Prediction of global solar radiation by artificial neural network based on a meteorological environmental data

Díaz-Gómez, J.A.; Parrales, A.; Álvarez, A.; Martı́nez, Susana Silva; Colorado, D.; Hernández, J.A.

doi:10.1080/19443994.2014.939861

Cited by 12 publications

(3 citation statements)

References 27 publications

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“…The superscripts i, h, and o represent the input, hidden, and output layers, respectively. The subscripts k, m, n are the input, hidden, and output neurons, respectively [27].…”

Section: Ann Modified Wilson Plot Methodologymentioning

confidence: 99%

Heat Transfer Coefficients Analysis in a Helical Double-Pipe Evaporator: Nusselt Number Correlations through Artificial Neural Networks

Parrales

Hernández-Pérez

Flores

et al. 2019

Entropy

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In this study, two empirical correlations of the Nusselt number, based on two artificial neural networks (ANN), were developed to determine the heat transfer coefficients for each section of a vertical helical double-pipe evaporator with water as the working fluid. Each ANN was obtained using an experimental database of 1109 values obtained from an evaporator coupled to an absorption heat transformer with energy recycling. The Nusselt number in the annular section was estimated based on the modified Wilson plot method solved by an ANN. This model included the Reynolds and Prandtl numbers as input variables and three neurons in their hidden layer. The Nusselt number in the inner section was estimated based on the Rohsenow equation, solved by an ANN. This ANN model included the numbers of the Prandtl and Jackob liquids as input variables and one neuron in their hidden layer. The coefficients of determination were R 2 > 0.99 for both models. Both ANN models satisfied the dimensionless condition of the Nusselt number. The Levenberg-Marquardt algorithm was chosen to determine the optimum values of the weights and biases. The transfer functions used for the learning process were the hyperbolic tangent sigmoid in the hidden layer and the linear function in the output layer. The Nusselt numbers, determined by the ANNs, proved adequate to predict the values of the heat transfer coefficients of a vertical helical double-pipe evaporator that considered biphasic flow with an accuracy of ±0.2 for the annular Nusselt and ±4 for the inner Nusselt.Generally, in the case of heat exchangers with helical pipelines, it is known that the flow behavior has a greater complexity compared to exchangers that include straight pipes [2]. Due to the generation of vortices, induced by the interaction between the viscous forces and the centrifugal force, a disturbance in the flow is generated, which increases the rate of the heat transfer and the friction losses-a behavior referred to as secondary flow [3].If a helical exchanger transports a biphasic flow, specifically gas-liquid, the complexity is even higher due to the sudden changes in the flow pattern. These phenomena are mainly caused by the sliding between the phases, the orientation of the pipe, and the void fraction, among other additional variables and the relationships between them [4]. Therefore, to know and understand these phenomena, it is necessary to estimate the values of the heat transfer coefficient in the helical exchangers that contain biphasic flows, and hence any method that leads to their calculation is considered useful.One of the methods commonly used to estimate heat transfer coefficients is the Wilson plot method, proposed by Wilson in 1915 and subsequently modified in 1957 [5]. This traditional method quantifies the heat flow for each section by the exchanger through the measurement of the global temperature between the two fluids involved and their rate of heat transfer [6]. One of the most significant advantages of this technique is that it avoids the complex dire...

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“…The superscripts i, h, and o represent the input, hidden, and output layers, respectively. The subscripts k, m, n are the input, hidden, and output neurons, respectively [27].…”

Section: Ann Modified Wilson Plot Methodologymentioning

confidence: 99%

Heat Transfer Coefficients Analysis in a Helical Double-Pipe Evaporator: Nusselt Number Correlations through Artificial Neural Networks

Parrales

Hernández-Pérez

Flores

et al. 2019

Entropy

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show abstract

“…Such a method like this has proven to be beneficial in a wide range of scenarios with a big number of inputs. In both linear and nonlinear problems, ANN models can respond well [59], outperforming traditional empirical models [60], [61]. As such ANNs have also been used for solar PV forecasting as seen other works of literature such as [61]- [63].…”

Section: I) Artificial Neural Network (Anns)mentioning

confidence: 99%

Machine Learning Based Solar Photovoltaic Power Forecasting: A Review and Comparison

et al. 2023

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The growing interest in renewable energy and the falling prices of solar panels place solar electricity in a favourable position for adoption. However, the high-rate adoption of intermittent renewable energy introduces challenges and the potential to create power instability between the available power generation and the load demand. Hence, accurate solar Photovoltaic (PV) power forecasting is essential to maintain system reliability and maximize renewable energy integration. The current solar PV power forecasting approaches are an essential tool to maintain system reliability and maximize renewable energy integration. This paper presents a comprehensive and comparative review of existing Machine Learning (ML) based approaches used in PV power forecasting, focusing on short-term horizons. We provide an overview of factors affecting solar PV power forecasting and an overview of existing PV power forecasting methods in the literature, with a specific focus on ML-based models. To further enhance the comparison and provide more insights into the advancement in the area, we simulate the performance of different ML methods used in solar PV power forecasting and, finally, a discussion on the results of the work.

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“…Modelling and forecasting solar radiation at different scales: monthly [78,79], daily [80,81], and hourly [82]. One of the biggest problems of this type of SE is that solar radiation depends on climatic factors that are difficult to forecast accurately, such as temperature, humidity, wind speed, and daylight duration [83]. In addition, there is a lack of accurate data about climatic variables [80].…”

mentioning

confidence: 99%

Intelligence Techniques in Sustainable Energy: Analysis of a Decade of Advances

Velásquez,

Cadavid,

Franco

2023

Energies

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In the last decade, many artificial intelligence (AI) techniques have been used to solve various problems in sustainable energy (SE). Consequently, an increasing volume of research has been devoted to this topic, making it difficult for researchers to keep abreast of its developments. This paper analyzes 18,715 articles—about AI techniques used for SE—indexed in Scopus and published from 2013 to 2022, which were retrieved and selected following a novel iterative methodology. Besides calculating basic bibliometric indicators, we used clustering techniques and a co-occurrence analysis of author keywords to discover and characterize dominant themes in the literature. As a result, we found eight dominant themes in SE (solar energy, smart grids and microgrids, fuel cells, hydrogen, electric vehicles, biofuels, wind energy, and energy planning) and nine dominant techniques in AI (genetic algorithms, support vector machines, particle swarm optimization, differential evolution, classical neural networks, fuzzy logic controllers, reinforcement learning, deep learning, and multi-objective optimization). Each dominant theme is discussed in detail, highlighting the most relevant work and contributions. Finally, we identified the AI techniques most widely used in each SE area to solve its specific problems.

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Prediction of global solar radiation by artificial neural network based on a meteorological environmental data

Cited by 12 publications

References 27 publications

Heat Transfer Coefficients Analysis in a Helical Double-Pipe Evaporator: Nusselt Number Correlations through Artificial Neural Networks

Heat Transfer Coefficients Analysis in a Helical Double-Pipe Evaporator: Nusselt Number Correlations through Artificial Neural Networks

Machine Learning Based Solar Photovoltaic Power Forecasting: A Review and Comparison

Intelligence Techniques in Sustainable Energy: Analysis of a Decade of Advances

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