State of the art in building modelling and energy performances prediction: A review

Abstract

In the European Union, the building sector is one of the largest consumer of energy with about 40% of the final energy consumption. Reducing consumption is, at the same time, a sociological, technological and scientific matter. New methods have to be devised in order to support building professionals in their effort to optimize designs and to enhance energy performances. Indeed, the research field related to building modelling and energy performances prediction is very productive, involving various scientific domains. Among them, one can distinguish physics-related fields, focusing on the resolution of equations simulating building thermal behaviour and mathematics-related ones, consisting in the implementation of prediction model thanks to machine learning techniques. This paper proposes a detailed review of these works. First, the approaches based on physical ("white box") models are reviewed according three-category classification. Then, we present the main machine learning ("black box") tools used for prediction of energy consumption, heating/cooling demand, indoor temperature. Eventually, a third approach called hybrid ("grey box") method is introduced, which uses both physical and statistical techniques. The paper covers a wide range of research works, giving the base principles of each technique and numerous illustrative examples.

Numerical analysis of passive strategies for energy retrofit of existing buildings in Mediterranean climate: thermal mass and natural ventilation combination

“…We implemented dynamic multi-zonal numerical simulation, which represents a good compromise between computation time and the level of analysis required for the current study, producing immediate results on hygrothermal conditions, air flow, comfort and energy consumption of the simulated indoor environment [59][60][61][62][63][64]. Compared to the systems described in Section 2.2, we excluded natural ventilation systems not directly affecting indoor air quality and those not immediately connected to an intervention on the single building unit.…”

Handling data uncertainties when using Swedish energy performance certificate data to describe energy usage in the building stock

“…3 The two most used methods of deriving Atemp are to either measure it using the building plan or to derive it from BOA and LOA. 4 Boverket recommends Atemp to be derived from BOA and LOA for larger multi-family-dwellings by applying a factor of 1.15 to the addition of BOA and LOA, or a factor of 1.25 if there is a heated basement. Prior to this recommendation Göransson [28] made a study on the relationship between Atemp and BOA + LOA based on 151 multi-family-dwellings.…”

“…Atemp is defined as the heated floor space including shared spaces and footprints of walls but not including garages. 4 BOA and LOA are real estate valuation measures of usable heated floor area for habitation and non-habitation (stair cases, shared areas etc.). BOA and LOA are registered for 90% of the multi-family-dwellings and 74% of the single family buildings.…”

“…The Swedish government has set an additional target of reducing the energy usage in the building sector by 50% by 2050 [3]. Having an overview of energy usage in the building stock is necessary for creating a refurbishment strategy, as engineering-based models are used in refurbishment strategies to predict energy savings for buildings after the application of renovation measures [4][5][6]. The difficulties with using engineering-based models on a city scale is that reliable data on energy usage on the building level is limited [7].…”

Thermophysical aspects of reconstruction of cold roof spaces

“…Scientists [8] analyzed the thermal field forming of the exterior wall of frontal concrete panel with mounting hooks. Scientists [9,10] researched the thermal qualities of sandwich panels with flexible links and reinforced concrete dowels.…”