A data-driven procedure to model occupancy and occupant-related electric load profiles in residential buildings for energy simulation

Abstract

Improving the reliability of energy simulation outputs is becoming a pressing task to reduce the performance gap between the design and the operation of buildings. Occupant behaviour modelling is one of the most relevant sources of uncertainty in building energy modelling and is typically modelled via a priori choices made by modellers. Thus, an improvement in the description of occupant behaviour is needed. To this regard, the availability of smart meter recordings might help to generate more reliable input data for building energy models. This paper discusses a novel data-driven procedure that enables to create yearly occupancy and occupantrelated electric load profiles to inform building energy modelling, using a typical uneven database made available by energy operators. The procedure is subdivided into three main tasks. The first has the intent to detect representative occupant-related electric load profiles from smart meters readings. The second task aims to generate yearly occupancy profiles from the same database. The last task assesses the impact of the generated occupancy and occupant-related electric load profiles on building energy simulation outputs. The procedure is applied to the case study of a multi-residential building in Milan, Italy and is meant to show the possibility to overcome deterministic inputs that might have little relation with the actual building operation. It showed a substantial improvement in the reliability of building energy simulation and that occupant related load profiles may account for about 8 % of the building's energy need for space heating.

Building stock simulation to support the development of a district multi-energy grid

“…Indeed, building and cities do not use energy, but people do, and to oversimplify or ignore the human factor in building simulations could lead to a mismatch between predicted and real energy use [22], called "performance gap" [29]. In this work, we decided not to use deterministic scheduled profiles of occupation and internal heat gains, but to simulate, during the year, the occupancy and the occupant-related electric energy used by appliances through stochastic schedules, which have been created using a data-driven procedure, developed by Causone et al [30]. The schedules have been set up starting from metered data of a residential building located in Milan, using machine learning techniques.…”

“…These values are shown in Table 4. Both the average value of the electricity uses and the probability profiles have been derived from the metered dataset of the building analysed by Causone et al [30], which is a residential building similar to the ones hereby studied. Both buildings are residential and located in a suburban area of Milan.…”

Italian prototype building models for urban scale building performance simulation

“…UBEM differs from building energy modeling (BEM) of individual buildings, achieved through traditional building performance simulation (BPS) software, mainly in the size of the stock modeled and usually on the descriptive data availability. BEM is primarily focused on single buildings or small blocks, usually with detailed information of individual buildings (i.e., detailed description of layers of the envelope constructions [5]; geometry, heating, ventilation and air conditioning (HVAC) systems; occupants' behavior [6]), including highly detailed analyses [7]. Conversely, UBEM models at least a large block (i.e., dozens of buildings) and up to all buildings in a city (ranging from tens of thousands to millions of buildings).…”

Influence of Data Sampling Frequency on Household Consumption Load Profile Features: A Case Study in Spain

“…More specifically, the choice of a temporal data granularity (data sampling frequency) for specifying consumption load profile features has a crucial impact on the results of any action or assessment, as discussed in the literature [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ], see Table 1 . This table summarizes for each potential action or assessment the time resolution (data granularity) and time horizon (time slice) envisaged for the works related to load profiles in households.…”