A Novel Hybrid Model for Solar Radiation Forecasting Using Support Vector Machine and Bee Colony Optimization Algorithm: Review and Case Study

Guermoui, Mawloud; Gairaa, Kacem; Boland, John; Arrif, Toufik

doi:10.1115/1.4047852

Cited by 37 publications

(11 citation statements)

References 43 publications

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“…One of the most recent and prominent research fields is hybrid methods of swarm intelligence and various machine learning models adapted for a large number of real-world problems. Some practical machine learning challenges that are successfully tackled with swarm intelligence include feature selection [ 29 , 30 , 31 ], training artificial neural networks (ANNs) [ 32 , 33 ], text clustering [ 34 ] and many others [ 35 , 36 ].…”

Section: Preliminaries and Related Workmentioning

confidence: 99%

Chaotic Harris Hawks Optimization with Quasi-Reflection-Based Learning: An Application to Enhance CNN Design

Basha

Bačanin

Vukobrat

et al. 2021

Sensors

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The research presented in this manuscript proposes a novel Harris Hawks optimization algorithm with practical application for evolving convolutional neural network architecture to classify various grades of brain tumor using magnetic resonance imaging. The proposed improved Harris Hawks optimization method, which belongs to the group of swarm intelligence metaheuristics, further improves the exploration and exploitation abilities of the basic algorithm by incorporating a chaotic population initialization and local search, along with a replacement strategy based on the quasi-reflection-based learning procedure. The proposed method was first evaluated on 10 recent CEC2019 benchmarks and the achieved results are compared with the ones generated by the basic algorithm, as well as with results of other state-of-the-art approaches that were tested under the same experimental conditions. In subsequent empirical research, the proposed method was adapted and applied for a practical challenge of convolutional neural network design. The evolved network structures were validated against two datasets that contain images of a healthy brain and brain with tumors. The first dataset comprises well-known IXI and cancer imagining archive images, while the second dataset consists of axial T1-weighted brain tumor images, as proposed in one recently published study in the Q1 journal. After performing data augmentation, the first dataset encompasses 8.000 healthy and 8.000 brain tumor images with grades I, II, III, and IV and the second dataset includes 4.908 images with Glioma, Meningioma, and Pituitary, with 1.636 images belonging to each tumor class. The swarm intelligence-driven convolutional neural network approach was evaluated and compared to other, similar methods and achieved a superior performance. The obtained accuracy was over 95% in all conducted experiments. Based on the established results, it is reasonable to conclude that the proposed approach could be used to develop networks that can assist doctors in diagnostics and help in the early detection of brain tumors.

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Section: Preliminaries and Related Workmentioning

confidence: 99%

Chaotic Harris Hawks Optimization with Quasi-Reflection-Based Learning: An Application to Enhance CNN Design

Basha

Bačanin

Vukobrat

et al. 2021

Sensors

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“…LS-SVM proves its high performance in the field of modeling and forecasting problems. In the current work, the GAO algorithm is applied for hyper-parameter tuning of the LS-SVM model [13,17,22].…”

Section: Grasshopper Optimization Least Square Support Vector Machine (Gao-ls-svm)mentioning

confidence: 99%

“…In the literature review of solar radiation components assessments, artificial neural network models (ANN) [5,30], support vector machine (SVM) [13,17,22], Gaussian process regressions (GPR) [18,20], and random forest (RF) [4] are often used models in this field due to their high performance and small parameters tuning during the modeling process.…”

Section: Introductionmentioning

confidence: 99%

A novel ensemble learning approach for hourly global solar radiation forecasting

Guermoui

Benkaciali

Gairaa

et al. 2021

Neural Comput & Applic

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Precise solar radiation forecasting can provide great benefits and solutions for smart grid distribution and electricity management. However, its non-stationary behavior and randomness render its estimation very difficult. In this respect, a new hybrid learning approach is proposed for multi-hour global solar radiation forecasting, relying on Convolutional Neural Network (CNN), Nonparametric Gaussian Process Regression (GPR), Least Support Vector Machine (LS-SVM), and Extreme Learning Machine (ELM) as essence predictors. Then compressive sensing technique is applied to perform a hybridization scheme of the model's output. Hourly global solar radiation data from two sites in Algeria with different climate conditions are used to evaluate the full potential of the integrated model, with stationarity checks with an advanced clear sky model (MecClear model). Different comparative simulations show the superiority of the proposed pipeline in forecasting hourly global solar radiation data for multi-hour ahead compared to the stand-alone model. Experimental results show that the proposed hybridization methodology can effectively improve the prediction accuracy and outperforms benchmarking models during all the forecasting horizons.

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“…However, most of the available work focuses on direct irradiance forecasting and power modeling. For example, in [ 12 ] hourly irradiance values are fed to a SVR to forecast few steps ahead of Global Horizontal Irradiance (GHI); while in [ 13 , 14 ] meteorological recordings are fed to an RF and CNN, respectively, to predict irradiance. In [ 15 ], recordings from neighbouring sites are used in their forecast given their correlation.…”

Section: Introductionmentioning

confidence: 99%

Increasing the Accuracy of Hourly Multi-Output Solar Power Forecast with Physics-Informed Machine Learning

Pombo

Bindner

Spataru

et al. 2022

Sensors

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Machine Learning (ML)-based methods have been identified as capable of providing up to one day ahead Photovoltaic (PV) power forecasts. In this research, we introduce a generic physical model of a PV system into ML predictors to forecast from one to three days ahead. The only requirement is a basic dataset including power, wind speed and air temperature measurements. Then, these are recombined into physics informed metrics able to capture the operational point of the PV. In this way, the models learn about the physical relationships of the different features, effectively easing training. In order to generalise the results, we also present a methodology evaluating this physics informed approach. We present a study-case of a PV system in Denmark to validate our claims by extensively evaluating five different ML methods: Random Forest, Support Vector Machine, Convolutional Neural Networks (CNN), Long-Short Term Memory (LSTM) and a hybrid CNN–LSTM. The results show consistently how the best predictors use the proposed physics-informed features disregarding the particular ML-method, and forecasting horizon. However, also, how there is a threshold regarding the number of previous samples to be included that appears as a convex function.

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A Novel Hybrid Model for Solar Radiation Forecasting Using Support Vector Machine and Bee Colony Optimization Algorithm: Review and Case Study

Cited by 37 publications

References 43 publications

Chaotic Harris Hawks Optimization with Quasi-Reflection-Based Learning: An Application to Enhance CNN Design

Chaotic Harris Hawks Optimization with Quasi-Reflection-Based Learning: An Application to Enhance CNN Design

A novel ensemble learning approach for hourly global solar radiation forecasting

Increasing the Accuracy of Hourly Multi-Output Solar Power Forecast with Physics-Informed Machine Learning

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