Data acquisition on building systems and operations in Urban Building Energy Modeling

The bottom-up physics-based Urban Building Energy Modeling (UBEM) is a framework for modeling building energy demand by utilizing physics-based energy demand simulations for individual building or using a number of reference building models representing the target building stock within a district or city. The framework holds significant potential to support cities/districts in evaluating the state of energy performance and potential reductions in carbon dioxide emissions. It also helps understand energy performance under various technological developments, energy efficiency and management scenarios. Reliable UBEM requires accurate and sufficient input data related to building physical characteristics, building systems and operation of building stock. Although useful methods have been established to represent building physical characteristics, limitations persist in acquiring building system and operation characteristics due to privacy concerns and the complexity of collecting the data. To fill the research gap, we first investigated available data sources to acquire building system and operation characteristics. We then evaluated how the data acquisition of these elements changes the performance of UBEM by developing UBEMs with different data acquisition scenarios and comparing estimated electricity demand data with smart meter data. The data acquisition scenarios were designed for building operation to represent the situations in the following: (1) a single building type classification allocated per building; (2) a single business category assigned to each floor with a common operation schedule assumed for the same business category; and (3) precise floor composition and business operation hours assigned to each floor. A data-driven building system prediction model was developed and applied to the target building stock. Due to the lack of ground truth data, we evaluated how the availability of building construction year affects building stock energy demand. The importance of the building construction year in the building system prediction model was assessed by observing the model performance with/without specifying the building construction year.

A case study was presented to validate the results and detailed performance comparisons were made for several cities in the greater Tokyo area. In the final stage, simulation outcomes were subsequently validated against existing energy consumption data to ensure simulation accuracy and reliability. The data acquisition for implementing UBEM and simulating the energy consumption at the municipality level is intended to contribute to the development of municipal plans to help achieve the national 2030 and 2050 climate targets.