Artificial neural networks in renewable energy systems applications: a review

By Soteris A. Kalogirou. 2001

1,128 citations recorded by the source corpus.

Artificial neural networks are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with non-linear problems and, once trained, can perform prediction and generalisation at high speed. They have been used in diverse applications in control, robotics, pattern recognition, forecasting, medicine, power systems, manufacturing, optimisation, signal processing and social/psychological sciences. They are particularly useful in system modelling such as in implementing complex mappings and system identification. This paper presents various applications of neural networks mainly in renewable energy problems in a thematic rather than a chronological or any other order. Artificial neural networks have been used by the author in the field of solar energy; for modelling and design of a solar steam generating plant, for the estimation of a parabolic trough collector intercept factor and local concentration ratio and for the modelling and performance prediction of solar water heating systems. They have also been used for the estimation of heating loads of buildings, for the prediction of air flow in a naturally ventilated test room and for the prediction of the energy consumption of a passive solar building. In all those models a multiple hidden layer architecture has been used. Errors reported in these models are well within acceptable limits, which clearly suggest that artificial neural networks can be used for modelling in other fields of renewable energy production and use. The work of other researchers in the field of renewable energy and other energy systems is also reported. This includes the use of artificial neural networks in solar radiation and wind speed prediction, photovoltaic systems, building services systems and load forecasting and prediction.

Subfields: Artificial Intelligence, Building and Construction, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/dadf8d6d493742c549ec3dc3c7edffe39c54f117

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Citations in the corpus

A roadmap towards intelligent net zero- and positive-energy buildings (2011, methodology): “…(fine-tuning) procedure after the initial deployment of the BO&C system (see e.g. Dalamagkidis et al., 2007; Kolokotsa et al., 2002, 2001; Bruant et al., 2001; Curtiss et al., 1994; Dounis and Caraiscos, 2009; Gouda et al., 2006; Kalogirou, 2000; Kreider et al., 1992; Wright et al., 2002).”
Applications of artificial neural networks for thermal analysis of heat exchangers – A review (2015, background): “Similarly, many researchers had reviewed the applications of ANN for refrigeration, air conditioning and heat pump systems [12], heat transfer problems in nuclear engineering [13], sizing of solar photovoltaic systems [14], modeling and control of combustion processes [15], modeling of energy system”
Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems—A review (2012, neutral): “[9] Kalogirou SA.”