Tertiary building stock modeling: Area determination by fusion of different datasets Tertiary building stock modeling: Area determination by fusion of different datasets

International audienceThe building stock accounts for 43% of the total French final energy consumption in 2017. The French building stock mainly includes residential and tertiary buildings, which have very different consumption patterns. Today, building stock models are largely used in many countries for energy demand forecast and decision making. In this context, good knowledge of the building stock is essential for modeling. However, this type of model is lacking for the tertiary sector, as it is difficult to categorize buildings that have a wide variety of area and energy uses, different systems and complex dynamic trends. Existing stock models are mostly based on an aggregated representation of the total heated floor area of the building stock. Such a representation limits explicit studies of the factors affecting the energy demand and fails at taking into account heterogeneity. This paper exposes and discusses new methods aiming at modeling the tertiary building stock at the city scale in France. While mainly relying on open source public data, methods are designed to be applicable at any scale (from small cities to large region or even national scale). 1. Introduction For many years, building energy modeling and simulation have been largely used in advising decision makers in the domain of energy policy [1]. The building stock accounts for 43% of the total French final energy consumption in 2017 [2]. The tertiary sector is responsible for one third of the total building stock's consumption. Whereas considerable research has been devoted to the residential building stock, less attention has been paid to the tertiary building stock. Heterogeneity is the main reason that makes it difficult to study the tertiary sector. The representation of the tertiary building stock is usually based on the total heated floor area of economic sub-sectors, aggregated at national scale, omitting heterogeneity. Through this work, we develop a building stock model which allows a detailed description of this sector's building stock. The purpose of this model is to study the energy demand as well as long-term projections of the tertiary stock. Recently, detailed individual building and building stock models have begun to merge into hybrid methods that might help evaluating the energy demand of a group of buildings, which may contain from dozens to thousands individuals [3]. Our ambition is to represent the tertiary building stock as a group of individual buildings. The first important step is to determine the area of each building. Many building stock models use the area as a key variable for estimating the energy demand. For example in the econometric model NEMS-National Energy Modeling System, the US tertiary building stock is described in its Commercial Demand Module [4]. In NEMS, the floor area is used in many o