Hybrid Ensemble-Learning Approach for Renewable Energy Resources Evaluation in Algeria

Abstract

In order to achieve a highly accurate estimation of solar energy resource potential, a novel hybrid ensemble-learning approach, hybridizing Advanced Squirrel-Search Optimization Algorithm (ASSOA) and support vector regression, is utilized to estimate the hourly tilted solar irradiation for selected arid regions in Algeria. Long-term measured meteorological data, including mean-air temperature, relative humidity, wind speed, alongside global horizontal irradiation and extra-terrestrial horizontal irradiance, were obtained for the two cities of Tamanrasset-and-Adrar for two years. Five computational algorithms were considered and analyzed for the suitability of estimation. Further two new algorithms, namely Average Ensemble and Ensemble using support vector regression were developed using the hybridization approach. The accuracy of the developed models was analyzed in terms of five statistical error metrics, as well as the Wilcoxon rank-sum and ANOVA test. Among the previously selected algorithms, K Neighbors Regressor and support vector regression exhibited good performances. However, the newly proposed ensemble algorithms exhibited even better performance. The proposed model showed relative root mean square errors lower than 1.448% and correlation coefficients higher than 0.999. This was further verified by benchmarking the new ensemble against several popular swarm intelligence algorithms. It is concluded that the proposed algorithms are far superior to the commonly adopted ones.

Dipper Throated Algorithm for Feature Selection and Classification in Electrocardiogram

“…Another research used convolution deep neural network for the classification of arrythmia and optimization using random search is presented in [43]. Two deep neural network architectures are used to classify electrical activity and generating rhythm using ECG reading and used Bayesian optimizer and provide 93% accuracy proposed in [44]. A proposed algorithm for classification of atrial fibrillation that includes connections in CNN, which increase the speed of data transmission and reduces the dependencies between data presented in [45].…”

Dipper Throated Optimization for Detecting Black-Hole Attacks in MANETs

“…It reroutes traffic and drops all packets as it passes through itself. As a result, this type of black-hole attack exclusively targets a network's architecture rather than introducing more fake routing signals into the network [24][25][26][27][28][29][30]. By receiving an instantaneous Route Reply Packet (RREP) packet, the malicious node persuades the entire adjacent node to submit their packets early in active black-hole attacks.…”

Assessment of hybrid machine learning algorithms using TRMM rainfall data for daily inflow forecasting in Três Marias Reservoir, eastern Brazil

“…The performance criteria employed are the mean absolute error (MAE), root mean square error (RMSE), Nash-Sutcliffe coefficient (NSE), and correlation coefficient (R). These metrics are computed according to equations (12)–(15 ), as detailed in the referenced studies [ [52] , [53] , [54] , [55] , [56] , [57] , [58] , [59] , [60] , [61] ]. where Q obs , i and Q sim , i are the observed and simulated data, respectively; N denotes the size of the time series, and and are the means of the measured and simulated data, respectively.…”