Application of neural networks and genetic algorithms for sizing of photovoltaic systems

Mellit, A.; Kalogirou, Soteris A.; Drif, M.

doi:10.1016/j.renene.2010.04.017

Cited by 86 publications

(26 citation statements)

References 31 publications

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“…The common methods that are used to optimize the power supply systems include Genetic Algorithm (GA) [66,67,101,105,108,109,116,[127][128][129], Particle Swarm Optimization (PSO) [71,78,128,130,131], Simulated Annealing (SA) [132,133], Honey Bee Mating Optimization (HBMO) [134,135], Neural Networks (NN) [111,114,136,137], Evolutionary Algorithm (EA) [66,116,134,138], and Simplex Algorithm [128,134,139,140]. In the context of remote telecommunication applications, Okundamiya et al [127] applied the GA approach to optimize a hybrid PV-wind-battery system for a BS site.…”

Section: System Sizing and Optimizationmentioning

confidence: 99%

Sustainable Power Supply Solutions for Off-Grid Base Stations

2015

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Abstract:The telecommunication sector plays a significant role in shaping the global economy and the way people share information and knowledge. At present, the telecommunication sector is liable for its energy consumption and the amount of emissions it emits in the environment. In the context of off-grid telecommunication applications, offgrid base stations (BSs) are commonly used due to their ability to provide radio coverage over a wide geographic area. However, in the past, the off-grid BSs usually relied on emission-intensive power supply solutions such as diesel generators. In this review paper, various types of solutions (including, in particular, the sustainable solutions) for powering BSs are discussed. The key aspects in designing an ideal power supply solution are reviewed, and these mainly include the pre-feasibility study and the thermal management of BSs, which comprise heating and cooling of the BS shelter/cabinets and BS electronic equipment and power supply components. The sizing and optimization approaches used to design the BSs' power supply systems as well as the operational and control strategies adopted to manage the power supply systems are also reviewed in this paper.

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Section: System Sizing and Optimizationmentioning

confidence: 99%

Sustainable Power Supply Solutions for Off-Grid Base Stations

2015

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“…Table 18 shows two applications of neural networks, neuro-fuzzy and GAs for sizing of an SAPV. GAs and neural networks have been used to determine the optimal sizing parameters in isolated areas in Algeria [259]. Firstly, the GA has been used to optimize the sizing parameters relative to 40-sites in Algeria and the ANN has been used to predict the optimal parameters in remotes area.…”

Section: Article In Pressmentioning

confidence: 99%

Artificial intelligence techniques for photovoltaic applications: A review

Mellit

Kalogirou

2008

Progress in Energy and Combustion Science

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“…In this work, an artificial neural network-based genetic algorithm (ANN-GA) model was developed for generating the sizing curve of stand-alone photovoltaic (SAPV) systems (Mellit et al 2010). First, a numerical method is used for generating the sizing curves for different loss of load probability (LLP) corresponding to 40 sites located in Algeria.…”

Section: Applications Of Ann and Ga Combined (A) Optimization Of Solamentioning

confidence: 99%

Artificial Neural Networks and Genetic Algorithms for the Modeling, Simulation, and Performance Prediction of Solar Energy Systems

Kalogirou

2013

Assessment and Simulation Tools for Sustainable Energy Systems

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In this chapter, two of the most important artificial intelligence techniques are presented together with a variety of applications in solar energy systems. Artificial neural network (ANN) models represent a new method in system modeling and prediction. An ANN mimics mathematically the function of a human brain. They learn the relationship between the input parameters, usually collected from experiments, and the controlled and uncontrolled variables by studying previously recorded data. A genetic algorithm (GA) is a model of machine learning, which derives its behavior from a representation of the processes of evolution in nature. GAs can be used for multidimensional optimization problems in which the character string of the chromosome can be used to encode the values for the different parameters being optimized. The chapter outlines an understanding of how ANN and GA operate by way of presenting a number of problems in different solar energy systems applications, which include modeling and simulation of solar systems, prediction of the performance, and optimization of the design or operation of the systems. The systems presented include solar thermal and photovoltaic systems.

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Application of neural networks and genetic algorithms for sizing of photovoltaic systems

Cited by 86 publications

References 31 publications

Sustainable Power Supply Solutions for Off-Grid Base Stations

Sustainable Power Supply Solutions for Off-Grid Base Stations

Artificial intelligence techniques for photovoltaic applications: A review

Artificial Neural Networks and Genetic Algorithms for the Modeling, Simulation, and Performance Prediction of Solar Energy Systems

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