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There are many data mining and machine learning techniques to manage large sets of complex energy supply and demand data for building, organization and city. As the amount of data

There are many data mining and machine learning techniques to manage large sets of complex energy supply and demand data for building, organization and city. As the amount of data continues to grow, new data analysis methods are needed to address the increasing complexity. Using data from the energy loss between the supply (energy production sources) and demand (buildings and cities consumption), this paper proposes a Semi-Supervised Energy Model (SSEM) to analyse different loss factors for a building cluster.

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    • 2015-09-14
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    Naganathan, H., Chong, W. K., & Chen, X. (2015). Semi-supervised Energy Modeling (SSEM) for Building Clusters Using Machine Learning Techniques. Procedia Engineering, 118, 1189-1194. doi:10.1016/j.proeng.2015.08.462

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