Intelligent Site Selection Model for Hydrogen Refueling Stations Based on Fuzzy Comprehensive Evaluation and Artificial Neural Network—A Case Study of Shanghai

An Intelligent Site Selection Model for Hydrogen Refueling Stations Based on Fuzzy Comprehensive Evaluation and Artificial Neural Network—A Case Study of Shanghai

A novel soft sensing method using intelligent modeling method for solar irradiance and temperature in distributed PV power plant

Feasibility assessment and prioritization of renewable energy resources: towards a energy transition for the society and the environment—a case study approach

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