Short-Term Prediction of Global Solar Radiation Energy Using Weather Data and Machine Learning Ensembles: A Comparative Study

Al-Hajj, Rami; Assi, Ali; Fouad, Mohamad M.

doi:10.1115/1.4049624

Cited by 33 publications

(16 citation statements)

References 38 publications

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“…Sometimes the performance of machine learning models, that are used separately, is not as promising as expected. Therefore, multiple machine learning models can be integrated and employed in a unified ensemble model to achieve better performance [35]. To realize this ensemble model, the simplest approach is to average the outputs of the trained machine learning models to calculate the final output.…”

Section: Literature Reviewmentioning

confidence: 99%

Robust Prediction of the Bandwidth of Metamaterial Antenna Using Deep Learning

Abdelhamid¹,

Alotaibi²

2022

Computers, Materials &Amp; Continua

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The design of microstrip antennas is a complex and time-consuming process, especially the step of searching for the best design parameters. Meanwhile, the performance of microstrip antennas can be improved using metamaterial, which results in a new class of antennas called metamaterial antenna. Several parameters affect the radiation loss and quality factor of this class of antennas, such as the antenna size. Recently, the optimal values of the design parameters of metamaterial antennas can be predicted using machine learning, which presents a better alternative to simulation tools and trialand-error processes. However, the prediction accuracy depends heavily on the quality of the machine learning model. In this paper, and benefiting from the current advances in deep learning, we propose a deep network architecture to predict the bandwidth of metamaterial antenna. Experimental results show that the proposed deep network could accurately predict the optimal values of the antenna bandwidth with a tiny value of mean-square error (MSE). In addition, the proposed model is compared with current competing approaches that are based on support vector machines, multi-layer perceptron, K-nearest neighbors, and ensemble models. The results show that the proposed model is better than the other approaches and can predict antenna bandwidth more accurately.

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Section: Literature Reviewmentioning

confidence: 99%

Robust Prediction of the Bandwidth of Metamaterial Antenna Using Deep Learning

Abdelhamid¹,

Alotaibi²

2022

Computers, Materials &Amp; Continua

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“…To integrate the prediction outputs of base classifiers, three stacking strategies have been explored and likened: feed-forward artificial neural network, support vector regressors, and k-nearest neighbor regressors [113]. The majority of the stacking models studied were seen to be capable of predicting Solar radiation.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques

et al. 2022

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Nowadays, learning-based modeling methods are utilized to build a precise forecast model for renewable power sources. Computational Intelligence (CI) techniques have been recognized as effective methods in generating and optimizing renewable tools. The complexity of this variety of energy depends on its coverage of large sizes of data and parameters, which have to be investigated thoroughly. This paper covered the most resent and important researchers in the domain of renewable problems using the learning-based methods. Various types of Deep Learning (DL) and Machine Learning (ML) algorithms employed in Solar and Wind energy supplies are given. The performance of the given methods in the literature is assessed by a new taxonomy. This paper focus on conducting comprehensive state-of-the-art methods heading to performance evaluation of the given techniques and discusses vital difficulties and possibilities for extensive research. Based on the results, variations in efficiency, robustness, accuracy values, and generalization capability are the most obvious difficulties for using the learning techniques. In the case of the big dataset, the effectiveness of the learning techniques is significantly better than the other computational methods. However, applying and producing hybrid learning techniques with other optimization methods to develop and optimize the construction of the techniques is optionally indicated. In all cases, hybrid learning methods have better achievement than a single method due to the fact that hybrid methods gain the benefit of two or more techniques for providing an accurate forecast. Therefore, it is suggested to utilize hybrid learning techniques in the future to deal with energy generation problems.

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“…In literature, researchers combine two or model machine learning models to enhance the performance and overcome the disadvantages of single weak learners (base models). These model combinations are called ensemble models [35]. The simple form of the ensemble is an average ensemble that calculates its output using the average of outputs of base models.…”

Section: Machine Learning Ensemblementioning

confidence: 99%

An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

Shahbazi¹,

Byun²

2022

Computers, Materials &Amp; Continua

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Metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their performance. Antenna size affects the quality factor and the radiation loss of the antenna. Metamaterial antennas can overcome the limitation of bandwidth for small antennas. Machine learning (ML) model is recently applied to predict antenna parameters. ML can be used as an alternative approach to the trial-and-error process of finding proper parameters of the simulated antenna. The accuracy of the prediction depends mainly on the selected model. Ensemble models combine two or more base models to produce a better-enhanced model. In this paper, a weighted average ensemble model is proposed to predict the bandwidth of the Metamaterial Antenna. Two base models are used namely: Multilayer Perceptron (MLP) and Support Vector Machines (SVM). To calculate the weights for each model, an optimization algorithm is used to find the optimal weights of the ensemble. Dynamic Group-Based Cooperative Optimizer (DGCO) is employed to search for optimal weight for the base models. The proposed model is compared with three based models and the average ensemble model. The results show that the proposed model is better than other models and can predict antenna bandwidth efficiently.

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Short-Term Prediction of Global Solar Radiation Energy Using Weather Data and Machine Learning Ensembles: A Comparative Study

Cited by 33 publications

References 38 publications

Robust Prediction of the Bandwidth of Metamaterial Antenna Using Deep Learning

Robust Prediction of the Bandwidth of Metamaterial Antenna Using Deep Learning

Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques

An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

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