Applications of artificial neural-networks for energy systems
AbstractArtificial 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.
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Bibliographic InfoArticle provided by Elsevier in its journal Applied Energy.
Volume (Year): 67 (2000)
Issue (Month): 1-2 (September)
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- Colorado, D. & Hernández, J.A. & Rivera, W. & Martínez, H. & Juárez, D., 2011. "Optimal operation conditions for a single-stage heat transformer by means of an artificial neural network inverse," Applied Energy, Elsevier, vol. 88(4), pages 1281-1290, April.
- Mohanraj, M. & Jayaraj, S. & Muraleedharan, C., 2012. "Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1340-1358.
- Jebaraj, S. & Iniyan, S., 2006. "A review of energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(4), pages 281-311, August.
- Najafi, G. & Ghobadian, B. & Tavakoli, T. & Buttsworth, D.R. & Yusaf, T.F. & Faizollahnejad, M., 2009. "Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network," Applied Energy, Elsevier, vol. 86(5), pages 630-639, May.
- Sözen, Adnan, 2009. "Future projection of the energy dependency of Turkey using artificial neural network," Energy Policy, Elsevier, vol. 37(11), pages 4827-4833, November.
- Sözen, Adnan & Arcakliog[caron]lu, Erol, 2005. "Effect of relative humidity on solar potential," Applied Energy, Elsevier, vol. 82(4), pages 345-367, December.
- Sözen, Adnan & Arcaklioglu, Erol & Özalp, Mehmet & Kanit, E. Galip, 2004. "Use of artificial neural networks for mapping of solar potential in Turkey," Applied Energy, Elsevier, vol. 77(3), pages 273-286, March.
- Sözen, Adnan & Arcaklioglu, Erol & Özkaymak, Mehmet, 2005. "Turkey's net energy consumption," Applied Energy, Elsevier, vol. 81(2), pages 209-221, June.
- Fadare, D.A., 2010. "The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria," Applied Energy, Elsevier, vol. 87(3), pages 934-942, March.
- Sözen, Adnan & Arcaklioglu, Erol & Özalp, Mehmet & Kanit, E. Galip, 2005. "Solar-energy potential in Turkey," Applied Energy, Elsevier, vol. 80(4), pages 367-381, April.
- Matallanas, E. & Castillo-Cagigal, M. & Gutiérrez, A. & Monasterio-Huelin, F. & Caamaño-Martín, E. & Masa, D. & Jiménez-Leube, J., 2012. "Neural network controller for Active Demand-Side Management with PV energy in the residential sector," Applied Energy, Elsevier, vol. 91(1), pages 90-97.
- Alam, Shah & Kaushik, S.C. & Garg, S.N., 2009. "Assessment of diffuse solar energy under general sky condition using artificial neural network," Applied Energy, Elsevier, vol. 86(4), pages 554-564, April.
- Mohanraj, M. & Jayaraj, S. & Muraleedharan, C., 2009. "Performance prediction of a direct expansion solar assisted heat pump using artificial neural networks," Applied Energy, Elsevier, vol. 86(9), pages 1442-1449, September.
- Sözen, Adnan & Arcaklioglu, Erol, 2005. "Solar potential in Turkey," Applied Energy, Elsevier, vol. 80(1), pages 35-45, January.
- Sözen, Adnan & Ali Akçayol, M., 2004. "Modelling (using artificial neural-networks) the performance parameters of a solar-driven ejector-absorption cycle," Applied Energy, Elsevier, vol. 79(3), pages 309-325, November.
- Ceylan, Ilhan & Aktas, Mustafa, 2008. "Modeling of a hazelnut dryer assisted heat pump by using artificial neural networks," Applied Energy, Elsevier, vol. 85(9), pages 841-854, September.
- Kara Togun, Necla & Baysec, Sedat, 2010. "Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural networks," Applied Energy, Elsevier, vol. 87(1), pages 349-355, January.
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