Global solar radiation prediction over North Dakota using air temperature: Development of novel hybrid intelligence model

Tao, Hai; Ewees, Ahmed A.; Al-Sulttani, Ali Omran; Beyaztaş, Ufuk; Hameed, Mohammed Majeed; Salih, Sinan Q.; Armanuos, Asaad M.; Al‐Ansari, Nadhir; Voyant, Cyril; Shahid, Shamsuddin; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher

doi:10.1016/j.egyr.2020.11.033

Cited by 73 publications

(27 citation statements)

References 57 publications

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“…Artificial Intelligence (AI) technique has been applied in the recent years to predict performance improvement in SIP with regards to its capacity for simulating nonlinear and complex relations and manage the lost information [6,7]. Various AI methods have been proposed earlier to predict SR methods like data mining, Fuzzy Logic (FL), support vector regression, genetic programming, regression tree, and Artificial Neural Network (ANN).…”

Section: Role Of Artificial Intelligence In Solar Irradiation Predictionmentioning

confidence: 99%

Artificial Intelligence Based Solar Radiation Predictive Model Using Weather Forecasts

Khan¹,

Abbas²,

Afaq³

et al. 2022

Computers, Materials &Amp; Continua

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Solar energy has gained attention in the past two decades, since it is an effective renewable energy source that causes no harm to the environment. Solar Irradiation Prediction (SIP) is essential to plan, schedule, and manage photovoltaic power plants and grid-based power generation systems. Numerous models have been proposed for SIP in the literature while such studies demand huge volumes of weather data about the target location for a lengthy period of time. In this scenario, commonly available Artificial Intelligence (AI) technique can be trained over past values of irradiance as well as weatherrelated parameters such as temperature, humidity, wind speed, pressure, and precipitation. Therefore, in current study, the authors aimed at developing a solar irradiance prediction model by integrating big data analytics with AI models (BDAAI-SIP) using weather forecasting data. In order to perform long-term collection of weather data, Hadoop MapReduce tool is employed. The proposed solar irradiance prediction model operates on different stages. Primarily, data preprocessing take place using various sub processes such as data conversion, missing value replacement, and data normalization. Besides, Elman Neural Network (ENN), a type of feedforward neural network is also applied for predictive analysis. It is divided into input layer, hidden layer, loadbearing layer, and output layer. To overcome the insufficiency of ENN in choosing the value of weights and hidden layer neuron count, Mayfly Optimization (MFO) algorithm is applied. In order to validate the performance of the proposed model, a series of experiments was conducted. The experimental

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Section: Role Of Artificial Intelligence In Solar Irradiation Predictionmentioning

confidence: 99%

Artificial Intelligence Based Solar Radiation Predictive Model Using Weather Forecasts

Khan¹,

Abbas²,

Afaq³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

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“…In an ANFIS model structure with multiple rules, several inputs, and one output, the nodes in the same layer perform the same role. e output of the i th node in the first layer is selected when the input membership functions (MFs) are contained in the first layer and the input values are delivered to the subsequent layer [55,56]. e generalized bell function must have the best nonlinear parameters categorization capability for it to be used.…”

Section: Complexitymentioning

confidence: 99%

Optimized Adaptive Neuro‐Fuzzy Inference System Using Metaheuristic Algorithms: Application of Shield Tunnelling Ground Surface Settlement Prediction

et al. 2021

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Deformation of ground during tunnelling projects is one of the complex issues that is required to be monitored carefully to avoid the unexpected damages and human losses. Accurate prediction of ground settlement (GS) is a crucial concern for tunnelling problems, and the adequate predictive model can be a vital tool for tunnel designers to simulate the ground settlement accurately. This study proposes relatively new hybrid artificial intelligence (AI) models to predict the ground settlement of earth pressure balance (EPB) shield tunnelling in the Bangkok MRTA project. The predictive models were various nature-inspired frameworks, such as differential evolution (DE), particle swarm optimization (PSO), genetic algorithm (GA), and ant colony optimizer (ACO) to tune the adaptive neuro-fuzzy inference system (ANFIS). To obtain the accurate and reliable results, the modeling procedure is established based on four different dataset scenarios including (i) preprocessed and normalized (PPN), (ii) preprocessed and nonnormalized (PPNN), (iii) non-preprocessed and normalized (NPN), and (iv) non-preprocessed and nonnormalized (NPNN) datasets. Results indicated that PPN dataset scenario significantly affected the prediction models in terms of their perdition accuracy. Among all the developed hybrid models, ANOFS-PSO model achieved the best predictability performance. In quantitative terms, PPN-ANFIS-PSO model attained the least root mean square error value (RMSE) of 7.98 and a correlation coefficient value (CC) of 0.83. Overall, the attained results confirmed the superiority of the explored hybrid AI models as robust predictive model for ground settlement of earth pressure balance (EPB) shield tunnelling.

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“…Para la predicción de radiación solar, el histórico de datos utilizado consta de variables observadas mediante estaciones meteorológicas desplegadas sobre un territorio o región (Carrera and Kim 2020;Kim et al 2019), de imágenes de satélite (Babar et al 2019;Ghimire et al 2019), de imágenes del cielo (Pedro et al 2019) (tomadas desde la superficie para analizar la claridad de la atmósfera) y modelos físicos (Yang et al 2019). En las aproximaciones al problema se han usado las fuentes de datos de forma independiente o mezclando variables procedentes de las diferentes fuentes (Tao et al 2021;Aguiar et al 2016;Lotfi et al 2020).…”

Section: Trabajos Relacionadosunclassified

Arquitectura Neuronal Para Predicción De Radiación Solar en Base a Variables Meteorológicas

Benavides

Manso

2021

Proceedings - 3rd Congress in Geomatics Engineering - CIGeo

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La predicción de la radiación solar presenta gran interés, debido al creciente uso de las energías renovables, comoalternativa a los problemas globales que plantean las fuentes de energía actuales. En la última década se han logradoimportantes avances, gracias a la introducción de resultados de otras áreas del conocimiento como la Inteligencia Artificial.En particular el aprendizaje profundo con las numerosas variantes de redes neuronales. Proponemos una solución basadaen clasificación supervisada que combina en un modelo, una capa convolucional con una capa GRU (Gated RecurrentUnits por sus siglas en inglés). Las clases se definen como intervalos de valores de radiación solar. Los objetos serepresentan a través de valores de temperatura ambiente, velocidad del viento, presión atmosférica y precipitación. Sedispone de un conjunto de datos de 112 estaciones con miles de registros de un período de dos años, tomados deestaciones meteorológicas distribuidas en todo el territorio de Galicia. Se ha empleado la métrica Macro-F1 para evaularla bondad de la clasificación porque las clases están desbalanceadas.

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Global solar radiation prediction over North Dakota using air temperature: Development of novel hybrid intelligence model

Cited by 73 publications

References 57 publications

Artificial Intelligence Based Solar Radiation Predictive Model Using Weather Forecasts

Artificial Intelligence Based Solar Radiation Predictive Model Using Weather Forecasts

Optimized Adaptive Neuro‐Fuzzy Inference System Using Metaheuristic Algorithms: Application of Shield Tunnelling Ground Surface Settlement Prediction

Arquitectura Neuronal Para Predicción De Radiación Solar en Base a Variables Meteorológicas

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