Comparison of artificial intelligence techniques via empirical equations for prediction of solar radiation

Çıtakoğlu, Hatice

doi:10.1016/j.compag.2015.08.020

Cited by 106 publications

(34 citation statements)

References 37 publications

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“…The proposed model generated good results during the testing stage and provided better performance compared with the MLP and ANFIS models. Hatice Citakoglu (2015) employed the adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), multiple linear regression (MLR), and four empirical equations for prediction of the solar radiation (SR) in Turkey (Citakoglu 2015). The author showed that the ANN method has a better performance to predict the SR than the other empirical equations.…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Forecasting Daily Global Solar Radiation in Hot Semi-Arid Climate Using a Novel Hybrid Machine Learning Paradigm

Jamei¹,

Ahmadianfar²,

Jamei³

et al. 2021

Preprint

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Solar energy is one of the most important renewable energy sources. Assessing the solar potential of area needs analyzed information about the dataset of the measured global solar radiation (GSR). Recently, researches detected the high potential of state-of-the-art artificial intelligence (AI) methods in estimating the GSR successfully. In this study, a novel hybrid AI-based tool consisting of least square support vector machine (LSSVM) integrated with improved simulated annealing (ISA) is proposed to predict the GSR over the Ahvaz synoptic station located in the South-West of Iran. The potential of the proposed hybrid paradigm so-called LSSVM-ISA was evaluated by using multivariate adaptive regression spline (MARS), generalization regression neural network (GRNN), and multivariate linear regression with interactions (MLRI). For precise assessment of efficiency of the AI models, various statistical metrics and validation methods were used to assess the precision of the developed models. A comparison of the obtained results indicated that the LSSVM-ISA method performed better than the MARS, GRNN, and MLRI models. The achieved RMSE values of the MARS, GRNN, and MLRI models were decreased by 9%, 16%, and 30% using the LSSVM-ISA model. Finally, the results demonstrated that the LSSVM-ISA model could be successfully employed for accurately predicting GSR.

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Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Forecasting Daily Global Solar Radiation in Hot Semi-Arid Climate Using a Novel Hybrid Machine Learning Paradigm

Jamei¹,

Ahmadianfar²,

Jamei³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…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.…”

mentioning

confidence: 99%

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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“…This great influence of the time of year in the incident solar radiation value over the Earth's surface, when thinking about the simulation, was also mentioned by different authors. Citakoglu [4] worked at a monthly temporary scale for the average solar irradiation estimation in Turkey, finding that the most significant explanatory variable was the month, and thus, the ANN model that included this variable behaved better than the multiple linear regression model, the adaptive network-fuzzy inference system model, and the tested empirical equations (Abdalla, Ångström, Bahel, and Hargreaves-Samani).…”

Section: Discussionmentioning

confidence: 99%

“…Many computer simulation models, which predict growth, development, and yield of agronomic and horticultural crops, require daily weather data as input. Evapotranspiration, which is very important for applications in agriculture and environment, can be calculated as a function of several meteorological data including solar radiation [3,4].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Horizontal Daily Global Solar Irradiation Using Artificial Neural Networks (ANNs) in the Castile and León Region, Spain

et al. 2020

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This article evaluates horizontal daily global solar irradiation predictive modelling using artificial neural networks (ANNs) for its application in agricultural sciences and technologies. An eight year data series (i.e., training networks period between 2004–2010, with 2011 as the validation year) was measured at an agrometeorological station located in Castile and León, Spain, owned by the irrigation advisory system SIAR. ANN models were designed and evaluated with different neuron numbers in the input and hidden layers. The only neuron used in the outlet layer was the global solar irradiation simulated the day after. Evaluated values of the input data were the horizontal daily global irradiation of the current day [H(t)] and two days before [H(t−1), H(t−2)], the day of the year [J(t)], and the daily clearness index [Kt(t)]. Validated results showed that best adjustment models are the ANN 7 model (RMSE = 3.76 MJ/(m2·d), with two inputs ([H(t), Kt(t)]) and four neurons in the hidden layer) and the ANN 4 model (RMSE = 3.75 MJ/(m2·d), with two inputs ([H(t), J(t)]) and two neurons in the hidden layer). Thus, the studied ANN models had better results compared to classic methods (CENSOLAR typical year, weighted moving mean, linear regression, Fourier and Markov analysis) and are practically easier as they need less input variables.

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Comparison of artificial intelligence techniques via empirical equations for prediction of solar radiation

Cited by 106 publications

References 37 publications

WITHDRAWN: Forecasting Daily Global Solar Radiation in Hot Semi-Arid Climate Using a Novel Hybrid Machine Learning Paradigm

WITHDRAWN: Forecasting Daily Global Solar Radiation in Hot Semi-Arid Climate Using a Novel Hybrid Machine Learning Paradigm

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

Prediction of Horizontal Daily Global Solar Irradiation Using Artificial Neural Networks (ANNs) in the Castile and León Region, Spain

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