Hybrid probabilistic-possibilistic treatment of uncertainty in building energy models : a case study of sizing peak cooling loads

International audienceOptimal sizing of peak loads has proven to be an important factor affecting the overall energy consumption of heating ventilation and air-conditioning (HVAC) systems. Uncertainty quantification of peak loads enables optimal configuration of the system by opting for a suitable size factor. However, the representation of uncertainty in HVAC sizing has been limited to probabilistic analysis and scenario-based cases, which may limit and bias the results. This study provides a framework for uncertainty representation in building energy modeling, due to both random factors and imprecise knowledge. The framework is shown by a numerical case study of sizing cooling loads, in which uncertain climatic data are represented by probability distributions and human-driven activities are described by possibility distributions. Cooling loads obtained from the hybrid probabilistic-possibilistic propagation of uncertainty are compared to those obtained by pure probabilistic and pure possibilistic approaches. Results indicate that a pure possibilistic representation may not provide detailed information on the peak cooling loads, whereas a pure probabilistic approach may underestimate the effect of uncertain human behavior. The proposed hybrid representation and propagation of uncertainty in this paper can overcome these issues by proper handling of both random and limited data

The hygrothermal performance of residential buildings at urban and rural sites: Sensible and latent energy loads and indoor environmental conditions

Cities often show nighttime air temperatures higher by 3-4. °C than adjacent non-urban areas. This yields to cooling loads in average higher by 13% for urban than rural buildings. Here we assess the hygrothermal performance and the heating and cooling loads of a reference building representative of the Italian stock. We compare its performance calculated with hourly urban weather data (2002-2008) with the performance of the same building using a rural dataset instead. Milan's Urban Heat Island reduces the heating loads by 12% and 16%, for the non-insulated and insulated building, respectively, while the cooling loads are increased by 41% and 39%. The urban building also shows dehumidification loads 74-78% lower than the rural building. Moreover, during the 2003 heat wave, the indoor air temperature is computed to be 1.5. °C-2.2. °C higher in a non-conditioned urban building than in the rural one. This increases the wakefulness, occupants' vulnerability to overheating, and impacts the overall hygrothermal performance. Our findings highlight the need of a different design concept for urban with respect to non-urban buildings, even though they are, by law, in the same climate zone

architecture-building-systems/CityEnergyAnalyst: CityEnergyAnalyst v.3.34.2

Enhanced zone helper to store street address [v3.34.2] 19 September 2023, in Zurich & Singapore zone_helper now saves the street addresses and postal codes of the buildings fetched from OpenStreetMap. The enhanced zone_helper also stores house names and residential types (for Singapore HDB Buildings only). The CEA Team # What's Changed Release 3.34.1 by @shizhongming in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3380 Update sg_energy_optimization.yml by @shizhongming in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3381 correcting typos by @shizhongming in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3382 Saving additional info zone helper by @shizhongming in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3388 Full Changelog: https://github.com/architecture-building-systems/CityEnergyAnalyst/compare/v.3.34.1...v3.34.