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Developing BEPS models which predict energy usage to a high degree of accuracy can be extremely time consuming. As a result, assumptions are often made regarding the input data required. Making these assumptions without introducing a significant amount of uncertainty to the model can be difficult, and requires experience. Even so, rules of thumb from one geographic region are not automatically transferrable to other regions. This paper develops a methodology which can be used to determine useful guidelines for defining the most influential input data for an accurate BEPS model. Differential sensitivity analysis is carried out on parametric data gathered from five archetype dwelling models. The sensitivity analysis results are used in order to form a guideline minimum set of accurately defined input data. Although the guidelines formed apply specifically to Irish residential dwellings, the methodology and processes used in defining the guidelines is highly repeatable. The guideline minimum data set was applied to practical examples in order to be validated. Existing buildings were modelled, and only the parameters within the minimum data set are accurately defined. All building models predict annual energy usage to within 10% of actual measured data, with seasonal energy profiles well-matching. [ABSTRACT FROM AUTHOR]

Simulation-based building retrofit analysis tools and electricity grid expansion planning tools are not readily compatible. Their integration is required for the combined study of building retrofit measures and electrified heating technologies using low-carbon electricity generation. The direct coupling of these modelling frameworks requires the explicit mathematical representation of Energy Conservation Measures (ECMs) in building-to-grid energy system models. The current paper introduces an automated calibration methodology which describes retrofitted buildings as parametric functions of ECMs. The buildings are represented using a lumped parameter modelling framework. A baseline model, representative of the building prior to retrofit, and the retrofit functions are calibrated using Particle Swarm Optimization. Synthetic temperature and heating load time-series data were generated using an EnergyPlus semi-detached house archetype model. The model is representative of this residential building category in Ireland. It is shown that the proposed methodology calibrates retrofitted building models to an acceptable level of accuracy ( MAE below 0.5 °C). The methodologies introduced in the current paper are capable of generating lumped parameter building models with similar dynamics for different ECMs for any archetype building energy model. The identified building retrofit models have the potential to be integrated with electricity grid models in a computationally-efficient manner. [ABSTRACT FROM AUTHOR]

For certain temperate climate types, cooling towers have the potential to produce high temperature chilled water, which when used in conjunction with radiant and displacement cooling technologies, have prompted interest in this concept as a potential approach for the conditioning of buildings. The feasibility of the proposed system depends largely, on achieving low approach water temperatures within an appropriately designed cooling tower, at acceptable levels of energy performance. Using a validated mathematical model, the current paper examines the performance of such a cooling tower for four different temperate climate conditions. The current assessment shows for building water return temperature of 22 °C, for temperate climates that are considered warm and humid, the limitations on cooling tower operation are when ambient conditions exceed 25 °C and 75% RH. Two strategies were investigated to allow the smallest practicable approach to the wet bulb temperature to be achieved: (i) increasing the tower air flowrate, (ii) decreasing the tower water flowrate. For a temperate climate that is warm and humid, operating the cooling tower with an approach of 1.0 K was found to be not practicable for all encountered weather conditions, but use of a 2.0 K approach was found to be practicable for all conditions. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes an investigation of the carbon footprint associated with plastic trays, used as packaging for foodstuffs (e.g., mushrooms). In recent years there has been an increase in both consumer and legislative pressure on the packaging sector to reduce the environmental impact of its products, which are often only single use items. Using data from a plastics manufacturer, a cradle-to-grave study was conducted for trays produced from recycled polyethylene terephthalate, calculating their product carbon footprint and analysing how various parameters affect the carbon footprint. A model based on a spreadsheet analysis was developed, which allows the product carbon footprint to be determined using production batch data. It was found that the cradle-to-grave carbon footprint of 1 kg of recycled polyethylene terephthalate trays containing 85% recycled content was 1.538 kg CO2e. The raw material, manufacturing, secondary packaging, transport and end-of-life stages each contributed 45%, 38%, 5%, 3% and 9% of the total life cycle greenhouse gases respectively. The recycled content of raw material was found to have a significant effect on product carbon footprint: a 24% decrease in tray carbon footprint could be obtained by manufacturing trays from 100% recycled content, compared to the current recycled content level of 85%. A reduction in tray weight was found to give almost an equivalent proportionate reduction in carbon footprint, with 20% and 30% tray weight reductions resulting in product carbon footprint reductions of 18.7% and 28% respectively. Transport was found to only contribute a minor amount of the greenhouse gases (3%) and hence improving transport efficiency had very little effect on the carbon footprint. The effect of end-of-life treatment was also found to be relatively small. The worst case scenario of no recycling taking place in the end-of-life stage results in the carbon footprint of the trays increasing by 2.7%, while increasing the recycling rate from 23.7% to 32% and 50%, results in the carbon footprint decreasing by 1% and 3% respectively. In both the extrusion and thermoforming processes, the specific manufacturing carbon footprints arising from consumption of electricity, chilled water energy and compressed air were found to decrease logarithmically with production speed. The greatest reductions in the carbon footprint of recycled polyethylene terephthalate trays can be achieved in the raw material and manufacturing life cycle stages. The proportion of recycled raw material should be maximised while extrusion and thermoforming process speeds should be optimised as significant manufacturing energy reductions can be attained when the speeds of both processes are increased. Tray light-weighting should be implemented to as great an extent as possible without compromising tray structural integrity while high recycling rates in the end-of-life stage should continue to be targeted. [Copyright &y& Elsevier]

Abstract: Design guidelines for natural ventilation (NV) in buildings focus on the potential hourly air change (ACH) rates based on the building space parameters. Critically, external airflow data is often assumed on the basis of a single mean wind speed and an associated prevailing wind direction. This can result in significant variation in ventilation rates and comfort conditions when non-design external wind conditions prevail. This paper describes a CFD study aimed at examining the influence of variations in external wind speed and direction on the air change rate for the atrium space of a two-storey naturally ventilated building. The building atrium is ventilated by a series of entry vents on one wall of the building in conjunction with roof vents. External wind speeds from 25 to 250% of the mean site wind speed (5.7m/s) were examined and found to result in an almost linear increase in the ACH rate. For a single wind speed, the relationship between wind direction and the ACH rate was also found to be approximately linear for wind directions between 0° and 90° (orthogonal and parallel) to the wall vent openings, but non-linear for other wind directions (90–135°). More generally, the significant variation in the atrium ACH rate with changes in external wind conditions, evident in this particular building model, illustrates the importance of considering non-design wind conditions when designing NV buildings. [Copyright &y& Elsevier]

Abstract: This paper describes numerical investigations, using computational fluid dynamics, conducted to examine the heat transfer mechanisms by which air-chiller U-bends cause enhanced downstream internal convection, where single phase secondary refrigerants under laminar conditions are employed as the heat exchanger fluid. The numerical model, created using FLUENT, consists of a single heat exchanger tube pass incorporating an inlet pipe, a U-bend and an outlet pipe. The model was validated using experimental data from the literature. Numerical investigations indicate that within the U-bend, secondary flows partially invert temperature profiles resulting in a significant localised decrease in average fluid temperature at the pipe surface. As a result, downstream heat transfer enhancement is observed, the magnitude of which can exceed that typical of a pipe combined entry condition in some circumstances by greater than 20% for up to 20 pipe diameters downstream. Heat transfer enhancement was found to increase with increasing U-bend radius, but to decrease with increasing heat exchanger pipe radius and internal Reynolds number. A simple technique based on quantification of the degree of temperature inversion at the U-bend is proposed which provides a mechanism by which heat transfer enhancement can be estimated. [Copyright &y& Elsevier]

Abstract: An examination of the role of design and operational parameters in a night ventilated library building located in Ireland, and which is subject to a maritime type climate, is considered. For this investigation, a self-contained space within the library building is analysed. A dynamic model of the space is created using the ESPr simulation package, which is compared with building data obtained from an experimental monitoring programme. Calculation of the mean bias deviation between the predicted and experimental data indicates agreement better than 0.45°C for the dry bulb temperature and 1.1°C for the mean radiant temperature. Using ESPr predictions, the role of different building design and operational parameters are examined by means of parametric analysis including building mass, ventilation duration, internal gain and ventilation rates. Compared to a reference base of the existing building, increasing the building mass was found to achieve a significant reduction in internal dry resultant temperatures, with a decrease of between 2 and 3°C observed when mass was increased from 800kg/m2 to 1600kg/m2. Reducing building internal gains from 40W/m2 to 20W/m2 was observed to have the potential of further reducing dry resultant temperatures by up to 1°C. Examination of night ventilation rates indicated that increasing night ventilation up to 10 ACH was observed to have a significant affect on reduction of dry resultant temperature, but further increases of air changes were observed to have a negligible effect. [Copyright &y& Elsevier]

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]

Forecasting of commercial building thermal loads can be achieved using data from Building Energy Management (BEM) systems. Experience in building load prediction using historical data has shown that data analysis is a key factor in order to produce accurate results. This paper examines the selection of appropriate input variables, for data-driven predictive models, from wider datasets obtained from BEM systems sensors, as well as from weather data. To address the lack of available complete datasets from actual commercial buildings BEM systems, detailed representation of reference buildings using EnergyPlus were implemented. Different types of commercial buildings in various climates are examined to investigate the existence of patterns in the selection of input variables. Data analysis of the simulated results is used to detect the correlation between thermal loads and possible input variables. The selection process is validated by comparing the performance of predictive models when the full or the pre-selected set of variables is introduced as inputs. [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]

This paper investigates the effect of different control strategies applied to electric thermal storage systems to provide demand response services. These results indicate how policymakers or manufacturers could target the implementation of advanced control on electric thermal storage systems and apply these to households characterised by different occupancy profiles, thereby making demand response initiatives more attractive to end users. [ABSTRACT FROM AUTHOR]

[Display omitted] • Harnessing energy flexibility is a complex socio-economic-technical challenge. • Research opportunities are identified in five areas and three development stages. • Most tools/methods are limited to the operational stage and the building level. • Outcomes from pilots are not sufficiently disseminated nor used in future research. • A systemic approach is required for effective energy flexibility exploitation. This paper examines building energy flexibility at an aggregated level and addresses the main barriers and research gaps for the development of this resource across three design and development phases: market and policy, early planning and design, and operation. We review methodologies and tools and discuss barriers, challenges, and opportunities, incorporating policy, economic, technical, professional, and social perspectives. Although various legal and regulatory frameworks exist to foster the development of energy flexibility for small buildings, financing mechanisms are limited with a significant number of perceived risks undermining private investment. For the early planning and design phase, planners and designers lack appropriate tools and face interoperability challenges, which often results in insufficient consideration of demand response programs. The review of the operational phase highlighted the socio-technical challenges related to both the complexity of deployment and communication, as well as privacy and acceptability issues. Finally, the paper proposes a number of targeted research directions to address challenges and promote greater energy flexibility deployments, including capturing building demand side dynamics, improving baseline estimations and developing seamless connectivity between buildings and districts. [ABSTRACT FROM AUTHOR]

Data are essential to urban building energy models and yet, obtaining sufficient and accurate building data at a large-scale is challenging. Previous studies have highlighted that the data impact on urban case studies has not been sufficiently discussed. This paper addresses this gap by providing an analysis of the impact of input data on building energy modelling at an urban scale. The paper proposes a joint review of data impact and data accessibility to identify areas where future survey efforts should be concentrated. Moreover, a Morris sensitivity analysis is carried out on a large-scale residential case study, to rank input parameters by impact on space heating demand. This paper shows that accessible data impact the whole modelling process, from approach selection to model replicability. The sensitivity analysis shows that the setpoint and thermal characteristics were the most impactful for the case study considered. Solutions proposed to overcome availability and accessibility issues include organising annual workshops between data users and data owners, or developing online databases that could be populated on a volunteer-basis by data owners. Overall, overcoming data challenges is essential for the transition towards smarter cities, and will require an improved communication between all city stakeholders. [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]

This paper addresses the challenge of assessing uncertainty in energy flexibility predictions, which is a significant open question in the energy flexibility assessment field. To address this challenge, a methodology that quantifies the flexibility of multiple thermal and electrical systems is developed using appropriate indicators and considers the different types of uncertainty associated with building energy use. A Bayesian convolutional neural network is developed to capture aleatoric and epistemic uncertainty related to energy conversion device operation and temperature deviations resulting from exploiting building flexibility. The developed prediction models utilise residential occupancy patterns and a sliding window technique and are periodically updated. The energy systems evaluated include a heat pump, a photovoltaic system, and a stationary battery, and use synthetic datasets obtained from a calibrated physics-based model of an all-electric residential building for two occupancy profiles. Simulation results indicate that building flexibility potential predictability is influenced by weather conditions and/or occupant behaviour. Furthermore, the day-ahead and hour-ahead prediction models show excellent performance for both occupancy profiles, achieving coefficients of determination between 0.93 and 0.99. This methodology can enable electricity aggregators to evaluate building portfolios, considering uncertainty and multi-step predictions, to shift electricity demand to off-peak periods or periods of excess onsite renewable electricity generation in an end-user-customised manner. • A data-driven probabilistic energy flexibility assessment framework to evaluate the operational energy flexibility of residential buildings. • A Bayesian deep learning framework based on short-term predictions and sliding windows. • A series of novel indicators to quantify building flexibility potential by considering the associated uncertainties. • Flexibility maps are used to estimate building flexibility within prediction intervals and depict it concisely and consistently. • Occupant behaviour and weather affect the predictability of building flexibility and the associated uncertainty. [ABSTRACT FROM AUTHOR]

Air to water electric heat pumps are one technological solution to achieve energy defossilisation goals for heating of residential building stock. Nevertheless, they may not necessarily be the only solution for all residential building stock. A case in point is where extensive fabric refurbishment is impracticable or where electric heat pumps are installed where low ambient temperatures prevail and/or high water delivery temperatures must be utilised. For such instances, hybrid (gas and electric) heat pumps offer an alternative option by facilitating fuel source switching between electricity and gas, when ambient temperatures are low or high water supply temperatures are required. In the current study, the effectiveness of an air-to-water electric heat pump and hybrid heat pump are examined for different building retrofit scenarios for a residential dwelling located in Ireland. This is achieved by means of a sensitivity study of a validated building simulation model, incorporating both heat pump systems, subject to different building retrofit scenarios. Relative to a conventional oil-fired boiler, for a deep building retrofit scenario, the hybrid and electric heat pumps achieve primary energy reduction of 128 kWh/ m 2 /year (72%) and of 123 kWh/ m 2 /year (70%), respectively. Considering the associated carbon footprints, the reductions were found to be 29.7 g C O 2 e / m 2 /year (74%) for the hybrid heat pump, and 27.6 g C O 2 e / m 2 /year (68%) for the electric heat pump. Finally, the deployment of either an electric heat pump or hybrid heat pump for deep building fabric retrofit achieves approximately half of the heating system capital cost return within 20 years. • Analysis of a hybrid gas-electric heat pump system in a residential building. • Benchmarked against, oil boiler, gas boiler and electric heat pump. • Integrated validated simulation model utilised for retrofit options analysis. • Electric and hybrid heat pumps with deep retrofit are the most economically attractive options. • Electric heat pump and hybrid heat pump systems with deep retrofit exhibit the lowest C O 2 emissions. [ABSTRACT FROM AUTHOR]

• Studies on data-driven methods for building energy flexibility quantification have been reviewed. • Existing data-driven energy flexibility KPIs have been systematically categorized. • Various aspects of the KPIs, including application, stakeholder, complexity, data requirements, and popularity, have been analyzed. • Public datasets for energy flexibility studies have been reviewed and summarized in a standardized data collection process. • Research trends, open questions, and future opportunities are identified. Energy flexibility, through short-term demand-side management (DSM) and energy storage technologies, is now seen as a major key to balancing the fluctuating supply in different energy grids with the energy demand of buildings. This is especially important when considering the intermittent nature of ever-growing renewable energy production, as well as the increasing dynamics of electricity demand in buildings. This paper provides a holistic review of (1) data-driven energy flexibility key performance indicators (KPIs) for buildings in the operational phase and (2) open datasets that can be used for testing energy flexibility KPIs. The review identifies a total of 48 data-driven energy flexibility KPIs from 87 recent and relevant publications. These KPIs were categorized and analyzed according to their type, complexity, scope, key stakeholders, data requirement, baseline requirement, resolution, and popularity. Moreover, 330 building datasets were collected and evaluated. Of those, 16 were deemed adequate to feature building performing demand response or building-to-grid (B2G) services. The DSM strategy, building scope, grid type, control strategy, needed data features, and usability of these selected 16 datasets were analyzed. This review reveals future opportunities to address limitations in the existing literature: (1) developing new data-driven methodologies to specifically evaluate different energy flexibility strategies and B2G services of existing buildings; (2) developing baseline-free KPIs that could be calculated from easily accessible building sensors and meter data; (3) devoting non-engineering efforts to promote building energy flexibility, standardizing data-driven energy flexibility quantification and verification processes; and (4) curating and analyzing datasets with proper description for energy flexibility assessm. [ABSTRACT FROM AUTHOR]

Highlights • Co-simulation and building energy models provide a framework to test control algorithms. • Machine learning algorithms are effective to develop predictive optimisation models. • Predictive controls for residential can reduce heating systems costs up to 40%. • Smart predictive control in buildings results in lower carbon footprint up to 39%. • The smart grid dynamically controls dwellings to balance electricity supply/demand. Abstract This paper assesses the performance of control algorithms for the implementation of demand response strategies in the residential sector. A typical house, representing the most common building category in Ireland, was fully instrumented and utilised as a test-bed. A calibrated building simulation model was developed and used to assess the effectiveness of demand response strategies under different time-of-use electricity tariffs in conjunction with zone thermal control. Two demand response algorithms, one based on a rule-based approach, the other based on a predictive-based (machine learning) approach, were deployed for control of an integrated heat pump and thermal storage system. The two algorithms were evaluated using a common demand response price scheme. Compared to a baseline reference scenario, the following reductions were observed: electricity end-use expenditure (20.5% rule-based and 41.8% predictive algorithm), utility generation cost (18.8% rule-based and 39% predictive algorithm), carbon emissions (20.8% rule-based and 37.9% predictive algorithm). [ABSTRACT FROM AUTHOR]

Highlights • The system suitability performance is correlated with the climatic conditions. • Heating degree days are driving parameter to asses their profitability. • Cogeneration systems are resilient to increases in energy market prices. • The design is influenced by the investment policy adopted. Abstract The aim of the proposed work is to investigate the technical and economic suitability of a gas turbine combined heat and power system in commercial buildings. These systems are widely recognised as a promising technology to provide significant fuel savings and carbon emissions reduction where they have been widely used in industrial settings due to the relatively constant electrical and thermal loads required for industrial processes. However, their deployment has been relatively stagnant over the last few decades due to challenges such as poor planning and policy measures, energy market changes and regulatory barriers. In this context, a preliminary system design and optimisation procedure has been developed based on a sensitivity analysis of different scenarios of building loads, market and weather conditions. The optimisation is performed considering several technical and environmental parameters (e.g., energy and exergy efficiencies and primary energy saving), as well as economic indexes (e.g., net present value, pay-back period, profitability, etc.). This allows the suitability assessment of the investment for different market price scenarios under different heating degree days demand scenarios. The analysis is carried out using an Italian case study as it exhibits a wide range of heating degree days variability, while subject to a single pricing market. Results show that strong correlations occur between the technical and economic performance indices and the weather conditions for all considered configurations. The methodology and conclusions, if coupled with the possibility of applying clustering techniques to determine common patterns of energy consumptions in building blocks, represent a powerful toolset to carry out preliminary techno-economic assessment of a combined heat and power system. [ABSTRACT FROM AUTHOR]

This paper exploits a whole-building energy simulation approach to develop and evaluate demand response strategies for commercial buildings. The research is motivated by the increasing penetration of renewable energy sources such as wind and solar, which owing to their stochastic nature, means that enhanced integration of demand response measures in buildings is becoming more challenging and complex. Using EnergyPlus, a simulation model of a multi-purpose commercial building was developed and calibrated. Demand response strategies are evaluated for a number of building zones, which utilise different heating, cooling and ventilation equipment. The results show that for events of varying demand response durations, different strategies should be selected for each zone based on their thermal and usage profiles. Overall, a maximum reduction of 14.7% in electrical power demand was recorded when targeting a centralised chiller load, with smaller reductions for other decentralised building loads. [ABSTRACT FROM AUTHOR]

The paper is concerned with the development and evaluation of control algorithms for the implementation of demand response strategies in a smart-grid enabled all-electric residential building. The dwelling is equipped with a 12 kW heat pump, a 0.8 m 3 water storage tank, a 6 kW photovoltaic (PV) array, solar thermal collectors for domestic hot water heating and an electric vehicle. The building, located in Ireland, is fully instrumented. An EnergyPlus building simulation model of the dwelling was developed and calibrated using monitored data from the building. The developed model is used to assess the effectiveness of demand response strategies using different time-of-use electricity tariffs in conjunction with zone thermal control. A reduction in generation cost (−22.5%), electricity end-use expenditure (−4.9%) and carbon emission (−7.6%), were estimated when DR measures were implemented and compared with a baseline system. Furthermore, when the zone control features were enabled, the efficiency of the control improved significantly giving, an overall annual economic saving of 16.5% for the residential energy cost. The analysis also identified an annual reduction of consumer electricity consumption of up to 15.9%, lower carbon emissions of 27% and facilitated greater utilisation of electricity generated by grid-scale renewable resources, resulting in a reduction of generation costs for the utility of up to 45.3%. [ABSTRACT FROM AUTHOR]

Energy security assessment quantifies the energy supply to a population and the likelihood, or risk, of an energy disruption or shortage and represents an important aspect of national security, economic stability and prosperity. The quantification of the state of energy supply is context-dependent and involves multiple perspectives: infrastructural, technological, environmental, market, social and geopolitical. Among all the different and relevant aspects involved, diversity and dependence of the energy fuel mix are two of the main energy security dimensions. The present paper investigates the diversification of the energy supply in Europe, by analysing import dependence, market concentration and renewable energy resource deployment in the European Union over the last decade. The analysis utilises a set of indicators aimed at measuring the fuel mix diversity, market concentration, geopolitical stability, renewable energy share and stochasticity - both at single country and at aggregated European levels. Results show a stable evolution of the diversity of the fuel mix and a relatively low market concentration of the period examined. However, the import dependency reduces the energy security by approximately 30% due to the high proportion of imports from a limited number of countries. Moreover, an increasing trend in renewable electricity production share is evident over the last decade, albeit with differences between member states, as a result of the decarbonisation policies implemented by the European Union. • Europe shows a stable evolution of the fuel mix diversity at EU28 aggregated level. • European import dependency reduces the energy security by 30%. • The implementation of the RES policies across the EU is not homogenous. • Diversification of the fuel mix and support for domestic fuels need to be fostered. • Policies need to merge decarbonisation targets with energy security constraints. [ABSTRACT FROM AUTHOR]

The development of building energy performance simulation models often requires significant time and effort to achieve an acceptable degree of prediction accuracy. As such, energy modelers introduce various simplifications and assumptions that require a high degree of modeling literacy to avoid any errors in energy predictions. Previous studies relate these simplifications to the identification of influential building parameters using engineering judgment techniques that are often subjective and differ based on experts' opinion. The proposed methodology accurately defines influential and non-influential building parameters to formulate a guideline minimum dataset in the context of residential building energy models. The methodology integrates two feature selection techniques (Bayesian Information Criteria and Least Absolute Shrinkage with Selection Operator) with parametric analysis to determine the set of influential parameters. The study uses Irish residential archetypes to compare and validate the subsets of influential parameters using sensitivity rankings and established validation metrics. The predicted annual energy use lies within 10% of measured data for both subsets of influential parameters. Thereby, energy modelers could significantly reduce the time and effort spent on model development while maintaining the desired accuracy. The formulated datasets represent only influential features and hence, could be used by urban planners and energy policymakers to estimate energy retrofit investment costs, emission reductions and energy savings. • Systematic methodology that defines the datasets of most influential parameters. • Morris and Sobol techniques provide similar sensitivity rankings. • Local sensitivity analysis provides the top cluster of important parameters. • BIC approach is more beneficial due to greater penalization on model complexity. • LASSO models are less accurate compared to the BIC models (within ± 0.5%). [ABSTRACT FROM AUTHOR]

A key issue in energy flexibility assessment is the lack of a scalable practicable approach to quantify and characterise the flexibility of individual residential buildings from an integrated energy system perspective without the need to use complex simulation models. In this study, this problem is addressed by explicitly quantifying the flexibility of multicomponent thermal and electrical systems commonly found in residential buildings based on an ensemble learning framework that consists of four algorithms, namely, random forests, multilayer perceptron neural network, support vector machine, and extreme gradient boosting. The day-ahead and hour-ahead prediction models developed are periodically updated considering dynamic feature selection based on residential occupancy patterns. The proposed methodology utilises synthetic data obtained from a calibrated white-box model of an all-electric residential building for two indicative occupancy profiles. The energy systems evaluated include a heat pump, a photovoltaic system, and a battery unit. The daily flexibility mappings are acquired by applying hourly independent, and consecutive demand response actions for each energy system considered, using suitable energy flexibility indicators. The results show that the ensemble models developed for each target variable outperform each of the constituent machine learning algorithms. Moreover, the storage capacity resulting from harnessing the heat pump downward flexibility demonstrates accurate accuracy with a coefficient of determination equal to 0.979 and 0.968 for day-ahead predictions and 0.998 and 0.978 for day ahead predictions for the two occupancy profiles considered, respectively. This framework can be used by electricity aggregators to evaluate a building portfolio in an end-user-tailored manner or optimally exploit its energy flexibility considering multi-step predictions to shift electricity usage to off-peak times or times of excess onsite renewable energy generation. • Energy flexibility analysis for a dwelling from an integrated systems perspective. • A robust data-driven framework for evaluating building energy flexibility. • An ensemble learning framework based on short-term predictions and sliding windows. • Occupant behaviour and weather affect the predictability of building flexibility. • Shorter prediction horizons give better forecasts of energy flexibility mappings. [ABSTRACT FROM AUTHOR]

The increasing penetration of renewable energy sources has the potential to contribute towards the decarbonisation of the building energy sector. However, this transition brings its own challenges including that of energy integration and potential grid instability issues arising due the stochastic nature of variable renewable energy sources. One potential approach to address these issues is demand side management, which is increasingly seen as a promising solution to improve grid stability. This is achieved by exploiting demand flexibility and shifting peak demand towards periods of peak renewable energy generation. However, the energy flexibility of a single building needs to be coordinated with other buildings to be used in a flexibility market. In this context, multi-agent systems represent a promising tool for improving the energy management of buildings at the district and grid scale. The present research formulates the energy management of four buildings equipped with thermal energy storage and PV systems as a multi-agent problem. Two multi-agent reinforcement learning methods are explored: a centralised (coordinated) controller and a decentralised (cooperative) controller, which are benchmarked against a rule-based controller. The two controllers were tested for three different climates, outperforming the rule-based controller by 3% and 7% respectively for cost, and 10% and 14% respectively for peak demand. The study shows that the multi-agent cooperative approach may be more suitable for districts with heterogeneous objectives within the individual buildings. • Two DRL architectures for buildings energy management are compared to a RBC. • Daily and seasonal KPIs are used to assess controllers stability and adaptability. • DRL controllers optimise district load reducing peaks and increasing self-consumption. • Cooperative DRL architecture performs better with heterogeneous energy systems. [ABSTRACT FROM AUTHOR]

To date, the energy flexibility assessment of multicomponent electrical and thermal systems in residential buildings is hindered by the lack of adequate indicators due to the different interpretations, properties, and requirements that characterise an energy flexible building. This paper addresses this knowledge gap by presenting a fundamental energy flexibility quantification framework applicable to various energy systems commonly found in residential buildings (i.e., heat pumps, renewables, thermal and electrical storage systems). Using this framework, the interactions between these systems are analysed, as well as assessing the net energy cost of providing flexibility arising from demand response actions where onsite electricity production is present. A calibrated white-box model of a residential building developed using EnergyPlus (including inter alia a ground source heat pump, a battery storage system, and an electric vehicle) is utilised. To acquire daily energy flexibility mappings, hourly independent, and consecutive demand response actions are imposed for each energy system, using the proposed indicators. The obtained flexibility maps give insights into both the energy volumes associated with demand response actions and qualitative characteristics of the modulated electricity consumption curves. The flexibility potential of each studied energy system is determined by weather and occupant thermal comfort preferences as well as the use of appliances, lighting, etc. Finally, simulations show that zone and water tank thermostat modulations can be suitably combined to shift rebound occurrences away from peak demand periods. These insights can be used by electricity aggregators to evaluate a portfolio of buildings or optimally harness the flexibility of each energy system to shift peak demand consumption to off-peak periods or periods of excess onsite electricity generation. • Few studies evaluate energy flexibility from an integrated energy systems perspective. • A novel unified framework is proposed for capturing the DR potential of thermal and electrical systems. • A series of novel indicators are introduced to acquire daily energy flexibility mappings. • Flexibility maps are used to quantify energy flexibility concisely and consistently. • Weather, occupant schedules and use of appliances shape the flexibility potential of each system. [ABSTRACT FROM AUTHOR]

Managing supply and demand in the electricity grid is becoming more challenging due to the increasing penetration of variable renewable energy sources. As significant end-use consumers, and through better grid integration, buildings are expected to play an expanding role in the future smart grid. Predictive control allows buildings to better harness available energy flexibility from the building passive thermal mass. However, due to the heterogeneous nature of the building stock, developing computationally tractable control-oriented models, which adequately represent the complex and nonlinear thermal-dynamics of individual buildings, is proving to be a major hurdle. Data-driven predictive control, coupled with the "Internet of Things", holds the promise for a scalable and transferrable approach, with data-driven models replacing traditional physics-based models. This review examines recent work utilising data-driven predictive control for demand side management application with a special focus on the nexus of model development and control integration, which to date, previous reviews have not addressed. Further topics examined include the practical requirements for harnessing passive thermal mass and the issue of feature selection. Current research gaps are outlined and future research pathways are suggested to identify the most promising data-driven predictive control techniques for grid integration of buildings. • A review of 115 data-driven predictive control studies in building energy management. • Growing trend of research on data-driven predictive control in recent years. • High level of simplification in modelling seen, mostly applied to single buildings. • Gaps identified were feature selection, benchmarking, data quality and scalability. [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]

The ability to control and optimise energy consumption at end-user level is of increasing interest as a means to achieve a balance between supply and demand, particularly when large penetration of distributed renewable energy sources is being considered. Demand Response programs consist of a series of externally-driven control strategies aimed at adapting consumer end-use load to specific grid requirements. In a demand response scenario, a network of connected systems can be exploited to activate balancing strategies, to provide demand flexibility during periods of high stress for the grid. However, the widespread deployment of demand response programs in the building sector still faces significant challenges. Smart technology deployment, the lack of common standardised assessment procedures and metrics, the absence of established regulatory frameworks are among the main obstacles limiting the development of portfolios of competitive flexibility assets. The residential sector is even more affected by these challenges due to a marginal economic case, the issue of long term harmonisation of hardware and software infrastructure and the influence of the end-user behaviour and preferences on energy consumption. The present paper provides a review on the current developments of the Demand Response programs, with specific reference to the residential building sector. Methodologies and procedures for assessing building energy flexibility and Demand Response programs are described with a special focus on numerical models and available control algorithms. Moreover, markets schemes and social aspects - such as technology acceptance and awareness - and their influence on smart control technologies and algorithms are discussed. Current research gaps and challenges are identified and analysed to provide guidance for future research activities. • The last developments on DR programs for residential buildings are analysed. • A lack of common definitions and methodology to assess DR programs still occurs. • Developing smart control algorithms linked with building models is essential. • Appropriate market mechanisms and tariff structures are still needed. • Social and behavioural issues are critical for high levels of user satisfaction. [ABSTRACT FROM AUTHOR]

The present paper introduces an iterative methodology to progressively reduce building simulation model complexity with the aim of identifying potential trade-offs between computational requirements (i.e., model complexity) and energy estimation accuracy. Different levels of model complexity are analysed, from commercial building energy simulation tools to low order calibrated thermal networks models. Experimental data from a residential building in Germany were collected and used to validate two detailed white-box models and a simplified white-box model. The validation process was performed in terms of internal temperature profiles and building thermal energy demand predictions. Synthetic profiles were generated from the validated models and used for calibrating high order models. A reduction (trimming) procedure was applied to reduce the model complexity using an energy performance criterion prior to model trimming. The proposed methodology has the advantage of keeping the physical structure of the original RC model, thus enabling the use of the trimmed lumped parameter building model for other applications. The analysis showed that it is possible to reduce the model complexity by half, while keeping the accuracy above 90% for the targeted building. [ABSTRACT FROM AUTHOR]