Applications of artificial neural-networks for energy systems

By Soteris A. Kalogirou. 2000

777 citations recorded by the source corpus.

Artificial neural networks offer 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 predictions and generalisations 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 mapping and system identification. This paper presents various applications of neural networks in energy problems in a thematic rather than a chronological or any other way. 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's 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 flows in a naturally ventilated test room and for the prediction of the energy consumption of a passive solar building. In all such models, a multiple hidden-layer architecture has been used. Errors reported when using these models are well within acceptable limits, which clearly suggests that artificial neural-networks can be used for modelling in other fields of energy production and use. The work of other researchers in the field of energy is also reported. This includes the use of artificial neural-networks in heating, ventilating and air-conditioning systems, solar radiation, modelling and control of power-generation systems, load-forecasting and refrigeration.

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

View Paper: https://www.semanticscholar.org/paper/ca6fe1cc235db94b651be25d577b5f41158ebf9d

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

In Building and Construction

In Electrical and Electronic Engineering

Citations in the corpus

Modelling and experimental performance analysis of solar-assisted ground source heat pump system (2017, methodology): “For energy systems, Kalogirou (2000) used applications of ANNs.”
State of the Art of Machine Learning Models in Energy Systems, a Systematic Review (2019, background): “ANNs can be utilized for sev ral purposes such as forecasting, regression and curve fitting.”
Machine learning for estimation of building energy consumption and performance: a review (2018, methodology): “Kalogirou & Bojic (Kalogirou and Bojic 2000; Kalogirou 2000) applies RNN to predict hourly energy demand of a passive solar building.”