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• A retrofit selection strategy employing economic and environmental decision criteria was proposed. • Building energy retrofits were evaluated through a life cycle thinking approach. • Retrofits with operational emissions savings may not be beneficial when considering life cycle impacts. • Energy mix and the heating system of the house greatly impact the effectiveness. • Retrofit selection changes with stakeholder priorities on environment and economics. Improving the energy performance of buildings has been a much-discussed topic over the past few decades. With the current focus on climate change mitigation, emissions reduction has also come to the forefront of this discussion. Retrofitting is an option to improve the energy and emissions performance of buildings. However, in the residential building stock, retrofit planning for existing buildings faces many complexities due to variations in climatic conditions and macro-environment as well as the presence of multiple stakeholder groups. Thus, in identifying solutions, it is necessary to take a holistic perspective that covers these different dimensions. In this paper, commonly used building energy retrofits were evaluated through a life cycle thinking approach. The performance of various retrofit options applicable for single family detached housing were evaluated using the HOT2000 energy simulation software package under the varying climatic conditions and energy supply scenarios across Canada. The retrofits were evaluated in terms of the additional investment, energy use and cost reduction achieved over the life cycle, and life cycle emissions reduction. The findings indicate that the provincial energy mix and the heating system of the house (i.e. electric or natural gas) play a major role in determining the effectiveness of a retrofit, and this "effectiveness" changes at different stakeholder levels. However, not all retrofits that reduce emissions make economic sense and vice versa when life cycle thinking comes into play. The findings will be useful for building owners and occupants as well as for policy developers and other decision makers interested in demand side management. [ABSTRACT FROM AUTHOR]

• A methodology was represented for calibrating the building energy performance model. • Accurate calibration was achieved on the model even with the short-term measurement. • The calibration process was utilized the MBE- CVRMSE and obtained an enhanced accuracy. • The HDD and CDD was applied in the absence of the real weather data. • The accurate model also provides the opportunity to trail the systems' performance. The calibration process has an excellent advantage, especially for large-scale projects that manage the potentially complex energy performances of various building systems. It also provides great advantages when the annual real-time measurements are not available since the created model can be calibrated with short-term measurements to obtain an accurate annual energy performance. This paper presents a methodology for creating an energy performance model based on a calibration process of a large-scale residential building incorporating heating and cooling energy consumptions. The main purpose is to calibrate the building energy model by utilizing mean bias error (MBE) and coefficient of variation of root-mean-squared error (CVRMSE) indices and obtain the annual building-energy performance with enhanced accuracy. Furthermore, two novel approaches were developed. The first is to provide accurate weather data in the absence of real weather data by taking advantage of available data for heating degree days (HDDs) and cooling degree days (CDDs). The second is to provide accuracy on the collected data of measurements by taking advantage of the predicted data of short term measurements. A methodology is evolved to calibrate the building model by using real-time short-term monitored data from the building. The methodology consists of four main steps and further sub-steps. Modeling, the first step, covers the collection of input data and the development of the model stages. Measurements, the second step, covers the process of data collection for calibration. Improvements for calibration represent the third step, where different versions of the model are developed by changing the input parameters of the model within the defined ranges of variation (ROV). Then, the results of the obtained versions are compared with the real-time measurements, and MBE and CVRMSE values for each version calculated to examine the model's accuracy. Finally, when the model is considered as calibrated, the simulation is run to obtain the annual energy performance of the building for better precision. The results in the model run here show the monthly MBE and CVRMSE values for the initial version to be 15.12% and 17.19%, respectively. After applied strategies, these values are improved according to the calibration criteria defined by ASHRAE to −2.76% and 8.40% respectively, which is considered as calibrated. Overall, it is usually hard and costly to track the real-time energy consumptions of buildings for a long period. Annual building energy consumptions can be obtained accurately by calibrating the building model with the short-term measurements. An accurate model gives the opportunity to trial the performance of each building system. [ABSTRACT FROM AUTHOR]

• The types and benefits of occupancy-based temperature controls for residential buildings are reviewed. • A systematic analysis approach is performed for a wide range of temperature controls specific to Saudi households. • Smart occupancy-controls can save on average 36% of annual energy consumption for a typical KSA household. The paper describes the potential energy efficiency and demand response benefits of occupancy-based temperature controls applicable to residential buildings for the Kingdom of Saudi Arabia (KSA). Specifically, various spatial and temporal temperature controls are evaluated to account for spatial and temporal occupancy patterns associated to typical living styles of Saudi households. The analysis results show that the benefits of occupancy-based temperature settings can be significant when at least two thermal zones controlled by different thermostats exist within the same housing unit. Specifically, occupancy-based thermostat settings can save up to 38.7% of total annual electricity consumption for a dwelling located in Riyadh. Moreover, when the indoor temperature settings are adjusted spatially, a reduction of up to 34.7% in peak demand can result for most KSA regions. Using a bottom-up analysis approach, it is found that when scaling up to the entire KSA housing stock, the adoption of smart thermostats has the potential to reduce annual electricity use by 48 TWh/year or over 30% of the national residential sector energy consumption. [ABSTRACT FROM AUTHOR]

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]

• A replicable methodology for identifying reference buildings is presented. • The k -means clustering method is applied to the energy performance certificate database. • To illustrate the methodology, an EPC database including 13,701 offices is used. • Sixteen clusters and corresponding representative offices are identified. • Representative offices can be used for cost-optimal retrofitting analysis of the entire stock. A large number of buildings must be evaluated to formulate energy retrofitting policies for existing building stock. In this context, it is crucial to identify reference buildings that can effectively represent the entire stock, since such buildings can then be used to assess the individualized cost-effectiveness of retrofitting measures. This paper presents a novel approach for identifying and defining a set of reference buildings by applying the k -means clustering method to energy performance certificate databases. To this end, a four-step methodology has been envisaged. First, an energy performance certificate database is prepared and variables that have an impact on energy consumption are pre-selected. Selected data are then pre-processed. Next, the k -means clustering method is applied. Finally, the resulting cluster centroids are used to identify the closest energy performance certificates in the database, in other words, the representative buildings that will then be used for cost-optimal retrofitting analysis. The methodology is illustrated using the energy performance certificate database managed by the Catalan Institute of Energy (ICAEN), which includes a sample of 13,701 offices. Due to the large number of missing values in the database, the k -means clustering algorithm was finally performed over 6,083 energy performance certificates. Seven representative office blocks and offices in industrial buildings and nine representative offices in residential buildings were identified. The results establish the basis for supporting strategic decision-making for energy saving retrofit interventions in existing Spanish offices. [ABSTRACT FROM AUTHOR]

Many studies have investigated different aspects in the decarbonisation of the European housing stock. However, a comprehensive quantitative analysis of the literature on the diffusion of energy efficiency technologies is still missing. We conducted a bibliometric analysis to better understand the knowledge base in the field energy efficiency technology diffusion in the European residential building stock. After the scanning and screening process, we identified 954 scientific articles pertinent to this topic. Through a co-citation network analysis, we generated a visual knowledge structure of the field and by the further investigation of the bibliography we were able to synthesize the state-of-the-art and answer to our initial research questions. Results of the co-citation network show a scattered and fragmented field in many domains. The descriptive analysis highlights this fragmentation, especially on a cross-country level among EU country members. Findings from this study contribute to map the scientific knowledge base in relation to technology diffusion in European residential building projects, identify relevant topic areas, visualize the links between the topics, as well as to recognize research gaps and opportunities. The methodology utilized in this paper proved to be viable approach to map and characterize the knowledge base within a field and can, therefore, be replicated in upcoming studies with analogous ambitions. [ABSTRACT FROM AUTHOR]

• The same building geometries and the same thermal properties of building envelope for different seven climatic cities were assumed. • Energy conservation effect including saving amount and saving ratio were discussed annually and monthly among seven cities. • The inner mechanism of the difference was revealed from the hourly and daily perspectives through two extremely contrast climatic cities. • Guidance and reference were provided for both standard determination and behavioral energy saving for air conditioning systems. Indoor setting temperature is a key controllable parameter by users, highly impacting the total cooling energy usage. In this paper, the hourly cooling load of residential buildings is simulated for seven cities respectively when the cooling setting temperature (CST) increases by 1°C, and the dissimilarity and internal mechanism of the energy saving effect are discussed and revealed from the hourly and daily perspectives through two extremely contrast climatic cities. The results show that although the higher yearly energy saving amount (ESA) can appear in a hotter city, energy saving effect is all significant in different cities. The ESA in hotter months of hotter cities may not be higher than that in the corresponding months of cooler cities, and the hotter cities have more obvious platform features while the cooler cities have single peak features, and the peak value is much higher than the plateau value. The yearly cooling saving ratio (CSR) is higher in cooler cities because CSR depends on both the hourly CSR and its weightiness, and the proportion of the higher ratio hours in cool cities is larger. This study can provide references for both standard determination and behavioral energy saving for air conditioning systems. [ABSTRACT FROM AUTHOR]