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
,
,
Alan Kwan
- School of Engineering, BRE Centre of Sustainable
Abstract
This paper uses MATLAB-Artificial NeuralNetworks (ANNs) to predict the current and future energyconsumption of new-builds in the domestic sector. This researchsimulates a prototype neighbourhood block, using multipleprototype domestic sectors across Baghdad, to predict thefuture energy use of urban projects and assess the potential useof renewable sources. This will identify how sustainablesolutions, such as solar energy, may impact on urbandevelopment compared to the conventional methods currentlyused. To construct the ANNs, data from one prototype blockfrom six which were assessed was used. Variables that directlyor indirectly impact on energy consumption were used. Thetrained ANN revealed that the use of sustainable solutions, suchas PV systems, can save energy in that there was a 33%reduction in energy consumption when comparing conventionaland sustainable energy scenarios.
Keywords
- ANN
- energy simulated
- predict energy
- urbansustainable development
How to 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. https://doi.org/ 10.7763/IJET.2018.V10.1111
Copyright & License
Copyright © 2018 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).