55 research outputs found
Very accurate parameter estimation of single- and double-diode solar cell models using a modified artificial bee colony algorithm
Artificial intelligence for photovoltaic systems
Photovoltaic systems have gained an extraordinary popularity in the energy generation industry. Despite the benefits, photovoltaic systems still suffer from four main drawbacks, which include low conversion efficiency, intermittent power supply, high fabrication costs and the nonlinearity of the PV system output power. To overcome these issues, various optimization and control techniques have been proposed. However, many authors relied on classical techniques, which were based on intuitive, numerical or analytical methods. More efficient optimization strategies would enhance the performance of the PV systems and decrease the cost of the energy generated. In this chapter, we provide an overview of how Artificial Intelligence (AI) techniques can provide value to photovoltaic systems. Particular attention is devoted to three main areas: (1) Forecasting and modelling of meteorological data, (2) Basic modelling of solar cells and (3) Sizing of photovoltaic systems. This chapter will aim to provide a comparison between conventional techniques and the added benefits of using machine learning methods
Application of hybrid crow-search with particle swarm optimization algorithm in AGC studies of multi-area systems
Interpretation of immunohistochemistry results of HER2/neu expression in invasive breast cancer: Evaluation of interobserver and intraobserver agreement in Iran
Discipline specificity and students’ English writing proficiency: A case of collaborative teaching for transfer
A comparative study of lumped equivalent circuit models of a lithium battery for state of charge prediction
Study the Impact of Parameter Settings and Operators Role for Genetic Algorithm Based Test Case Prioritization
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