Parameter identification of solar cells using artificial bee colony optimization

By Diego Oliva, Erik Cuevas, Gonzalo Pajares. 2014

439 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/72c783775848ecd37079ad34f14fdd29d6618cfe

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Richard Binns
2. Derick Mathew
3. C. Rani
4. Yue Wang
5. Krishna K. Busawon
6. M. Rajesh Kumar

In Electrical and Electronic Engineering

1. Richard Binns
2. Derick Mathew
3. C. Rani
4. Yue Wang
5. Krishna K. Busawon
6. M. Rajesh Kumar

In Renewable Energy, Sustainability and the Environment

1. Richard Binns
2. Derick Mathew
3. C. Rani
4. Yue Wang
5. Krishna K. Busawon
6. M. Rajesh Kumar

Citations in the corpus

• Wind-Driven Optimization Technique for Estimation of Solar Photovoltaic Parameters (2018, methodology): “In the recent years, different optimization techniques have been introduced to estimate the parameters of solar PV, namely the genetic algorithm [8], pattern search optimization [9], artificial immune system [10], bacterial foraging algorithm [11], simulated annealing [12], harmony search algorithm ”