A novel residential heating consumption characterisation approach at city level from available public data: Description and case study

Improvement Proposal of Bottom-Up Approach for the Energy Characterization of Buildings in the Tropical Climate

“…To work towards a scenery of building with better energy efficiency in Colombia, the Ministry of Mines and Energy, Ministry of Science, Technology, and Innovation, and the Universidad Industrial de Santander established technical guidelines to create an energylabeling system of buildings-ELSB. Its creation demands a detailed comprehension of the energy behavior of the buildings [2,4,5]. In general, this is carried out in four steps: (i) selection of a representative sample of buildings belonging to the building category; (ii) collection of detailed data of buildings; (iii) characterization of energy behavior of selected buildings through methods of modeling and engineering; and (iv) extrapolation and application of obtained results taking into account the number of buildings of the category [4].…”

“…On the other hand, the bottom-up methods use models of buildings that represent the category in [2,5]. They can be classified as statistical methods based on historical data and engineering methods that allow determining the energy consumption according to models based and physical principles, building characteristics, and the use of the equipment and inner spaces [5,6]. These representative energy models of buildings allow the prediction of energy consumptions and estimating the impacts of the potential implementation of energy efficiency strategies [7].…”

“…The archetypes are energy simulation models created (no reals) from a combination of several characteristics common or representative of a building category with similar attributes [5], while a sample is a group of real buildings that represents the whole category.…”

“…Potentially, the use of a building sample can more accurately reflect the heterogeneity of a category of existing buildings than an archetype. Nevertheless, a significant number of representative buildings or an expert analysis is necessary to ensure that the characteristics of the sampling are convenient to extrapolate the results [5].…”

“…First, the uncertainty of input data, as detailed use of appliances and behavior of users, reduces the reliability of the energy modeling [7][8][9][10][11]. Frequently, the energy characterization studies are limited for data availability, for that the analysis team tends to complete the lack information according to their expertise [5,12,13]. Second, there are insufficiencies of the detail level of results to describe the energy behavior of the buildings category and, thus, to identify energy-saving measures and their impact [5,12].…”