High Resolution Space-time Data: Methodology For Residential Building Simulation Modelling

Abstract

A bottom-up approach is developed for the specification of operational data with a high spacetime resolution, to be used as inputs in multi-zone residential building models. These archetype models will be used to analyse demand modulation of total domestic electricity consumption, thus requiring a detailed knowledge of domestic loads. The approach is based on national Time-Use Survey (TUS) resident activity data. To illustrate the approach, the EnergyPlus simulation platform is used to model a multi-zone case study building. Occupancy profiles, lighting load and disaggregated electrical appliance load profiles, as well as their associated heat gains, are generated and spatially mapped within the building. A good match is seen between synthesised and measured profiles. A greater sharing of electrical appliances, as the household size increases, is also seen. Fifteen-minute resolution of the model outputs is found to be sensible in the context of the current project, due to aggregation.

On the feasibility of affordable high-fidelity CFD simulations for indoor environment design and control

“…Therefore, a relative error of the global quantities should remain below 5%, as it is suggested in [52]. A building project in many cases requires several representative daily simulations [53]. We assume the simulation speed for HVAC design should be at least twice faster than real-time (R 0.5) so that an engineer can start a daily simulation at the end of the working day and obtain the results the next morning.…”

A three-year dataset supporting research on building energy management and occupancy analytics

“…Measuring ground truth at high resolution in all buildings is impractical and challenging 5,6 . Therefore, it is critical to collect, curate, and make publicly available high-resolution data from a small number of buildings that have broad applicability to a variety of high-impact use cases.…”

Prediction of Residential Building Demand Response Potential Using Data-Driven Techniques

“…The mid-floor apartment utilised is considered an archetype due to the integration of the necessary operational data with high space and time resolution (15-minutes). This data subset is built upon the bottom-up approach proposed by Neu et al (2013), by implementing an adjusted Markov-Chain Monte Carlo technique, pioneered by Richardson et al (2008). This technique is applied to the 2005 Irish time-use survey (TUS) activity data (ESRI, 2005a), thus taking into account end-user behaviour.…”

“…This technique is applied to the 2005 Irish time-use survey (TUS) activity data (ESRI, 2005a), thus taking into account end-user behaviour. Activity-specific profiles for occupancy, electrical appliance and lighting use, domestic hot water (DHW) demand (Neu et al, 2013(Neu et al, , 2014(Neu et al, , 2016 are developed following this technique. These operational inputs were verified against surveyed (ESRI, 2005b) and metered data (CER, 2012).…”