Estimating Energy Efficiency of Buildings Matthew Wysocki.

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Presentation transcript:

Estimating Energy Efficiency of Buildings Matthew Wysocki

Introduction  Research into building efficiency  Heating, ventilation, and cooling  Software simulations  UCI Machine Learning Repository

Dataset  Generated using Ecotect  Using 8 different parameters  Relative compactness  Surface area  Wall area  Roof Area  Overall Height  Orientation  Glazing Area  Glazing Area Distribution  Constant volume  Same Materials  768 samples

Algorithm  Regression tree  Each node represents a binary decision  Leaves represent outputs  Random forest method

Correlation coefficients (Heating load only) Input ValuePearson product- moment coefficient Spearman’s rank correlation coefficient Kendall’s rank correlation coefficient Relative Compactness Surface Area Wall area Roof area Overall height Orientation Glazing Area Glazing Area Distribution

Estimating Error Output variableMean Absolute Error Mean Squared Error Mean Relative Error Heating load Cooling

Conclusions  Accurate estimates of outputs based on input variables  Good understanding of correlations  Unnecessary to run many simulations

References  Tsanas, A. Xifara: 'Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools', Energy and Buildings, Vol. 49, pp , 2012  Lee, S., Park, Y., and Kim, C. (2012) Investigating the Set of Parameters Influencing Building Energy Consumption. ICSDC 2011: pp *Figures without references were generated by me