Using artificial neural networks to estimate solar radiation in Kuwait

Bou-Rabee, Mohammed A.; Sulaiman, Shaharin Anwar; Saleh, Magdy Saad; Marafi, Suhaila

doi:10.1016/j.rser.2017.01.013

Cited by 84 publications

(46 citation statements)

References 11 publications

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“…Since OS-ELM involves the calculation of inversion during the update learning process, once the autocorrelation matrix of the hidden layer output matrix is singular or ill-conditioned, the generalization ability of OS-ELM will be severely degraded. Therefore, Huynh [16] combined Tikhonov regularization with OS-ELM and proposed a regularization of OS-ELM to improve the stability and generalization of the algorithm. To address the role of new samples in the process of online learning, the concept of the forgetting factor [43] was introduced to OS-ELM to strengthen the role of new samples by forgetting old samples, so that the updated predictive model is closer to the current state of the time-varying system.…”

Section: Given a Training Set Withmentioning

confidence: 99%

“…Hence, the motivation of scientists is encouraged to find new alternative modeling strategies to solve this problem.The application of intelligence models presented in the form of artificial intelligence (AI) models have been introduced for solar radiation prediction since [14]. Several AI versions have been conducted to simulate the actual pattern of solar radiation, including an artificial neural network [15][16][17][18], fuzzy set models [19][20][21], genetic programming [22][23][24][25], and complementary models [26][27][28][29][30]. Although there has been massive implementation of the AI models, multiple drawbacks are associated with these models, such as poor prediction for a dataset which is not in range of the learning values, the incorporation of error through the modeling phase, and the requirement of long-time series data for model training, testing and tuning of the multiple internal parameters.…”

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Global Solar Radiation Prediction Using Hybrid Online Sequential Extreme Learning Machine Model

et al. 2018

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Accurate global solar radiation prediction is highly essential for related research on renewable energy sources. The cost implication and measurement expertise of global solar radiation emphasize that intelligence prediction models need to be applied. On the basis of long-term measured daily solar radiation data, this study uses a novel regularized online sequential extreme learning machine, integrated with variable forgetting factor (FOS-ELM), to predict global solar radiation at Bur Dedougou, in the Burkina Faso region. Bayesian Information Criterion (BIC) is applied to build the seven input combinations based on speed (W speed ), maximum and minimum temperature (T max and T min ), maximum and minimum humidity (H max and H min ), evaporation (E o ) and vapor pressure deficiency (V PD ). For the difference input parameters magnitudes, seven models were developed and evaluated for the optimal input combination. Various statistical indicators were computed for the prediction accuracy examination. The experimental results of the applied FOS-ELM model demonstrated a reliable prediction accuracy against the classical extreme learning machine (ELM) model for daily global solar radiation simulation. In fact, compared to classical ELM, the FOS-ELM model reported an enhancement in the root mean square error (RMSE) and mean absolute error (MAE) by (68.8-79.8%). In summary, the results clearly confirm the effectiveness of the FOS-ELM model, owing to the fixed internal tuning parameters. Energies 2018, 11, 3415 2 of 19Moreover, several studies have investigated the relationship of the energy level of several countries to their levels of development [2]. Consequently, the findings of these studies have reported a positive relationship of energy utilization with living standards. The level of energy usage in the developed nations is lower. Advanced countries are significantly consuming the energy, which greatly affects the levels of development in backward and developing countries [3].Since energy is the main factor that drives all developmental processes, its effect on human, crops, plants, animals, and ecosystems depends on the form of energy employed [4,5]. Studies have reported two forms of available energy on Earth (renewable and non-renewable energies) [6]. The method employed in harnessing and applying these forms of energy for human use is very important in the way they affect humankind and the immediate environment. The uncontrolled utilization of the nonrenewable form of energy has been linked to several factors such as climate change, global warming, environmental pollution, and other economic and environmental hazards. Similarly, the renewable form of energy, which is globally available at varying levels, ought to be given attention. This form of energy can be obtained from natural processes, and therefore posits minimal or zero impact on humans and their immediate environment.Renewable energy sources have attracted much research interest in the 21st century due to the discoveries of the impact of non-renewable en...

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Section: Given a Training Set Withmentioning

confidence: 99%

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confidence: 99%

Global Solar Radiation Prediction Using Hybrid Online Sequential Extreme Learning Machine Model

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“…Bou-Rabee ve arkadaşları, Kuveyt için günlük ortalama güneş ışınım değerlerini tahmin eden bir modeli yapay sinir ağlarını kullanarak geliştirmişlerdir [32]. Vakili ve arkadaşları, meteorolojik verilere bağlı yapay sinir ağları kullanılarak yapılan günlük ışınım değerlerinin tahmininde partiküller madde kirliliğinin etkisini inceleyerek tahmin modelinin verimliliğini arttırmışlardır [33].…”

Section: Gi̇ri̇ş (Introduction)unclassified

Mersin / Türkiye için Ölçülen Global Güneş Işınımının Yapay Sinir Ağları ile Tahmin Edilmesi ve Yaygın Işınım Modelleri ile Karşılaştırılması

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Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

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ÖzBu çalışmada, Nisan 2017 -Mart 2018 tarihleri aralığında Mersin için ölçülen günlük toplam global güneş ışınım değerlerinin yapay sinir ağı kullanılarak modellenmesi yapılmıştır ve literatürde bulunan yaygın modellerin günlük toplam global güneş ışınım değerlerini tahmin etme performansları incelenmiştir. Günlük ortalama hava sıcaklığı, bağıl nem, rüzgar hızı, güneşlenme süresi ve bulut kapalılığı verileri, Devlet Meteoroloji İşleri Genel Müdürlüğü'nden temin edilmiş olup güneş ışınım değerleri ise piranometre ile ölçülmüştür. Sonuç olarak, incelenen modeller içerisinde en iyi tahmin performansını belirlilik katsayısı (R 2 ) 0,83 olan Model 37 (Zhang ve Huang) göstermiştir. AbstractIn this study, the daily total global solar radiation values measured for Mersin between April 2017 and March 2018 were modeled using artificial neural networks and the performance of estimating daily total global solar radiation values of the common models in the literature was investigated. Daily average air temperature, relative humidity, wind speed, sunshine duration, and cloud cover data are obtained from the Turkish State Meteorological Service and solar radiation values are measured with a pyranometer. As a result, Model 37 (Zhang and Huang) showed the best prediction performance among the models examined, with the coefficient of determination (R 2 ) being 0.83.

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“…Artificial neural networks are recognized by scientists as efficient forecasting tools as there are a lot of scientific papers that demonstrate their superiority for the forecasting accuracy when compared to other methods like statistical ones [10][11][12][13]. There are many papers that cover a wide range of artificial neural network implementations that prove their undeniable advantages in terms of performing specific tasks efficiently, with very good results [14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers

et al. 2017

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This paper focuses on an important issue regarding the forecasting of the hourly energy consumption in the case of large electricity non-household consumers that account for a significant percentage of the whole electricity consumption, the accurate forecasting being a key-factor in achieving energy efficiency. In order to devise the forecasting solutions, we have developed a series of dynamic neural networks for solving nonlinear time series problems, based on the non-linear autoregressive (NAR) and non-linear autoregressive with exogenous inputs (NARX) models. In both cases, we have used large datasets comprising the hourly energy consumption recorded by the smart metering device from a commercial center type of consumer (a large hypermarket), while in the NARX case we have used supplementary temperature and time stamps datasets. Of particular interest was to research and obtain an optimal mix between the training algorithm (Levenberg-Marquardt, Bayesian Regularization, Scaled Conjugate Gradient), the hidden number of neurons and the delay parameter. Using performance metrics and forecasting scenarios, we have obtained results that highlight an increased accuracy of the developed forecasting solutions. The developed hourly consumption forecasting solutions can bring significant benefits to both the consumers and electricity suppliers.

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Using artificial neural networks to estimate solar radiation in Kuwait

Cited by 84 publications

References 11 publications

Global Solar Radiation Prediction Using Hybrid Online Sequential Extreme Learning Machine Model

Global Solar Radiation Prediction Using Hybrid Online Sequential Extreme Learning Machine Model

Mersin / Türkiye için Ölçülen Global Güneş Işınımının Yapay Sinir Ağları ile Tahmin Edilmesi ve Yaygın Işınım Modelleri ile Karşılaştırılması

Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers

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