This chapter presents the use of neural network for predicting energy consumption in buildings and their expenditure. Application of artificial intelligence by the use of neural networks to predict the energy consumption for heating rehabilitated buildings is underscored by the need to develop a generic model that can be used for prediction of the consumption of the energy in buildings. The model presented for the prediction of the energy consumption of natural gas has been developed on the basis of data obtained for the winter period. Alternatively, a comparative economic study was conducted. An average error of the training phase for the model was 2.4 %, while the test phase error was 3.2 %. This indicates that the neural network model is presented successfully to predict the energy consumption by using natural gas as clean energy for heating buildings
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Entrop AG, Brouwers HJH, Reinders AHME (2010) Evaluation of energy performance indicators and financial aspects of energy saving techniques in residential real estate. Energy Build 42(5):618–629
CrossRef
2.
Akande OK, Adebamowo MA (2010) Indoor thermal comfort for residential buildings in hot-dry climate in Nigeria. In: Proceedings of conference: adapting to change: new thinking on comfort Cumberland Lodge, Windsor, 2010
3.
Emsley MW, Lowe DJ, Duff AR, Harding A, Hickson A (2002) Data modelling and the application of a neural network approach to the prediction of total costs. Constr Manag Econ 20:465–472
CrossRef
4.
Bouabaz M, Hamami M (2008) A cost estimation model for repair bridges based on artificial neural networks. Am J Appl Sci 5(4):334–339
CrossRef
5.
Ansett M, Kreider JF (1993) Application of neural networking models to predict energy use. ASHRAE Trans 99(1):505–517
6.
Kalogirou SA (2001) Artificial neural networks in renewable energy systems applications: a review. Renew Sustain Energy Rev 5:373–401
CrossRef
7.
Kreider JF, Claridge DE, Curtiss P, Haberl JS, Krarti M (1995) Building energy use prediction and system identification using recurrent networks. Trans ASME J Sol Energy Eng 117:161–166
CrossRef
8.
Breekweg MRB, Gruber P, Ahmed O (2000) Development of generalized neural network model to detect faults in building energy performance. ASHRAE Trans 43(72):61–93
9.
Melek Y, Sedat A (2005) Artificial neural networks application in building energy predictions and a case study for tropical climates. Int J Energy Res 29:891–901
CrossRef
10.
Michelis-Tzanakou E (2011) Artificial neural networks: an overview. Netw Comput Neural Syst 22:208–230
11.
Ashworth A, Skitmore RM (1982) Accuracy in estimating. Occasional paper no .27. Chartered Institute of Building, London
Über dieses Kapitel
Titel
Forecasting the Energy Consumption Using Neural Network Approach
Die B2B-Firmensuche für Industrie und Wirtschaft: Kostenfrei in Firmenprofilen nach Lieferanten, Herstellern, Dienstleistern und Händlern recherchieren.
Die Entwicklung des mitteleuropäischen Energiesystems und insbesondere die Weiterentwicklung der Energieinfrastruktur sind konfrontiert mit einer stetig steigenden Diversität an Herausforderungen, aber auch mit einer zunehmenden Komplexität in den Lösungsoptionen. Vor diesem Hintergrund steht die Weiterentwicklung von Hybridnetzen symbolisch für das ganze sich in einer Umbruchsphase befindliche Energiesystem: denn der Notwendigkeit einer Schaffung und Bildung der Hybridnetze aus systemischer und volkswirtschaftlicher Perspektive steht sozusagen eine Komplexitätsfalle gegenüber, mit der die Branche in der Vergangenheit in dieser Intensität nicht konfrontiert war. Jetzt gratis downloaden!
