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General Information
Editor-in-chief
Prof. T. Hikmet Karakoc
Anadolu University, Faculty of Aeronautics and Astronautics, Turkey

IJET 2018 Vol.10(6): 505-511 ISSN: 1793-8236
DOI: 10.7763/IJET.2018.V10.1111

Energy Forecasting, Based on ANN Machine Learning, for Domestic Properties in Dry Hot Arid Regions: A Case Study in Baghdad

Marwah M. Mohsin, Thomas Beach, and Alan Kwan
Abstract—This paper uses MATLAB-Artificial Neural Networks (ANNs) to predict the current and future energy consumption of new-builds in the domestic sector. This research simulates a prototype neighbourhood block, using multiple prototype domestic sectors across Baghdad, to predict the future energy use of urban projects and assess the potential use of renewable sources. This will identify how sustainable solutions, such as solar energy, may impact on urban development compared to the conventional methods currently used. To construct the ANNs, data from one prototype block from six which were assessed was used. Variables that directly or indirectly impact on energy consumption were used. The trained ANN revealed that the use of sustainable solutions, such as PV systems, can save energy in that there was a 33% reduction in energy consumption when comparing conventional and sustainable energy scenarios.

Index Terms—ANN, energy simulated, predict energy, urban sustainable development.

The authors are with School of Engineering, BRE Centre of Sustainable Construction, Cardiff University, UK (e-mail: MohsinMM@Cardiff.ac.uk, beachth@cf.ac.uk, kwan@cardiff.ac.uk).

[PDF]

Cite: Marwah M. Mohsin, Thomas Beach, and Alan Kwan, "Energy Forecasting, Based on ANN Machine Learning, for Domestic Properties in Dry Hot Arid Regions: A Case Study in Baghdad," International Journal of Engineering and Technology vol. 10, no. 6, pp. 505-511, 2018.

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