ABSTRACTHybrid energy projects are gaining global interest due to their potential to promote sustainable development. This study addresses a gap in the literature by proposing a stochastic model that assesses the econ...
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ABSTRACTHybrid energy projects are gaining global interest due to their potential to promote sustainable development. This study addresses a gap in the literature by proposing a stochastic model that assesses the economic feasibility of a hybrid energy system combining wind, photovoltaic generation, and battery storage. Unlike traditional deterministic approaches, the model incorporates uncertainties in key financial and operational variables, improving decision-making in real-world conditions. The optimization framework integrates Design of Experiments (DoE), Response Surface Methodology (RSM), and the Desirability function to balance economic return and financial risk. By maximizing the mean and minimizing the variance of the Net Present Value (NPV), the model identifies an optimal system configuration with 92% wind energy, a demand level of 230 kWh/month, and lithium-ion battery storage. This approach reduces computational complexity and enhances hybrid system viability in regions with strong wind resources, providing a valuable tool for investors and policymakers in sustainable energy planning.
In order to improve equipment efficiency in terms of performance, energy consumption and degradation for example, the industry has increased the use of control systems as the PID (proportional-integral-derivative) to ...
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In order to improve equipment efficiency in terms of performance, energy consumption and degradation for example, the industry has increased the use of control systems as the PID (proportional-integral-derivative) to a new baseline. This structure has few parameters to adjust and it is easy to implement practically. However, there are some requirements often included on multivariable systems that cannot be solved concurrently by classical methods. To solve this problem, the current paper approaches the application of Multiobjective Differential Evolution (MODE), Multiobjective Harmony Search (MOHS) and Multiobjective Particle Swarm Optimization (MOPSO) on multivariable PID controllers tuning. Moreover, an improved version of MOPSO (I-MOPSO) is proposed and its performance is compared with the other algorithms. In order to validate it under control systems, the optimization technique is applied on a two degree of freedom robotic manipulator. Finally, a detailed analysis is made on the I-MOPSO achievements.
The present work is focused on the study of indoor thermal comfort control problem in buildings equipped with heating systems. The occupants' thermal comfort sensation is addressed here by a comfort index known as...
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The present work is focused on the study of indoor thermal comfort control problem in buildings equipped with heating systems. The occupants' thermal comfort sensation is addressed here by a comfort index known as PMV (Predict Mean Vote). In this context, two control algorithms are proposed by using only-one-actuator system associated to the heating system. The methods are based on the model predictive control scheme and on the improvement of the PMV index together with energy minimization. Simulation results - obtained by using the weather data file for the city of Curitiba, Brazil - for two case studies: i ) variations between low and moderate occupants metabolic rates and ii) variations between moderate and high occupants metabolic rates are presented to validate the proposed methodology in terms of room air temperature, relative humidity and PMV control.
The firefly algorithm (FA) is a new population-based metaheuristic bioinspired on the behavior of the flashing characteristics of fireflies. As a population-based algorithm, the FA suffers from large execution times s...
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The use of synthetic images has been highlighted within the computer vision field as a more practical option that helps reduce the time and cost spent on vehicle detection tasks in urban environments. This paper prese...
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ISBN:
(纸本)9798400707995
The use of synthetic images has been highlighted within the computer vision field as a more practical option that helps reduce the time and cost spent on vehicle detection tasks in urban environments. This paper presents a framework for automated synthetic image generation, aiming at the detection of vehicles in urban environments. The creation of the 3D scenario is carried out through the Blender software. Some adverse conditions of the scenes were developed, including: rain, fog, occlusion by tree branches and electrical power lines, and low lighting. Then the algorithm for the automated generation of images was developed based on parameters chosen by the user to generate the scene and to generate the annotations of the images. Additionally, a convolutional neural network with YOLOv5 architecture along with a cross-validation technique was applied to carry out vehicle detections on the developed synthetic dataset, on both real and mixed datasets. The proposed framework generated a consistent dataset that can be used in vehicle detection networks, obtaining a mAP 0.5 above 96% and mAP 0.5:0.95 above 82%, also the precision values remained above 92%, and recall above 95%. It was also observed that the combination of synthetic images with real images can improve the performance of the model.
Particle swarm optimization (PSO) is a population-based stochastic optimization technique, originally developed by Eberhart and Kennedy, inspired by simulation of a social psychological metaphor instead of the surviva...
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Particle swarm optimization (PSO) is a population-based stochastic optimization technique, originally developed by Eberhart and Kennedy, inspired by simulation of a social psychological metaphor instead of the survival of the fittest individual. In PSO, the system (swarm) is initialized with a population of random solutions (particles) and searches for optima using cognitive and social factors by updating generations. PSO has been successfully applied to a wide range of applications, mainly in solving continuous nonlinear optimization problems. Based on the PSO and chaos theories, this paper discusses the use of a chaotic PSO approach hybridized with an implicit filtering (IF) technique to optimize performance of economic dispatch problems. The chaotic PSO with chaos sequences is the global optimizer and the IF is used to fine-tune the chaotic PSO run in sequential manner. The hybrid methodology is validated for a test system consisting of 13 thermal units whose incremental fuel cost function takes into account the valve-point loading effects.
Energy supply and mobility are necessary functions of economic development and have become global latent themes concerning sustainability. In the electricity sector renewable energy technologies, such as solar photovo...
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Optimization metaheuristics, such as Particle Swarm Optimization, Ant Colony Optimization and bacterial foraging strategies have become very popular in the optimization community and have been successfully applied to ...
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This article implements the multi-objective grey wolf optimizer (MOGWO) in the tuning process of the gains of the Fractional-Order Proportional-Integral-Derivative (FOPID) controller applied in the control of a boiler...
This article implements the multi-objective grey wolf optimizer (MOGWO) in the tuning process of the gains of the Fractional-Order Proportional-Integral-Derivative (FOPID) controller applied in the control of a boiler system. For a sequence of 100 runs, this application had compared and analyzed with other implementation that uses MOGWO to optimize the gains of the classical Proportional-Integral-Derivative (PID) controller. In the computational simulation, the value hypervolume metric had used to analyze the performance of the controllers. In the results, the implementation of the FOPID showed superior to PID, where the comparison had validated by a hypothesis test. Despite the higher computational cost concerning the tuning process of the PID controller, this study proved that the FOPID controller can be advantageous for industrial applications.
Technology has been successfully applied in sports, where biomechanical analysis is one of the most important areas used to raise the performance of athletes. In this context, this paper focuses on swim velocity profi...
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ISBN:
(纸本)9782874190957
Technology has been successfully applied in sports, where biomechanical analysis is one of the most important areas used to raise the performance of athletes. In this context, this paper focuses on swim velocity profile identification using Radial Basis Functions Neural Networks (RBF-NN) trained by the Gustafson-Kessel clustering combined with a novel Dynamic Self-adaptive Multiobjective Harmony Search (DS-MOHS). One study case is analyzed, from real data acquired of an elite female athlete, swimming breaststroke style. Better results are obtained by DS-MOHS when compared with standard multiobjective harmony search in terms of accuracy and generalization of the model.
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