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and Heating","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"# Energy Signature Analyser\n\nThis is a toolbox to analyse energy signatures of buildings and compare the signatures of all buildings within an entire building stock.\nEnergy signatures are simple scatter plots comparing outside air temperature (x-axis) with the building's energy consumption (usually smart meter data resampled to daily mean).\nBy analysing the shape of such a scatter plot one can estimate the buildings' energy efficiency on the one side,\non the other one can extract characteristics of buildings like the installed heating system or which type of building it is.\n\n## Method\nThis stack of codes explicitly aims at extracting prevailing building characteristics in a building stock. Therefore,\nthe shape of each building's energy signature is extracted (using a univariate Support Vector Regression model), and similar shapes clustered (Dynamic Time Warping based). The clustering helps to estimate\nthe type of each building (apartment, free standing) and its heating system (gas furnace, heat pump, electrict resistance heater).\n\n![](Method.png)\n\n## Overview on codes\nThe analysis is conducted by calling methods of two classes. One class is dedicated to analyse the energy signature\nof each building individually (Python module ES.py) and the second class jointly analyses all buildings in a stock (ES_buildingstock.py).\n\n![](Structure_Code.png)\n\n## Application\nThe toolbox helps to automatically conduct data-driven retrofit analysis to:\n* Find building characteristcs\n* Find building specific performance metrics (heating rate, cooling rate)\n* Benchmarking performance of buildings with similar characteristics.\n\n\n## Contact:\n\nPaul Westermann [Energy in Cities group, University of Victoria, Canada](https://energyincities.gitlab.io/website/)\n\n\u003cimg src=\"uvic_logo.jpg\" alt=\"drawing\" width=\"200\"/\u003e\n","funding_links":[],"readme_doi_urls":[],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":[],"project_url":"https://ost.ecosyste.ms/api/v1/projects/327","html_url":"https://ost.ecosyste.ms/projects/327"}