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Despite all the literature on building energy management, building-stock-scale models depicting its impact for energy-market-scale optimisation models are lacking. To address this shortcoming, an open-source tool called ArchetypeBuildingModel.jl has been developed for aggregating building-stock-level data into simplified lumped-capacitance thermal models compatible with existing open-source energy-system modelling frameworks. This paper aims to demonstrate the feasibility of these simplified thermal models by comparing their performance against dedicated building simulation software, as well as examining their sensitivity to key modelling and parameter assumptions. Modelling and parameter assumptions comparable to the existing literature achieved an acceptable performance according to ASHRAE Guideline 14 across all tested buildings and nodal configurations. The most robust performance was achieved with a period of variations above 13 days and interior node depth between 0.1 and 0.2 for structural thermal mass calibrations, and with external shading coefficients between 0.6 and 1.0 and solar heat gain convective fractions between 0.4 and 0.6 for solar heat gain calibrations. Furthermore, three-plus-node lumped-capacitance thermal models are recommended when modelling buildings with structures varying in terms of thermal mass. Nevertheless, the ArchetypeBuildingModel.jl performance was found to be robust against uncertain key parameter assumptions, making it plausible for energy-market-scale applications. [ABSTRACT FROM AUTHOR]

The performance gap caused by occupant behaviour (OB) is one of the main challenges to the accuracy of building performance simulations (BPS) models. Calibration of BPS models has shown great improvements in tertiary and single residential buildings. Nevertheless, the calibration in collective residential buildings is still uncertain. This study aims to identify the opportunities and barriers to the calibration of collective residential building BPS models for the analysis of heating energy consumption. For this, the research calibrates a real case study of a social rental housing building located in northern Spain. The method involves the adjustment of input data based on OB clusters, developed by monitorization and survey data and the statistical comparison of the results of normative models, calibrated models and real data. The results show an average improvement of 67% in hourly indoor temperature and 16% in hourly heating energy consumption in calibrated models, but still with a considerable performance gap. The main barriers to a higher accuracy are the wide diversity and lack of uniformity of OB patterns, uncertainty of parameters, and use of auxiliary heating systems. However, deeper monitorization and survey campaigns with the use of OB clusters can be a promising opportunity. [ABSTRACT FROM AUTHOR]

Global warming is expected to increase 1.5 °C between 2030 and 2052. This may lead to an increase in building energy consumption. With the changing climate, university campuses need to prepare to mitigate risks with building energy forecasting models. Although many scholars have developed buildings energy models (BEMs), only a few have focused on the interpretation of the meaning of BEM, including climate change and its impacts. Additionally, despite several review papers on BEMs, there is no comprehensive guideline indicating which variables are appropriate to use to explain building energy consumption. This study developed building energy prediction models by using statistical analysis: multivariate regression models, multiple linear regression (MLR) models, and relative importance analysis. The outputs are electricity (ELC) and steam (STM) consumption. The independent variables used as inputs are building characteristics, temporal variables, and meteorological variables. Results showed that categorizing the campus buildings by building type is critical, and the equipment power density is the most important factor for ELC consumption, while the heating degree is the most critical factor for STM consumption. The laboratory building type is the most STM-consumed building type, so it needs to be monitored closely. The prediction models give an insight into which building factors remain essential and applicable to campus building policy and campus action plans. Increasing STM is to raise awareness of the severity of climate change through future weather scenarios. [ABSTRACT FROM AUTHOR]

Despite all the literature on building energy management, building-stock-scale models depicting its impact for energy-market-scale optimisation models are lacking. To address this shortcoming, an open-source tool called ArchetypeBuildingModel.jl has been developed for aggregating building-stock-level data into simplified lumped-capacitance thermal models compatible with existing open-source energy-system modelling frameworks. This paper aims to demonstrate the feasibility of these simplified thermal models by comparing their performance against dedicated building simulation software, as well as examining their sensitivity to key modelling and parameter assumptions. Modelling and parameter assumptions comparable to the existing literature achieved an acceptable performance according to ASHRAE Guideline 14 across all tested buildings and nodal configurations. The most robust performance was achieved with a period of variations above 13 days and interior node depth between 0.1 and 0.2 for structural thermal mass calibrations, and with external shading coefficients between 0.6 and 1.0 and solar heat gain convective fractions between 0.4 and 0.6 for solar heat gain calibrations. Furthermore, three-plus-node lumped-capacitance thermal models are recommended when modelling buildings with structures varying in terms of thermal mass. Nevertheless, the ArchetypeBuildingModel.jl performance was found to be robust against uncertain key parameter assumptions, making it plausible for energy-market-scale applications.

Developing methodologies to accurately characterise the energy conditions of existing building stock is a fundamental aspect of energy consumption reduction strategies. To that end, a case study using a thermal information modelling method for existing buildings (as-is T-BIM) is reported. This proposed new method is based on the automatic processing of 3D thermal clouds of interior zones of a building that generates a semantic proprietary model that contains time series of surface temperatures assigned to its surface elements. The proprietary as-is T-BIM automatically generates an as-is BEM model with gbXML standards for energy simulation. This is a multi-zone energy model of the building. In addition, the surface temperature data series of the as-is T-BIM model elements permit the calculation of their thermal transmittances, increasing the calibration options of the obtained as-is BEM model. To test the as-is TBIM method, a case study compares the as-is BEM model obtained by as-is T-BIM methods with the one obtained by standard methods for the same building. The results demonstrate differences in geometry, transmittance, and infiltration values, as well as insignificant differences in annual air conditioning energy consumption or the comfort parameters tested. This seems to indicate shorter modelling times and greater accuracy of the as-is T-BIM model. [ABSTRACT FROM AUTHOR]

Several studies have indicated a significant potential in using Model Predictive Control (MPC) of space heating for demand response purposes. The performance of the MPC depends on the predictive performance of the embedded control model. The studies often employ black- or grey-box control models; however, no previous studies consider whether a black- or grey-box model is more robust against weather changes. To assess this, the simulation-based study reported in this paper analysed how the predictive performance of black- and grey-box models trained with different input–output datasets from a certain period of a year is affected when subject to weather conditions in other periods of the year. The predictive performance of the grey-box models was slightly better compared to the black-box model. Furthermore, the grey-box models were slightly more robust to changes in weather data. Future studies should investigate whether the differences have practical significance in relation to MPC. [ABSTRACT FROM AUTHOR]

Global warming is expected to increase 1.5 °C between 2030 and 2052. This may lead to an increase in building energy consumption. With the changing climate, university campuses need to prepare to mitigate risks with building energy forecasting models. Although many scholars have developed buildings energy models (BEMs), only a few have focused on the interpretation of the meaning of BEM, including climate change and its impacts. Additionally, despite several review papers on BEMs, there is no comprehensive guideline indicating which variables are appropriate to use to explain building energy consumption. This study developed building energy prediction models by using statistical analysis: multivariate regression models, multiple linear regression (MLR) models, and relative importance analysis. The outputs are electricity (ELC) and steam (STM) consumption. The independent variables used as inputs are building characteristics, temporal variables, and meteorological variables. Results showed that categorizing the campus buildings by building type is critical, and the equipment power density is the most important factor for ELC consumption, while the heating degree is the most critical factor for STM consumption. The laboratory building type is the most STM-consumed building type, so it needs to be monitored closely. The prediction models give an insight into which building factors remain essential and applicable to campus building policy and campus action plans. Increasing STM is to raise awareness of the severity of climate change through future weather scenarios.

An approach to automatically generate a dynamic energy simulation model in Modelica for a single existing building is presented. It aims at collecting data about the status quo in the preparation of energy retrofits with low effort and costs. The proposed method starts from a polygon model of the outer building envelope obtained from photogrammetrically generated point clouds. The open-source tools TEASER and AixLib are used for data enrichment and model generation. A case study was conducted on a single-family house. The resulting model can accurately reproduce the internal air temperatures during synthetical heating up and cooling down. Modelled and measured whole building heat transfer coefficients (HTC) agree within a 12% range. A sensitivity analysis emphasises the importance of accurate window characterisations and justifies the use of a very simplified interior geometry. Uncertainties arising from the use of archetype U-values are estimated by comparing different typologies, with best- and worst-case estimates showing differences in pre-retrofit heat demand of about ±20% to the average; however, as the assumptions made are permitted by some national standards, the method is already close to practical applicability and opens up a path to quickly estimate possible financial and energy savings after refurbishment. [ABSTRACT FROM AUTHOR]