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Though uncertainties of input variables may have significant implications on building simulations, they are quite often not identified, quantified, or included in building simulations results. This paper considers climatic deterministic, uncertainty, and sensitivity analysis through a series of simulations using the CIBSE UKCIP02 future weather years, CIBSE TM48 for design summer years (DSYs), and the latest CIBSE TM49 DSY future weather data which incorporates the UKCP09 projections to evaluate the variance and the impact of differing London future weather files on indoor operative temperature of a detached dwelling in the United Kingdom using the CIBSE TM52 overheating criteria. The work analyses the variability of comparable weather data set to identify the most influential weather parameters that contribute to thermal comfort implications for these dwellings. The choice of these weather files is to ascertain their differences, as their development is underpinned by different climatic projections. The overall pattern of the variability of the UKCIP02 and UKCP09 Heathrow weather data sets under Monte Carlo sensitivity consideration do not seem to be very different from each other. The deterministic results show that the operative temperatures of the UKCIP02 are slightly higher than those of UKCP09, with the UKCP09 having a narrow range of operative temperatures. The Monte Carlo sensitivity analysis quantified and affirmed the dry bulb and radiant temperatures as the most influential weather parameters that affect thermal comfort on dwellings.

In 2008, the Chartered Institution of Building Services Engineers (CIBSE TM46 UC) presented an annual-fixed thermal energy benchmark of 240 kWh/m2/yr for university campus (UC) buildings as an attempt to reduce energy consumption in public buildings. However, the CIBSE TM46 UC benchmark fails to consider the difference between energy demand in warm and cold months, as the thermal performance of buildings largely depends on the ambient temperature. This paper presents a new generation of monthly thermal energy benchmarks (MTEBs) using two computational methods including mixed-use model and converter model, which consider the variations of thermal demand throughout a year. MTEBs were generated using five basic variables, including mixed activities in the typical college buildings, university campus revised benchmark (UCrb), typical operation of heating systems, activities impact, and heating degree days. The results showed that MTEBs vary from 24 kWh/m2/yr in January to one and nearly zero kWh/m2/yr in June and July, respectively. Based on the detailed assessments, a typical college building was defined in terms of the percentage of its component activities. Compared with the 100% estimation error of the TM46 UC benchmark, the maximum 21% error of the developed methodologies is a significant achievement. The R-squared value of 99% confirms the reliability of the new generation of benchmarks.

Enclosed-space ventilation plays a pivotal role in enhancing indoor air quality (IAQ). Various regulations and guidelines determine minimum fresh air requirements, varying by the application, country, etc. The present study employs real-time indoor CO2 measurements to calculate the actual air change rate (ACH) and models the building’s compliance with Chartered Institution of Building Services Engineers (CIBSE) guidelines to assess its impact on IAQ. The contaminant considered is the SARS-CoV-2 virus with multiple infectors, and the study calculates the virus’s transmission risk under different scenarios. Fresh air is assumed to be provided by mechanical or hybrid ventilation. The results reveal that the current building conditions are unacceptable, showing a significant gap from the recommended guideline rates. Meeting CIBSE recommendations ensures indoor air safety in terms of CO2 concentration. However, in extreme conditions with five infectors, findings show a 14% reduction in mechanical ventilation efficacy. Among the hybrid ventilation scenarios, combining CIBSE rates with natural ventilation, with a 30% door and window opening from the class outset, proves most effective in reducing the virus’s transmission risk, with a 58–70% reduction from the building’s current condition. Furthermore, implementing supplementary measures like air cleaners resulted in an additional 28–50% reduction in transmission risk.

Due to the impacts of carbon emissions on climate change and the expected dramatic increase in global cooling demand by 2050, it is of a paramount importance that the required energy to cool buildings is accurately predicted. This ensures that equipment is appropriately sized, which ultimately reduces energy consumption and global carbon emissions. CIBSE and ASHRAE standards are both widely adopted for cooling load predictions, but they adopt different calculation methods, with CIBSE adopting admittance and ASHRAE adopting radiant time series (RTS), which produce significantly different results in cooling load. This study comparatively and qualitatively evaluates the CIBSE admittance and ASHRAE RTS cooling load models by analysing their structures and key input parameters for a mock-up building to identify inconsistencies between the two methods. There were flaws within both models that resulted in the CIBSE method underpredicting the cooling load, whereas the ASHRAE method typically overpredicting it. This resulted in a maximal average difference of over 60%. The substantial predicted cooling load difference was mainly caused by the ASHRAE RTS model, which was highly receptive to solar gains, and it consequently led to overprediction in cooling load when compared to the CIBSE admittance model.

The role and influence of building services engineers are undergoing rapid change and are pivotal to achieving low-carbon buildings. However, textbooks in the field have tended to remain fairly traditional with a detailed focus on the technicalities of heating, ventilation and air conditioning (HVAC) systems, often with little wider context. This book addresses that need by embracing a contemporary understanding of the urgent challenge to address climate change, together with practical approaches to energy efficiency and carbon mitigation for mechanical and electrical systems, in a concise manner. The essential conceptual design issues for planning the principal building services systems that influence energy efficiency are examined in detail. These are HVAC and electrical systems. In addition, the following issues are addressed: background issues on climate change, whole-life performance and design collaboration generic strategies for energy-efficient, low-carbon design health and wellbeing and post occupancy evaluation building ventilation air conditioning and HVAC system selection thermal energy generation and distribution systems low-energy approaches for thermal control electrical systems, data collection, controls and monitoring building thermal load assessment building electric power load assessment space planning and design integration with other disciplines. In order to deliver buildings that help mitigate climate change impacts, a new perspective is required for building services engineers, from the initial conceptual design and throughout the design collaboration with other disciplines. This book provides a contemporary introduction and guide to this new approach, for students and practitioners alike.

Background Context-sensitive systems use contextual information in order to adapt to the user’s current needs or requirements failure. Therefore, they need to dynamically adapt their behavior. It is of paramount importance to specify and analyze the intended behavior of these systems before they are fully implemented. The behavioral specification can be used for requirements validation in order to check if these systems will be able to achieve their goals. Moreover, the reasoning about properties of these systems, such as deadlocks, reachability, completeness and correctness of the system, can be supported. Therefore, it is of paramount importance to have an approach to specify the dynamic behavior of the context-sensitive systems. In this work, we propose the GO2S (GOals to Statecharts) process to systematically derive the behavior of context-sensitive systems, expressed as statecharts, from requirements models, described as goal models. Results The GO2S process addresses the specification of the tasks required for monitoring of requirements satisfaction as well as the system adaptation according to the context, the operationalization of non-functional requirements and prioritization of alternatives to be used at runtime (variants). It is an iterative process centered on the incremental refinement of a goal model, obtaining different views of the system (design, contextual, behavioral). Furthermore, we conducted a controlled experiment to evaluate the statecharts produced following GO2S process (experimental group) in relation to the ones elaborated in ad-hoc fashion (control group). Conclusions The experiment results showed that the structural complexity of the statecharts of the experimental group was lower in relation to the control group. Moreover, the average of functionalities whose behavior was modeled according to the specification and the time spent to produce the models of the experimental group were higher in relation to the control group. Besides, the subjects agreed that the GO2S process is easy to use.

Background Establishing representative samples for Software Engineering surveys is still considered a challenge. Specialized literature often presents limitations on interpreting surveys’ results, mainly due to the use of sampling frames established by convenience and non-probabilistic criteria for sampling from them. In this sense, we argue that a strategy to support the systematic establishment of sampling frames from an adequate source of sampling can contribute to improve this scenario. Method A conceptual framework for supporting large scale sampling in Software Engineering surveys has been organized after performing a set of experiences on designing such strategies and gathering evidence regarding their benefits. The use of this conceptual framework based on a sampling strategy developed for supporting the replication of a survey on characteristics of agility and agile practices in software processes is depicted in this paper. Result A professional social network ( Linkedln ) was established as the source of sampling and its groups of interest as the units for searching members to be recruited. It allowed to deal with a sampling frame composed by more than 110,000 members (prospective subjects) distributed over 19 groups of interest. Then, through the similarity levels observed among these groups, eight strata were organized and 7745 members were invited, from which 291 have confirmed participation and answered the questionnaire. Conclusion The heterogeneity and number of participants in this replication contributed to improve the strength of original survey’s results. Therefore, we believe the sharing of this experience, the instruments and plan can be helpful for those researchers and practitioners interested on executing large scale surveys in Software Engineering.

Typical weather data (TWD) consists of 8760 values of various selected meteorological parameters such as ambient temperature, solar radiation, relative humidity and wind velocity, and are originally derived from long-term data. In this paper, TWDs have been generated, using three different methodologies, from hourly meteorological data measured in Seoul, Daejeon, Daegu, Gwangju, and Busan. The aims of this study were to compare the simulation results from the three methodologies (ISO Test Reference Year, CIBSE Test Reference Year, and Typical Meteorological Year) with those from individual years and their long-term means to assess the important five components of the building envelope (wall-to-window ratio, window overhang, window transmissivity, window U-value, and wall U-value) for the influence on the heating and cooling load and to study the influence of the different weather parameters (temperature and solar radiation). Results of the comparison show that predicted monthly load and energy consumption profiles from the TWD tended to follow the long-term mean quite closely. The load calculations vary almost linearly as the building envelope components varied apart from the window U-value. The yearly average values of mean bias error and root mean squared error for the heating and cooling loads were evaluated for the three TWDs and long-term measured meteorological data. Practical application: This paper will enable engineers and building designers of buildings in South Korea to assess the heating and cooling loads with more confidence. The paper will enable designers to use the appropriate weather data when using computer simulation for the design. The designer will also be able to assess the energy efficiency better.

Background Software reference architectures are becoming widely adopted by organizations that need to support the design and maintenance of software applications of a shared domain. For organizations that plan to adopt this architecture-centric approach, it becomes fundamental to know the return on investment and to understand how software reference architectures are designed, maintained, and used. Unfortunately, there is little evidence-based support to help organizations with these challenges. Methods We have conducted action research in an industry-academia collaboration between the GESSI research group and everis , a multinational IT consulting firm based in Spain. Results The results from such collaboration are being packaged in order to create guidelines that could be used in similar contexts as the one of everis . The main result of this paper is the construction of empirically-grounded guidelines that support organizations to decide on the adoption of software reference architectures and to gather evidence to improve RA-related practices. Conclusions The created guidelines could be used by other organizations outside of our industry-academia collaboration. With this goal in mind, we describe the guidelines in detail for their use.

Background The stakeholders of a software system are, to a greater or lesser extent, concerned about its software architecture, as an essential artifact for capturing the key design decisions of the system. The architecture is normally documented in the Software Architecture Document (SAD), which tends to be a large and complex technical description, and does not always address the information needs of every stakeholder. Individual stakeholders are interested in different, sometimes overlapping, subsets of the SAD and they also require varying levels of detail. As a consequence, stakeholders are affected by an information overload problem, which in practice discourages the usage of the architectural knowledge and diminishes its value for the organization. Methods This work presents a semi-automated approach to recommend relevant contents of a given SAD to specific stakeholder profiles. Our approach assumes that SADs are hosted in Wikis, which not only favor communication and interactions among stakeholders, but also enable us to apply User Profiling techniques to infer stakeholders’ interests with respect to particular documents. Results We have built a recommendation tool implementing our approach, which was tested in two experiments with Wiki-based SADs. The experiments aimed at assessing the performance reached by our tool when inferring stakeholders’ interests. To this end, precision and recall metrics were used. Conclusions Although preliminary, the results have shown that the recommendations of the tool help to find the architectural documents that best match the stakeholders’ interests.