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The large-scale implementation of demand-response measures has the potential to play a significant role in overcoming the various issues related to electricity supply/demand imbalances. This paper assesses various building energy modelling techniques and compares them using a schematic representation; the integration of the building models into a demand response scheme is then addressed and guidelines are given for different contexts. From the review carried out, the paper highlights the need to develop demand-response estimation tools at a large-scale taking into account the buildings characteristics. An outlook is given on a proposed method for large-scale demand response estimation. [ABSTRACT FROM AUTHOR]

Abstract: With the increasing demand for more energy efficient buildings, the construction industry is faced with the challenge to ensure that the energy performance predicted during the design stage is achieved once a building is in use. There is, however, significant evidence to suggest that buildings are not performing as well as expected and initiatives such as PROBE and CarbonBuzz aim to illustrate the extent of this so called ‘performance gap’. This paper discusses the underlying causes of discrepancies between energy modelling predictions and in-use performance of occupied buildings (after the twelve month liability period). Many of the causal factors relate to the use of unrealistic input parameters regarding occupancy behaviour and facilities management in building energy models. In turn, this is associated with the lack of feedback to designers once a building has been constructed and occupied. The paper aims to demonstrate how knowledge acquired from Post-Occupancy Evaluation (POE) can be used to produce more accurate energy performance models. A case study focused specifically on lighting, small power and catering equipment in a high density office building is analysed and presented. Results show that by combining monitoring data with predictive energy modelling, it was possible to increase the accuracy of the model to within 3% of actual electricity consumption values. Future work will seek to use detailed POE data to develop a set of evidence based benchmarks for energy consumption in office buildings. It is envisioned that these benchmarks will inform designers on the impact of occupancy and management on the actual energy consumption of buildings. Moreover, it should enable the use of more realistic input parameters in energy models, bringing the predicted figures closer to reality. [Copyright &y& Elsevier]

Highlights • A 12 month mixed-method longitudinal study of energy demand in Kuwaiti villas. • Building energy models grounded with physical and social empirical data. • Space cooling accounts for 50% to 75% of total energy use in the case study villas. • Air conditioning thermostat settings are a key driver of energy use. • Insulation and double glazing contribute to more uniform internal temperatures. Abstract This paper investigates the physical and social determinants of energy use in four case study villas in Kuwait. The investigation consisted of a 12 month mixed-method longitudinal monitoring and surveying study followed by building energy modelling. Monitoring and surveying provided empirical data about household energy use, internal temperatures, building and system characteristics and occupant energy-use behaviours. This data was used to ground models to better predict energy use. Models grounded with empirical data show that space cooling accounts for 50% to 75% of total energy use in villas. Results suggest that drivers of cooling energy are varied, with 30% of cooling energy in some villas attributable to heat gains from appliances. Furthermore, while the study found no distinct reduction in measured and modelled annual energy use intensities of villas with improved levels of efficiency (insulation and double glazing), such measures did contribute to more uniform internal temperatures. The study also identified key occupant energy-consuming behaviours including a tendency not to adjust air conditioning (AC) thermostat settings during periods of summer travel and lack of regular AC maintenance. The approach and findings of the study can inform future studies in Kuwait seeking to understand energy use in a nationally representative sample. [ABSTRACT FROM AUTHOR]

Abstract Sensitivity analysis (SA) can be applied to building energy models (BEM) to identify which input parameters that drive the majority of the model output variation. The screening-based Morris method is often applied for this purpose; however, consideration regarding the effect of the user-defined number of levels (p) and trajectories (r) on the obtained results are rare. This paper investigates how the choice of p and r affects the outcome of a SA using the Morris method on a high fidelity BEM. The results indicates that the Morris method was not able to replicate the ranking from the variance-based Sobol' method no matter the choice of r and p. It was, however, able to identify groups of input parameters (parameter clusters) most sensitive to the model output variability, but it required significantly more r than usually applied in studies featuring the Morris method. The reason is that marginal differences in absolute values of elementary effects (the sensitivity indices of the Morris method) for some input parameters may lead to a change in ranking position several times as the number of r increases. Users of the Morris method must therefore not be predetermined on the size of the parameter cluster; instead, they must make a visual assessment of the convergence of the parameter ranking to qualitatively determine the appropriate size of parameter cluster. The final recommendation for future studies deploying the Morris method for SA applied to a high fidelity BEM is to choose p ≥ 4 as it seems to lead the analysis towards a more truthful ranking, and then run simulations in steps of r = 100 when making the visual assessment to determine convergence and the size of parameter cluster. The identified need for more r questions the general notion that the Morris method is a computationally efficient screening method in terms of absolute time use. However, the Morris method is still much more computational efficient than a Sobol'-based analysis if the purpose of the SA is to identify a cluster of input parameters most sensitive to the model output variability. [ABSTRACT FROM AUTHOR]

The four Higher Education Funding Councils in the United Kingdom want all universities to reduce CO 2 emissions by 34% by 2020 compared to a 2005 base. Universities that have installed Combined Heat and Power (CHP) technology are making good moves towards achieving their CO 2 reduction target. For a CHP project to be successful, a detailed technical, economic and environmental assessment is required. Generally, this assessment is carried out using a computer-based model. Currently, available CHP models have limitations in terms of flexibility, accuracy, reliability and complexity and their use could result in an undersized or oversized CHP scheme that could lead to a complete failure of the project. Therefore, there is an urgent need for a robust and user-friendly model, which integrates multiple features that are missing in the currently available models. This paper presents the development of a spreadsheet based CHP sizing model for a single or multiple university buildings. The major strengths of the model are its simplicity, flexibility of data entry, selection of multiple electrical and thermal demands, an in-built real database for a range of CHP sizes, multiple control strategies, multiple investment routes and their life cycle cash flow analysis, and the potential for detailed sensitivity analysis of payback period using the Monto Carlo Simulation technique. The model, which we call the London South Bank University (LSBU) CHP model, has been tested with three other CHP models for different control modes for the same building and the comparisons are discussed. [ABSTRACT FROM AUTHOR]

Modern district energy systems are highly complex with several controllable and uncontrollable variables. To effectively manage a multi-vector district requires a holistic perspective in terms of both modelling and optimisation. Current district optimisation strategies found in the literature often consider very simple models for energy generation and conversion technologies. To improve upon the state of the art, more realistic and accurate models must be produced whilst remaining computationally and mathematically simple enough to calculate within short periods. Therefore, this paper provides a comprehensive review of modelling techniques for common district energy conversion technologies including Power-to-Gas. In addition, dynamic building modelling techniques are reviewed, as buildings must be considered active and flexible participants in a district energy system. In both cases, a specific focus is placed on artificial intelligence-based models suitable for implementation in the real-time operational optimisation of multi-vector systems. Future research directions identified from this review include the need to integrate simplified models of energy conversion units, energy distribution networks, dynamic building models and energy storage into a holistic district optimisation framework. Finally, a future district energy management solution is proposed. It leverages semantic modelling to allow interoperability of heterogeneous data sources to provide added value inferencing from contextually enriched information. [ABSTRACT FROM AUTHOR]

This paper gives practical information related to the use of simplified EnergyPlus models and its impact on estimates of the cooling demand, especially when EnergyPlus models are coupled with an urban canopy model. By simplified models, it is referred to shoebox as well as multi-floor models. For the interest of climatologists, their influence on estimates of urban temperature and specific humidity is also studied. To explore divergences between the use of detailed and simplified EnergyPlus models, a typical office area in Singapore is selected as a case study. As a result, it is observed that the use of multi-floor models provides estimates of the cooling demand and urban microclimatic conditions which achieve the best agreement with these assessed from detailed models. On the other hand, less time is required to run a shoebox than a multi-floor model. Although these outcomes are inferred from an excellent case study, further research must be conducted on several types of urban areas to elaborate a general theory. [ABSTRACT FROM AUTHOR]

The industrial sector accounts for 17% of end-use energy in the UK, and 54% globally. Therefore, there is substantial scope for simulating and assessing potential energy retrofit options for industrial buildings. Building Energy Modelling (BEM) applied to industrial buildings poses a complex but important opportunity for reducing global energy demand, due to years of renovation and expansion. Large and complex industrial buildings make modelling existing geometry for BEM difficult and time consuming. This paper presents a potential solution for quickly capturing and processing as-built geometry of a factory to be utilized in BEM. Laser scans were captured from the interior of an industrial facility to produce a Point Cloud. The existing capabilities of a Point Cloud processing software were assessed to identify the potential development opportunities to automate the conversion of Point Clouds to building geometry for BEM applications. In conclusion, scope exists for increasing the speed of 3D geometry creation of an existing industrial building for application in BEM and subsequent thermal simulation. [ABSTRACT FROM AUTHOR]

In this paper the authors present an algorithm that abstracts an arbitrarily shaped set of building volumes into a group of simplified ‘shoebox’ building energy models. It is shown that for generic perimeter and core floorplans the algorithm provides a faster but comparably accurate simulation results of annual load profiles vis-à-vis multi-zone thermal models generated according to ASHRAE90.1 Appendix G guidelines. Envisioned applications range from rapid thermal model generation for urban building energy modelling to schematic architectural design. Following a description of the algorithm, its ability to produce load profiles for a mixed-use neighborhood of 121 fully conditioned buildings for a variety of climates is demonstrated. The comparison yields relative mean square errors in simulated annual building energy use intensity of five to 10 percent compared to ASHRAE 90.1 compliant building energy models while reducing simulation times by a factor of 296. [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]

Current literature holds various examples of investigations that make use of a building energy model (BEM) combined with a sensitivity analysis (SA) technique to identify and rank the BEM input parameters that the model output is most sensitive to. However, a sound argumentation that vouches for the reliability, validity and necessary complexity of the chosen SA method for the specific purpose of the BEM-based analysis, is rare. This paper reports on an investigation of how two different levels of a-priori information about input parameters, applied to three different SA methods (Local, Morris and Sobol), influenced the identification and ranking of the input parameters that the annual energy need output of a quasi-steady-state BEM using monthly time steps, and a simple dynamic BEM using hourly time steps, is most sensitive to. It was found that the three SA methods, to a great extent, were able to identify the same cluster of most sensitive input parameters, independent of the level of a-priori input parameter information and BEM. However, the ranking of most sensitive input parameters varied with the applied SA method, BEM, and level of a-priori input parameter information. From a practical point of view, the choice of appropriate SA method is concluded to depend on the purpose of the SA analysis. [ABSTRACT FROM AUTHOR]

• Grouping techniques are used to select representative buildings for stock modelling. • Three grouping approaches are applied to a case study from Germany. • Partition comparison relies on validation indices and impact on demand modelling. • Grouping techniques & selection indices impact the representative buildings chosen. Grouping techniques are frequently used in urban studies as a means for partitioning building stock heterogeneity. The choice of grouping techniques and selection indices can have a significant influence on the segmentation of building stock and associated identification of representative buildings. To date, this issue has not been systematically examined nor justified. The current paper compares different grouping and partition selection techniques using residential building data from Germany. Three grouping techniques are investigated: supervised, unsupervised and semi-supervised. The unsupervised approach is addressed through three clustering algorithms in this work: agnes, diana and partition around medoids. The semi-supervised approach consists of typology-based seed buildings coupled with a distance-based grouping, while the supervised approach relies on classification rules. The resulting partitions are assessed through multiple criteria: internal indices (CH, Dunn2, Silhouette), external index (F-measure) and impact on heating demand modelling (Heating Demand Error). Results show that the algorithms and the selection indices impact the choice of representative buildings to be modelled. Moreover, considering the F-measure, similarities between the three techniques results were observed on some of the groups. Parameters to account for when selecting a grouping technique are discussed and include the number of groups, group uniformity, and compactness/separation. [ABSTRACT FROM AUTHOR]

• Calibrating energy simulation models is crucial when assessing existing buildings. • Sensitivity analysis is key to reduce computational time in the calibration process. • Uncertainty techniques may be applied to assess energy models' accuracy. • Test Cells allow the performance of building simulation tools to be estimated. Improving the energy efficiency of existing buildings is a priority for meeting energy consumption and CO 2 emission targets in buildings. Building simulation tools play a crucial role in evaluating the performance of energy retrofit options. In this paper, a Bayesian calibration approach is applied to reduce the discrepancies between measured and simulated temperature data. Through its application to a test cell case study, the incorporation of sensitivity analysis and Bayesian calibration techniques are proven to improve the level of agreement between on-site measurements and simulated outputs, whilst accounting for both experimental and simulation uncertainties. The accuracy of a building simulation model developed using EnergyPlus was evaluated before and after calibration. Uncalibrated models were within the uncertainty ranges specified by the ASHARE Guidelines, with hourly simulation data over-predicting measurements by 3.2 °C on average. After Bayesian calibration, the average maximum temperature difference was reduced to around 0.68 °C, an improvement of almost 80%. [ABSTRACT FROM AUTHOR]

• Methods for monitoring buildings, testing and adjusting energy simulation software are developed. • Factors influencing energy building performance are investigated in two case studies. • Mixed-method approach helps to define building simulation templates and users' patterns. • Adjusted simulation models allow a reliable building energy consumption evaluation. • Weather data and occupancy patterns are the most influential factors in the adjustment of models. This paper focuses on exploring methods for reducing the gap between the expected and actual building energy performance by using simulation tools. The study has two purposes. The first is to quantify the relative effect of the different building parameters measured on the energy heating and cooling consumption compared with standard parameters through the adjustment of simulation models. The second is to develop an approach, based on three methods, for monitoring residential buildings, while also testing and calibrating methodologies for the simulation software. The approach developed is applied and tested in two real case studies (two apartments in two identically constructed buildings, one refurbished and the other not) in the city of Madrid, Spain. The analysis of the case studies shows that there is a four-fold difference in potential savings in energy for heating between models adjusted with standard and actual parameters. Moreover, the results reveal the significant impact of the use of actual weather data and users' behaviour in the adjustment of simulation models and demonstrate the utility of the application of these methods. [ABSTRACT FROM AUTHOR]