Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools

By Athanasios Tsanas, Angeliki Xifara. 2012

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Citations in the corpus

Making Tree Ensembles Interpretable (2016, methodology): “We evaluated the performance of the proposed method on a synthetic data and on an energy efficiency data (Tsanas & Xifara, 2012)1.”
Application of machine learning to construction injury prediction (2016, methodology): “Finally, Yang et al. [76] developed an algorithm to automatically track workers in digital videos; Tsanas and Xifara [68] used RF to predict heating and cooling loads of residentialbuildings fromwallarea,glazing area,overallheight, and other input variables; and Son et al. [61] used a support…”
Building Predictive Models via Feature Synthesis (2015, methodology): “Dataset p Exemplars”
Expert Systems With Applications (unknown): “Energy Eﬃciency data sets ( Tsanas & Xifara, 2012 ) are obtained by running simulation model.”
A Comparative Study of PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN in Estimating the Heating Load of Buildings’ Energy Efficiency for Smart City Planning (2019, methodology): “For data collection, twelve types of buildings were investigated and simulated by Ecotect computer software [13].”
Convex Formulations for Fair Principal Component Analysis (2018, unknown): “2013) 2 (Horton and Nakai 1996) 3 (Tsanas and Xifara 2012) 4 (Bock et al.”
A review of the-state-of-the-art in data-driven approaches for building energy prediction (2020, result): “Similar studies could also be found in references [44]–[47].”
Multi-target regression via input space expansion: treating targets as inputs (2012, background): “The Energy Building dataset (Tsanas and Xifara 2012) concerns the prediction of the heating load and cooling load requirements of buildings (i.”