Artificial intelligence techniques for sizing photovoltaic systems: A review

By Adel Mellit, Soteris A. Kalogirou, L. Hontoria, S. Shaari. 2009

423 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Energy Engineering and Power Technology, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/4997a2ec66f08f5b0241e4c3790f049786eab137

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. Holger R. Maier
2. Wenyan Wu
3. Graeme C. Dandy
4. Muhammad Naveed Akhter
5. Hazlie Mokhlis
6. Noraisyah Mohamed Shah
7. Saad Mekhilef
8. M. Muselli

In Energy Engineering and Power Technology

1. Saad Mekhilef
2. Rajab Khalilpour
3. A. Vassallo

In Renewable Energy, Sustainability and the Environment

1. Muhammad Naveed Akhter
2. Hazlie Mokhlis
3. Noraisyah Mohamed Shah
4. Saad Mekhilef
5. M. Muselli
6. Hocine Belmili
7. Cyril Voyant
8. Rajab Khalilpour

Citations in the corpus

• A survey of the most used MPPT methods: Conventional and advanced algorithms applied for photovoltaic systems (2015, background): “Solar energy is a good alternative for electric power generation due to its availability and cleanliness [2–4].”
• Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling (2014, background): “…and Wilby, 2001; Kalogirou, 2001; Sharma et al., 2005; AhmadiNedushan et al., 2006; Abrahart et al., 2008; Kalteh et al., 2008; Céréghino and Park, 2009; Mellit et al., 2009; Elshorbagy et al., 2010a, 2010b; Maier et al., 2010; Abrahart et al., 2012). wenyan.wu@adelaide.edu.au All rights reserved.”
• Forecasting of preprocessed daily solar radiation time series using neural networks (2010, methodology): “In this way, we have investigated both in time series forecasting, which is a challenge in many fields, and in artificial intelligence techniques, which are becoming more and more popular in the renewable energy domain (Mellit and Kalogirou, 2008; Mellit et al., 2009) and particularly for the predic”
• Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques (2019, methodology): “Large data is required during the training stage, and due to its multi-layered structure, the system complexity also increased [4, 97, 98].”
• Leaving the grid: An ambition or a real choice? (2015, methodology): “Some studies have also used artificial intelligence techniques for sizing PV–battery systems (Mellit et al., 2009).”