Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison

Ağbulut, Ümit; Gürel, Ali Etem; Biçen, Yunus

doi:10.1016/j.rser.2020.110114

Cited by 207 publications

(63 citation statements)

References 77 publications

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“…If MBE value is positive, then it means that mean of predicted values is higher than that of actual values. If it is negative, it means that mean of predicted values is smaller than that of actual values [22]. With this viewpoint, all results in terms of MBE metric are positive and varied from 0.014 to 1.1356 (See Table 4).…”

Section: Resultsmentioning

confidence: 94%

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ANN Based Prediction of Engine Performance and Exhaust Emission Responses of a CI Engine Powered By Ternary Blends

Karagöz

2020

International Journal of Automotive Science and Technology

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In this study, experimental data was gathered from a single cylinder diesel engine fuelled with pyrolytic oil, neat diesel and butanol fuel blends. The experiments were conducted at varying engine loads from 0.25 kW to 1 kW with the interval of 0.25 kW. The engine performance and emission data obtained were predicted using an artificial neural network (ANN) algorithm. Assessed responses are CO, NOx, BSFC, and BTE. The results were discussed in terms of R2, MBE, and RMSE metrics. The performance and emission responses were predicted with a good R2 value of 0.986, 0.963, 0.991, and 0967 for BTE, BSFC, NOx, and CO, respectively, and all MBE value is very close to zero and smaller than 1.14. In the conclusion, the present paper showed that the ternary form of n-butanol-pyrolytic fuel and diesel fuel can be used in a CI engine with no modification on the vehicular system and the emission and performance responses of ternary fuels can be accurately predicted using an artificial neural network.

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Section: Resultsmentioning

confidence: 94%

“…These are R 2 , RMSE and MBE. Table 3 gives the equations and desired cases of these metrics [19,21,22]. Table 3.…”

Section: Evaluation Metricsmentioning

confidence: 99%

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ANN Based Prediction of Engine Performance and Exhaust Emission Responses of a CI Engine Powered By Ternary Blends

Karagöz

2020

International Journal of Automotive Science and Technology

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“…A review of solar irradiance forecasting using four different machine learning techniques (artificial neural network (ANN), support vector machine (SVM), k-nearest neighbor (k-NN), and deep learning (DL)) are presented in [61]. The comparison between the different techniques identified that ANN algorithm provided the best fitting for the data, followed by the DL, SVM, and k-NN techniques.…”

Section: Ai For Solar Irradiance Forecastingmentioning

confidence: 99%

A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems

et al. 2021

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The use of artificial intelligence (AI) is increasing in various sectors of photovoltaic (PV) systems, due to the increasing computational power, tools and data generation. The currently employed methods for various functions of the solar PV industry related to design, forecasting, control, and maintenance have been found to deliver relatively inaccurate results. Further, the use of AI to perform these tasks achieved a higher degree of accuracy and precision and is now a highly interesting topic. In this context, this paper aims to investigate how AI techniques impact the PV value chain. The investigation consists of mapping the currently available AI technologies, identifying possible future uses of AI, and also quantifying their advantages and disadvantages in regard to the conventional mechanisms.

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“…One of the key features of ML is a training model using different dependent and independent attributes, which further depends on the type of learning algorithm (supervised, semisupervised, or unsupervised). ML algorithms are widely used for prediction purposes and applied to provide solutions to questions such as global solar radiation [9], accuracy in determining the mortality rate in COVID-19 patients [10], and efficient processes for manufacturing industries [11]. In addition, ML algorithms assist the educational sector to evaluate student performance [12], forecast student dropout rates in any course [13], and understand students' unique learning styles [14].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

et al. 2021

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Electronic learning management systems provide live environments for students and faculty members to connect with their institutional online portals and perform educational activities virtually. Although modern technologies proactively support these online sessions, students’ active participation remains a challenge that has been discussed in previous research. Additionally, one concern for both parents and teachers is how to accurately measure student performance using different attributes collected during online sessions. Therefore, the research idea undertaken in this study is to understand and predict the performance of the students based on features extracted from electronic learning management systems. The dataset chosen in this study belongs to one of the learning management systems providing a number of features predicting student’s performance. The integrated machine learning model proposed in this research can be useful to make proactive and intelligent decisions according to student performance evaluated through the electronic system’s data. The proposed model consists of five traditional machine learning algorithms, which are further enhanced by applying four ensemble techniques: bagging, boosting, stacking, and voting. The overall F1 scores of the single models are as follows: DT (0.675), RF (0.777), GBT (0.714), NB (0.654), and KNN (0.664). The model performance has shown remarkable improvement using ensemble approaches. The stacking model by combining all five classifiers has outperformed and recorded the highest F1 score (0.8195) among other ensemble methods. The integration of the ML models has improved the prediction ratio and performed better than all other ensemble approaches. The proposed model can be useful for predicting student performance and helping educators to make informed decisions by proactively notifying the students.

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Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison

Cited by 207 publications

References 77 publications

ANN Based Prediction of Engine Performance and Exhaust Emission Responses of a CI Engine Powered By Ternary Blends

ANN Based Prediction of Engine Performance and Exhaust Emission Responses of a CI Engine Powered By Ternary Blends

A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

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