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A strong correlation exists between occupant behaviour and space heating energy use. In particular, the occupancy status (e.g., daytime absence) is known to have a significant influence on residential heating load profiles, as well as on cumulative heating energy consumption. In the literature, many occupancy models have been utilised to predict occupancy profiles of individual dwellings as part of the larger residential building stock. However, none of the existing models consider diversity associated with occupancy-integrated archetypes to generate high-temporal resolution heating load profiles. The current paper uses Time Use Survey (TUS) data to develop a high-temporal resolution residential building occupancy model. The key feature of the proposed model, implemented using MATLAB, is the ability to generate stochastic occupancy time-series data for national population subgroups characterised by specific occupancy profiles. It is shown that the results are capable of closely approximating data available from TUS. The developed model can be applied to improve the quality of modelled high-temporal resolution heating load profiles for generic building stock characterised by population subgroups represented by different occupancy-integrated archetypes. A case study is performed on a building stock sample located in London, UK. The developed occupancy model has been implemented in MATLAB and is available for download. [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]

• A methodology to integrate retrofit optimisation and power systems optimisation is introduced. • Retrofitted building energy models (a.k.a. Ensemble models) are obtained using Ensemble Calibration. • Ensemble models are rewritten as a bi-linear model. • The bi-linear model is used to formulate a linear heuristic optimisation algorithm. • The linear heuristic optimisation algorithm provides computational advantages with respect to a brute force approach while converging towards the optimal solution. The dynamic integration of building energy models and power system models is essential to analyse the potential supply-side benefits of adopting Energy Conservation Measures (ECM) at the national, regional scale. The integration of these models requires the development of linear building energy models which are numerically compatible with linear power systems models. Ensemble Calibration is an automated model calibration methodology which identifies a cluster of lumped parameter building energy models (denoted Ensemble models) using an archetype building energy model. Each lumped parameter building energy model represents an ECM configuration (i.e., a combination of ECMs). The current paper introduces a novel mechanism by which Ensemble models are integrated with power systems models in a manner such that cost-optimal retrofit decision-making problems and power systems optimisation problems can be simultaneously solved. To achieve this objective, Ensemble models are reformulated as bi-linear models, which are then co-optimised with power systems models using a multi-stage optimisation algorithm. The methodology is tested using two building energy archetypes: a detached house archetype and a mid-floor apartment archetype. The archetypes are representative of the Irish residential stock built prior to 1970. The results show that the proposed multi-stage optimisation algorithm provides computational advantages to the solution of the equivalent problem using a brute force approach (i.e., solving building-to-grid models for each ECM configuration). The proposed method is 4.73 times faster than the brute force approach when the archetypes are decoupled and 73 times faster when the models are deemed to be coupled via a power systems model. [ABSTRACT FROM AUTHOR]

Building stock modelling usually deploys representative building archetypes to obtain reliable results of annual energy heating demand and to minimise the associated computational cost. Available methodologies define archetypes considering only the physical characteristics of buildings. Uniform occupancy schedules, which correspond to national averages, are generally used in archetype energy simulations, despite evidence of occupancy schedules which can vary considerably for each building. This paper presents a new methodology to define occupancy-integrated archetypes. The novel feature of these archetype models is the integration of different occupancy schedules within the archetype itself. This allows building stock energy simulations of national population subgroups characterised by specific occupancy profiles to be undertaken. The importance of including occupant-related data in residential archetypes, which is different than the national average, is demonstrated by applying the methodology to the UK national building stock. The resultant occupancy-integrated archetypes are then modelled to obtain the annual final heating energy demand. It is shown that the relative difference between the heating demand of occupancy-integrated archetypes and uniform occupancy archetypes can be up to 30%. [ABSTRACT FROM AUTHOR]

• A novel method based on clustering, building energy models and Bayesian techniques is presented. • The approach enables the calibration of model parameters for large groups of buildings at an urban scale. • Various data-driven techniques have been tested acting as proxy of the simulation engine. • Three different surrogate structures have been investigated with comparison of the results. • The calibrated models provide realistic results compared to current literature studies. Prediction of building energy use, when performed at urban scale, is influenced by the choice of modelling approach, as well as the quality of available data. In the case of data scarcity, one of the main limitations of current urban scale building energy simulation models, is the use of deterministic approaches to specify modelling inputs for entire classes of buildings. This modelling approach is characterised by three major shortcomings: first, data uncertainties are not comprehensively considered; second, a rigorous method of identification of groups of buildings to be populated by similar modelling parameters is not specified; and third, strategies to calibrate the developed energy models are missing. Considering these challenges, the current paper presents a non-deterministic calibration method for groups of buildings. The methodology utilises four techniques: (i) the use of clustering to identify building groups and associated representative buildings within the urban context; (ii) the application of an automatic building energy modelling approach to simulate buildings within these groups; (iii) the application of data-driven models, used as emulators of the dynamic simulation engine, in a computationally efficient manner; and (iv) the exploitation of a non-deterministic Bayesian calibration framework to identify sets of representative parameters for the building clusters. Datasets, based on 2646 buildings from the city of Geneva, Switzerland, are used as a case study. Validation with measured data, shows that energy consumption and energy intensity predictions when considered at an aggregated scale, are within +/– 5%. On an individual building basis, the validation error is within +/– 20% for approximately 70% of the buildings. [ABSTRACT FROM AUTHOR]

The aim of the present paper is to investigate the influence of electricity tariffs on energy flexibility in buildings and associated energy costs. A residential building located in Stuttgart, Germany, equipped with a hybrid heat pump which is coupled with a thermal energy storage unit and a gas boiler is used as a case study. A model predictive control algorithm is used to minimise the daily operational cost over a full heating season. Several demand response programs based on controlling the heat pump power consumption were tested and analysed by adopting different metrics capable of describing the flexibility potential and cost of demand response programs. Several tariff structures, including: real-time pricing, two-level day-night tariffs and critical-peak pricing with both fixed and variable feed-in price components, were investigated. The results show that the building can provide up to 1370 kWh e of energy flexibility over the heating season with an average specific (marginal) costs of between €0.024–0.035 per kWh e of flexibility provided. The demand response programs lead to higher utilisation of thermal energy storage along with increased boiler consumption, by up to 17.1% and 12.1%, respectively in case of maximum demand response intensity. This in turn leads to a higher overall primary energy consumption of between 1.6% and 9.1% depending on demand response intensity. Typically, real-time pricing is the most favourable tariff structure, capable of offering the greatest energy flexibility with lowest associated electricity costs. [ABSTRACT FROM AUTHOR]

• Current indicators used to quantify building energy flexibility reviewed. • Comparison through four case studies with different demand response contexts. • Certain indicators more suitable for end-use, others more suitable for grid side. • Set of indicators capturing quantity, efficiency and rebound found to be robust. Buildings are increasingly being seen as a potential source of energy flexibility to the smart grid as a form of demand side management. Indicators are required to quantify the energy flexibility available from buildings, enabling a basis for a contractual framework between the relevant stakeholders such as end users, aggregators and grid operators. In the literature, there is a lack of consensus and standardisation in terms of approaches and indicators for quantifying energy flexibility. In the present paper, current approaches are reviewed and the most recent and relevant market independent indicators are compared through analysis of four different case studies comprising varying building types, climates and control schemes to assess their robustness and applicability. Of the indicators compared, certain indicators are found to be more suitable for use by the end user when considering energy and carbon dioxide emission reductions. Other indicators are more useful for the grid operator. The recommended indicators are found to be robust to different demand response contexts, such as type of energy flexibility, control scheme, climate and building types. They capture the provided flexibility quantity, its shifting efficiency and rebound effect. A final cost index is also recommended given specific market conditions to capture the cost of a building providing energy flexibility. [ABSTRACT FROM AUTHOR]