An Innovative Metaheuristic Strategy for Solar Energy Management through a Neural Networks Framework

An innovative machine learning based on feed-forward artificial neural network and equilibrium optimization for predicting solar irradiance

As is known, having a reliable analysis of energy sources is an important task toward sustainable development. Solar energy is one of the most advantageous types of renewable energy. Compared to fossil fuels, it is cleaner, freely available, and can be directly exploited for electricity. Therefore, this study is concerned with suggesting novel hybrid models for improving the forecast of Solar Irradiance (IS). First, a predictive model, namely Feed-Forward Artificial Neural Network (FFANN) forms the non-linear contribution between the IS and dominant meteorological and temporal parameters (including humidity, temperature, pressure, cloud coverage, speed and direction of wind, month, day, and hour). Then, this framework is optimized using several metaheuristic algorithms to create hybrid models for predicting the IS. According to the accuracy assessments, metaheuristic algorithms attained satisfying training for the FFANN by using 80% of the data. Moreover, applying the trained models to the remaining 20% proved their high proficiency in forecasting the IS in unseen environmental circumstances. A comparison among the optimizers revealed that Equilibrium Optimization (EO) could achieve a higher accuracy than Wind-Driven Optimization (WDO), Optics Inspired Optimization (OIO), and Social Spider Algorithm (SOSA). In another phase of this study, Principal Component Analysis (PCA) is applied to identify the most contributive meteorological and temporal factors. The PCA results can be used to optimize the problem dimension, as well as to suggest effective real-world measures for improving solar energy production. Lastly, the EO-based solution is yielded in the form of an explicit formula for a more convenient estimation of the IS.

A novel ensemble approach for estimating the competency of bank telemarketing

Having a reliable understanding of bank telemarketing performance is of great importance in the modern world of economy. Recently, machine learning models have obtained high attention for this purpose. In order to introduce and evaluate cutting-edge models, this study develops sophisticated hybrid models for estimating the success rate of bank telemarketing. A large free dataset is used which lists the clients' information of a Portuguese bank. The data are analyzed by four artificial neural networks (ANNs) trained by metaheuristic algorithms, namely electromagnetic field optimization (EFO), future search algorithm (FSA), harmony search algorithm (HSA), and social ski-driver (SSD). The models predict the subscription of clients for a long-term deposit by evaluating nineteen conditioning parameters. The results first indicated the high potential of all four models in analyzing and predicting the subscription pattern, thereby, revealing the competency of neuro-metaheuristic hybrids. However, comparatively speaking, the EFO yielded the most reliable approximation with an area under the curve (AUC) around 0.80. FSA-ANN emerged as the second-accurate model followed by the SSD and HSA with respective AUCs of 0.7714, 0.7663, and 0.7160. Moreover, the superiority of the EFO-ANN is confirmed against several conventional models from the previous literature, and finally, it is introduced as an effective model to be practically used by banking institutions for predicting the likelihood of deposit subscriptions.

A survey on advanced machine learning and deep learning techniques assisting in renewable energy generation

The sustainability of the earth depends on renewable energy. Forecasting the output of renewable energy has a big impact on how we operate and manage our power networks. Accurate forecasting of renewable energy generation is crucial to ensuring grid dependability and permanence and reducing the risk and cost of the energy market and infrastructure. Although there are several approaches to forecasting solar radiation on a global scale, the two most common ones are machine learning algorithms and cloud pictures combined with physical models. The objective is to present a summary of machine learning-based techniques for solar irradiation forecasting in this context. Renewable energy is being used more and more in the world's energy grid. Numerous strategies, including hybrids, physical models, statistical approaches, and artificial intelligence techniques, have been developed to anticipate the use of renewable energy. This paper examines methods for forecasting renewable energy based on deep learning and machine learning. Review and analysis of deep learning and machine learning forecasts for renewable energy come first. The second paragraph describes metaheuristic optimization techniques for renewable energy. The third topic was the open issue of projecting renewable energy. I will wrap up with a few potential future job objectives.

A Systematic Review on Metaheuristic Optimization Techniques for Feature Selections in Disease Diagnosis: Open Issues and Challenges

There is a need for some techniques to solve various problems in today's computing world. Metaheuristic algorithms are one of the techniques which are capable of providing practical solutions to such issues. Due to their efficiency, metaheuristic algorithms are now used in healthcare data to diagnose diseases practically and with better results than traditional methods. In this study, an efficient search has been performed where 173 papers from different research databases such as Scopus, Web of Science, PubMed, PsycINFO, and others have been considered impactful in diagnosing the diseases using metaheuristic techniques. Ten metaheuristic techniques have been studied, which include spider monkey, shuffled frog leaping algorithm, cuckoo search algorithm, ant lion technique of optimization, lion optimization technique, moth flame technique, bat-inspired algorithm, grey wolf algorithm, whale optimization, and dragonfly technique of optimization for selecting and optimizing the features to predict heart disease, Alzheimer's disease, brain disorder, diabetes, chronic disease features, liver disease, covid-19, etc. Besides, the framework has also been shown to provide information on various phases behind the execution of metaheuristic techniques to predict diseases. The study's primary goal is to present the contribution of the researchers by demonstrating their methodology to predict diseases using the metaheuristic techniques mentioned above. Later, their work has also been compared and evaluated using accuracy, precision, F1 score, error rate, sensitivity, specificity, an area under a curve, etc., to help the researchers to choose the right field and methods for predicting the diseases in the future.

Solar Irradiance Forecasting to Short-Term PV Power: Accuracy Comparison of ANN and LSTM Models

The use of renewable energies, such as Photovoltaic (PV) solar power, is necessary to meet the growing energy consumption. PV solar power generation has intrinsic characteristics related to the climatic variables that cause intermittence during the generation process, promoting instabilities and insecurity in the electrical system. One of the solutions for this problem uses methods for the Prediction of Solar Photovoltaic Power Generation (PSPPG). In this context, the aim of this study is to develop and compare the prediction accuracy of solar irradiance between Artificial Neural Network (ANN) and Long-Term Short Memory (LSTM) network models, from a comprehensive analysis that simultaneously considers two distinct sets of exogenous meteorological input variables and three short-term prediction horizons (1, 15 and 60 min), in a controlled experimental environment. The results indicate that there is a significant difference (p < 0.001) in the prediction accuracy between the ANN and LSTM models, with better overall prediction accuracy skill for the LSTM models (MAPE = 19.5%), except for the 60 min prediction horizon. Furthermore, the accuracy difference between the ANN and LSTM models decreased as the prediction horizon increased, and no significant influence was observed on the accuracy of the prediction with both sets of evaluated meteorological input variables.

Horizontal and vertical search artificial bee colony for image segmentation of COVID-19 X-ray images

The artificial bee colony algorithm (ABC) has been successfully applied to various optimization problems, but the algorithm still suffers from slow convergence and poor quality of optimal solutions in the optimization process. Therefore, in this paper, an improved ABC (CCABC) based on a horizontal search mechanism and a vertical search mechanism is proposed to improve the algorithm's performance. In addition, this paper also presents a multilevel thresholding image segmentation (MTIS) method based on CCABC to enhance the effectiveness of the multilevel thresholding image segmentation method. To verify the performance of the proposed CCABC algorithm and the performance of the improved image segmentation method. First, this paper demonstrates the performance of the CCABC algorithm itself by comparing CCABC with 15 algorithms of the same type using 30 benchmark functions. Then, this paper uses the improved multi-threshold segmentation method for the segmentation of COVID-19 X-ray images and compares it with other similar plans in detail. Finally, this paper confirms that the incorporation of CCABC in MTIS is very effective by analyzing appropriate evaluation criteria and affirms that the new MTIS method has a strong segmentation performance.

An effective model for predicting serum albumin level in hemodialysis patients

Patients on hemodialysis (HD) are known to be at an increased risk of mortality. Hypoalbuminemia is one of the most important risk factors of death in HD patients, and is an independent risk factor for all-cause mortality that is associated with cardiac death, infection, and Protein-Energy Wasting (PEW). It is a clinical challenge to elevate serum albumin level. In addition, predicting trends in serum albumin level is effective for personalized treatment of hypoalbuminemia. In this study, we analyzed a total of 3069 records collected from 314 HD patients using a machine learning method that is based on an improved binary mutant quantum grey wolf optimizer (MQGWO) combined with Fuzzy K-Nearest Neighbor (FKNN). The performance of the proposed MQGWO method was evaluated using a series of experiments including global optimization experiments, feature selection experiments on open data sets, and prediction experiments on an HD dataset. The experimental results showed that the most critical relevant indicators such as age, presence or absence of diabetes, dialysis vintage, and baseline albumin can be identified by feature selection. Remarkably, the accuracy and the specificity of the method were 98.39% and 96.77%, respectively, demonstrating that this model has great potential to be used for detecting serum albumin level trends in HD patients.

Multi-Threshold Image Segmentation of Maize Diseases Based on Elite Comprehensive Particle Swarm Optimization and Otsu

Maize is a major global food crop and as one of the most productive grain crops, it can be eaten; it is also a good feed for the development of animal husbandry and essential raw material for light industry, chemical industry, medicine, and health. Diseases are the main factor limiting the high and stable yield of maize. Scientific and practical identification is a vital link to reduce the damage of diseases and accurate segmentation of disease spots is one of the fundamental techniques for disease identification. However, one single method cannot achieve a good segmentation effect to meet the diversity and complexity of disease spots. In order to solve the shortcomings of noise interference and oversegmentation in the Otsu segmentation method, a non-local mean filtered two-dimensional histogram was used to remove the noise in disease images and a new elite strategy improved comprehensive particle swarm optimization (PSO) method was used to find the optimal segmentation threshold of the objective function in this study. The experimental results of segmenting three kinds of maize foliar disease images show that the segmentation effect of this method is better than other similar algorithms and it has better convergence and stability.

Detection of COVID-19 severity using blood gas analysis parameters and Harris hawks optimized extreme learning machine

Coronavirus disease-2019 (COVID-19) has made the world more cautious about widespread viruses, and a tragic pandemic that was caused by a novel coronavirus has harmed human beings in recent years. The new coronavirus pneumonia outbreak is spreading rapidly worldwide. We collect arterial blood samples from 51 patients with a COVID-19 diagnosis. Blood gas analysis is performed using a Siemens RAPID Point 500 blood gas analyzer. To accurately determine the factors that play a decisive role in the early recognition and discrimination of COVID-19 severity, a prediction framework that is based on an improved binary Harris hawk optimization (HHO) algorithm in combination with a kernel extreme learning machine is proposed in this paper. This method uses specular reflection learning to improve the original HHO algorithm and is referred to as HHOSRL. The experimental results show that the selected indicators, such as age, partial pressure of oxygen, oxygen saturation, sodium ion concentration, and lactic acid, are essential for the early accurate assessment of COVID-19 severity by the proposed feature selection method. The simulation results show that the established methodlogy can achieve promising performance. We believe that our proposed model provides an effective strategy for accurate early assessment of COVID-19 and distinguishing disease severity. The codes of HHO will be updated in https://aliasgharheidari.com/HHO.html.

Artificial Neural Networks Based Optimization Techniques: A Review

In the last few years, intensive research has been done to enhance artificial intelligence (AI) using optimization techniques. In this paper, we present an extensive review of artificial neural networks (ANNs) based optimization algorithm techniques with some of the famous optimization techniques, e.g., genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), and backtracking search algorithm (BSA) and some modern developed techniques, e.g., the lightning search algorithm (LSA) and whale optimization algorithm (WOA), and many more. The entire set of such techniques is classified as algorithms based on a population where the initial population is randomly created. Input parameters are initialized within the specified range, and they can provide optimal solutions. This paper emphasizes enhancing the neural network via optimization algorithms by manipulating its tuned parameters or training parameters to obtain the best structure network pattern to dissolve the problems in the best way. This paper includes some results for improving the ANN performance by PSO, GA, ABC, and BSA optimization techniques, respectively, to search for optimal parameters, e.g., the number of neurons in the hidden layers and learning rate. The obtained neural net is used for solving energy management problems in the virtual power plant system.

Predicting the Parameters of Vortex Bladeless Wind Turbine Using Deep Learning Method of Long Short-Term Memory

From conventional turbines to cutting-edge bladeless turbines, energy harvesting from wind has been well explored by researchers for more than a century. The vortex bladeless wind turbine (VBT) is considered an advanced design that alternatively harvests energy from oscillation. This research investigates enhancing the output electrical power of VBT through simulation of the fluid–solid interactions (FSI), leading to a comprehensive dataset for predicting procedure and optimal design. Hence, the long short-term memory (LSTM) method, due to its time-series prediction accuracy, is proposed to model the power of VBT from the collected data. To find the relationship between the parameters and the variables used in this research, a correlation matrix is further presented. According to the value of 0.3 for the root mean square error (RMSE), a comparative analysis between the simulation results and their predictions indicates that the LSTM method is suitable for modeling. Furthermore, the LSTM method has significantly reduced the computation time so that the prediction time of desired values has been reduced from an average of two and a half hours to two minutes. In addition, one of the most important achievements of this study is to suggest a mathematical relation of output power, which helps to extend it in different sizes of VBT with a high range of parameter variations.

An Improved Particle Swarm Optimization with Chaotic Inertia Weight and Acceleration Coefficients for Optimal Extraction of PV Models Parameters

The efficiency of PV systems can be improved by accurate estimation of PV parameters. Parameter estimation of PV cells and modules is a challenging task as it requires accurate operation of PV cells and modules followed by an optimization tool that estimates their associated parameters. Mostly, population-based optimization tools are utilized for PV parameter estimation problems due to their computational intelligent behavior. However, most of them suffer from premature convergence problems, high computational burden, and often fall into local optimum solution. To mitigate these limitations, this paper presents an improved variant of particle swarm optimization (PSO) aiming to reduce shortcomings offered by conventional PSO for estimation of PV parameters. PSO is improved by introducing two strategies to control inertia weight and acceleration coefficients. At first, a sine chaotic inertia weight strategy is employed to attain an appropriate balance between local and global search. Afterward, a tangent chaotic strategy is utilized to guide acceleration coefficients in search of an optimal solution. The proposed algorithm is utilized to estimate the parameters of the PWP201 PV module, RTC France solar cell, and a JKM330P-72 PV module-based practical system. The obtained results indicate that the proposed technique avoids premature convergence and local optima stagnation of conventional PSO. Moreover, a comparison of obtained results with techniques available in the literature proves that the proposed methodology is an efficient, effective, and optimal tool to estimate PV modules and cells’ parameters.