Ubem's Archetypes Improvement Via Data-Driven Occupant-Related Schedules Randomly Distributed and Their Impact Assessment

32 Pages Posted: 10 Apr 2022

See all articles by Martina Ferrando

Martina Ferrando

Polytechnic University of Milan

Sibilla Ferroni

Polytechnic University of Milan

Martina Pelle

affiliation not provided to SSRN

Anita Tatti

Polytechnic University of Milan

Silvia Erba

Polytechnic University of Milan

Francesco Causone

Polytechnic University of Milan

Abstract

In Urban Building Energy Model (UBEM) tools, buildings are usually modelled via archetypes describing occupants’ behaviour via fixed schedules. This research (i) creates data-driven schedules for electric use and occupancy derived from smart meter readings randomly distributed in the model to improve residential archetypes, (ii) assesses the impact of these schedules on UBEMs' energy results at different temporal resolutions and spatial scales. The novel assessment procedure exploits an integrated heat map based on the coefficient of variation of the root means square error (CVRMSE). The outcomes show that the differences in energy needs, with randomized schedules, range based on temporal and spatial aggregation. Yearly, for the entire neighbourhood, heating and cooling energy needs, and electric uses are estimated respectively -2%, +1%, and +18%, compared to the base case. The outputs show the importance of focusing on data-driven schedules randomly distributed when the analysis output is hourly or daily, also spatial aggregation must be considered. Particularly, when simulations are focused on the whole model (e.g., the district), fixed schedules can be enough to describe energy patterns. However, if the simulation is focused on small groups of buildings (e.g., 5 or fewer), randomising the schedules improves the reliability of the simulation.

Keywords: Urban Building Energy Model (UBEM), Building Archetype, Smart Meter, Clustering, Occupant Behaviour (OB), urban modelling interface (umi)

Suggested Citation

Ferrando, Martina and Ferroni, Sibilla and Pelle, Martina and Tatti, Anita and Erba, Silvia and Causone, Francesco, Ubem's Archetypes Improvement Via Data-Driven Occupant-Related Schedules Randomly Distributed and Their Impact Assessment. Available at SSRN: https://ssrn.com/abstract=4080326 or http://dx.doi.org/10.2139/ssrn.4080326

Martina Ferrando (Contact Author)

Polytechnic University of Milan ( email )

Piazza Leonardo da Vinci
Milan, 20100
Italy

Sibilla Ferroni

Polytechnic University of Milan ( email )

Piazza Leonardo da Vinci
Milan, 20100
Italy

Martina Pelle

affiliation not provided to SSRN ( email )

No Address Available

Anita Tatti

Polytechnic University of Milan ( email )

Piazza Leonardo da Vinci
Milan, 20100
Italy

Silvia Erba

Polytechnic University of Milan ( email )

Piazza Leonardo da Vinci
Milan, 20100
Italy

Francesco Causone

Polytechnic University of Milan ( email )

Piazza Leonardo da Vinci
Milan, 20100
Italy

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