Journal ArticleDOI
Novel application of adaptive swarm intelligence techniques coupled with adaptive network-based fuzzy inference system in predicting photovoltaic power
Mosbeh R. Kaloop,Mosbeh R. Kaloop,Abidhan Bardhan,Navid Kardani,Pijush Samui,Jong Wan Hu,Ahmed Ramzy +6 more
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TLDR
The newly constructed ANFIS-APSO outperformed the standard ANFis-PSO model including other hybrid models, and hence has very potential to be a new alternative to assist engineers for predicting the PV power of solar systems at short- and long-time horizons.Abstract:
Accurate photovoltaic (PV) power prediction is necessary for future development of the micro-grids projects and the economic dispatch sector. This study investigates the potential of using a novel hybrid approach of adaptive swarm intelligence techniques and adaptive network-based fuzzy inference system (ANFIS) in estimating the PV power of a solar system at different time horizons, from 0 to 24 h. The developed approach is an integration of ANFIS and particle swarm optimization (PSO) with adaptive and time-varying acceleration coefficients, i.e., ANFIS-APSO (ANFIS-PSO with adaptive acceleration coefficients) and ANFIS-IPSO (ANFIS-PSO with time-varying acceleration coefficients), were developed. The performance of the proposed models was compared with other hybrid ANFIS models, namely ANFIS-PSO (ANFIS coupled with PSO), ANFIS-BBO (ANFIS coupled with biogeography-based optimization), ANFIS-GA (ANFIS coupled with genetic algorithm), and ANFIS-GWO (ANFIS coupled with grey wolf optimization). For this purpose, the climatic variables and historical PV power data of a 960 kWP grid-connected PV system in the south of Italy were used to design and evaluate the models. Several statistical analyses were implemented to evaluate the accuracy of the proposed models and assess the impact of variables that affects the PV power values. The experimental results show that the proposed ANFIS-APSO attained the most accurate prediction of the PV power with R2 = 0.835 and 0.657, RMSE = 0.088 kW and 0.081 kW, and MAE = 0.077 kW and 0.079 kW in the testing phase at time horizons 12 h and 24 h, respectively. Based on the obtained results, the newly constructed ANFIS-APSO outperformed the standard ANFIS-PSO model including other hybrid models, and hence very potential to be a new alternative to assist engineers for predicting the PV power of solar systems at short- and long-time horizons.read more
Citations
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Journal ArticleDOI
Predicting the thermal conductivity of soils using integrated approach of ANN and PSO with adaptive and time-varying acceleration coefficients
TL;DR: In this article , a hybrid adaptive neuro swarm intelligence (HANSI) technique was proposed for predicting the thermal conductivity of unsaturated soils, which integrated artificial neural networks (ANNs) and particle swarm optimisation (PSO) with adaptive and time-varying acceleration coefficients.
Journal ArticleDOI
A novel improved Harris Hawks optimization algorithm coupled with ELM for predicting permeability of tight carbonates
Navid Kardani,Abidhan Bardhan,Bishwajit Roy,Pijush Samui,Majidreza Nazem,Danial Jahed Armaghani,Annan Zhou +6 more
TL;DR: In this paper, a hybrid model constructed by combination of the improved version of the Harris Hawks optimisation (HHO), and extreme learning machine (ELM) is proposed to predict the permeability of tight carbonates using limited number of input variables.
Journal ArticleDOI
Soft computing techniques for the prediction of concrete compressive strength using Non-Destructive tests
TL;DR: Experimental results show that the BPNN attained the most accurate prediction of concrete CS based on both ultrasonic pulse velocity and rebound number values, and these two models are very potential to assist engineers in the design phase of civil engineering projects to estimate the concrete CS with a greater accuracy level.
Journal ArticleDOI
Prediction of rapid chloride penetration resistance of metakaolin based high strength concrete using light GBM and XGBoost models by incorporating SHAP analysis
Anas Abdulalim Alabdullah,Mudassir Iqbal,Muhammad Zahid,Kaffayatullah Khan,Muhammad Nasir Amin,Fazal E. Jalal +5 more
TL;DR: In this article , the authors investigated the non-linear capabilities of two machine learning prediction models, namely Light GBM and XGBoost, for predicting the values of Rapid Chloride Penetration Test (RCPT).
Journal ArticleDOI
A novel integrated approach of ELM and modified equilibrium optimizer for predicting soil compression index of subgrade layer of Dedicated Freight Corridor
Abidhan Bardhan,Catherine Early,Anasua GuhaRay,Shubham Gupta,Biswajeet Pradhan,Candan Gokceoglu +5 more
TL;DR: In this paper, a modified equilibrium optimizer (MEO) and the extreme learning machine (ELM) were combined to predict soil compression index (Cc) in roadways, railways, and airport runways.
References
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Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Journal ArticleDOI
ANFIS: adaptive-network-based fuzzy inference system
TL;DR: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference System implemented in the framework of adaptive networks.
Journal ArticleDOI
Grey Wolf Optimizer
TL;DR: The results of the classical engineering design problems and real application prove that the proposed GWO algorithm is applicable to challenging problems with unknown search spaces.
Journal ArticleDOI
Biogeography-Based Optimization
TL;DR: This paper discusses natural biogeography and its mathematics, and then discusses how it can be used to solve optimization problems, and sees that BBO has features in common with other biology-based optimization methods, such as GAs and particle swarm optimization (PSO).