In fire, any information about the actual condition within the building could be essential for quick and safe response of both fire–fighters and occupants. In most cases, however, the emergency responders will rarely be aware of the actual conditions within a building and they will have to make critical decisions based on limited information. Recent buildings are equipped with numbers of sensors which may potentially contain useful information about the fire; however, most buildings do not have capability of exploiting these sensors to provide any useful information beyond the initial stage of warning about the possible existence of a fire. A sensor–linked modelling tool for live prediction of uncontrolled compartment fires, K– CRISP, has therefore been developed. The modelling strategy is an extension of the Monte– Carlo fire model, CRISP, linking simulations to sensor inputs which controls evolution of the parametric space in which new scenarios are generated, thereby representing real–time “learning” about the fire. CRISP itself is based on a zone model representation of the fire, with linked capabilities for egress modelling and failure prediction for structural members, thus providing a major advantage over more detailed approaches in terms of flexibility and practicality, though with the conventional limitations of zone models. Large numbers of scenarios are required, but computational demands are mitigated to some extent by various procedures to limit the parameters which need to be varied. HPC (high performance computing) resources are exploited in “urgent computing” mode. K–CRISP was demonstrated in conjunction with measurements obtained from two sets of full–scale fire experiments. In one case, model execution was performed live. The thesis further investigates the predictive capability of the model by running it in pseudo real–time. The approach adopted for steering is shown to be effective in directing the evolution of the fire parameters, thereby driving the fire predictions towards the measurements. Moreover, the availability of probabilistic information in the output assists in providing potential end users with an indication of the likelihood of various hazard scenarios. The best forecasts are those for the immediate future, or for relatively simple fires, with progressively less confidence at longer lead times and in more complex scenarios. Given the uncertainties in real fire development the benefits of more detailed model representations may be marginal and the system developed thus far is considered to be an appropriate engineering approach to the problem, providing information of potential benefit in emergency response. Thus, the sensor–linked model proved to be capable of forecasting the fire development super–real– time and it was also able to predict critical events such as flashover and structural collapse. Finally, the prediction results are assessed and the limitations of the model were further discussed. This enabled careful assessment of how the model should be applied, what sensors are required, and how reliable the model can be, etc.

Residential space and water heating accounts for around 13% of the greenhouse gas emissions of the UK. Reducing this is essential for meeting the national emission reduction target of 80% by 2050 from the 1990 baseline. One of the strategies adopted for achieving this is focused around large scale shift towards electrical heating. This could lead to unsustainable disparity between the daily peak and off-peak electricity loads, large seasonal variation in electricity demands, and challenges of matching the short and long term supply with the demands. These challenges could impact the security and resilience of UK electricity supply, and needs to be addressed. Rechargeable Thermal Energy Storage (TES) in residential buildings can help overcome these challenges by enabling Heat Demand Shifts (HDS) to off-peak times, reducing the magnitude of the peak loads, and the difference between the peak and off-peak loads. To be effective a wide scale uptake of TES would be needed. For this to happen, the benefits and impacts of TES both for the demand side and the supply side have to be explored, which could vary considerably given the diverse physical, thermal, operational and occupancy characteristics of the UK housing stock. A greater understanding of the potential consequence of TES in buildings is necessary. Such knowledge could enable appropriate policy development to help drive the uptake of TES or to encourage development of alternative solutions. Through dynamic building simulation in TRNSYS, this work generated predictions of the space and water heating energy and power demands, and indoor temperature characteristics of the UK housing stock. Twelve building archetypes were created consisting of: Detached, semi-detached, mid-terrace and flat built forms with thermal insulation corresponding to the 1990 building regulation, and occupied floor areas of 70m2, 90m2 and 150m2. Typical occupancy and operational conditions were used to create twelve Base Case scenarios, and simulations performed for 60 winter days from 2nd January. HDS of 2, 3 and 4 hours from the grid peak time of 17:00 were simulated with sensible TES system sizes of 0.25m3, 0.5m3 and 0.75m3, and water storage temperatures of 75°C and 95°C. Parametric analysis were performed to determine the impacts and benefits of: thermal insulation equivalent to 1980, 1990 (Base Case), 2002 and 2010 building regulation; locations of Gatwick (Base Case) and Aberdeen; heating durations of 6, 9 (Base Case), 12 and 16 hours per day; thermostat settings of 19°C, 21°C (Base Case) and 23°C, and number of occupiers of 1 person and 3 persons (Base Case) per household. Good correlation was observed between the simulated results and published heat energy consumption data for buildings with similar thermal, physical, occupancy and operational conditions. The results allowed occupied space temperatures and overall daily and grid peak time energy consumption to be predicted for the range of building archetypes and parameter values considered, and the TES size necessary for a desired HDS to be determined. The main conclusions drawn include: The overall daily energy consumption predictions varied from 36.8kWh to 159.7kWh. During the critical grid peak time (17:00 to 21:00) the heat consumption varied from 4.2kWh to 58.7kWh, indicating the range of energy demands which could be shifted to off-peak times. On average, semi-detached, mid-terrace, and flat built forms consumed 7.0%, 13.8% and 22.7% less energy for space heating than the detached built form respectively. Thermal insulation changing from the 1990 building regulation level to the 1980 and 2010 building regulation levels could change the mean energy use by +14.7% and -19.6% respectively. A 0.25m3 TES size with 75°C water storage temperature could enable a 2 hour HDS, shifting 4.3kWh to 11.7kWh (mean 8.7kWh) to off peak times, in all 70m2 Base Case archetypes with the 60 day mean thermal comfort of 100%, but with the minimum space temperature occasionally dropping below an 18°C thermal comfort limit. A 0.5m3 TES size and water storage of 95°C could allow a 3 hour HDS, shifting 9.8kWh to 28.2kWh (mean 18.7kWh) to off peak times, in all 90m2 Base Case archetypes without thermal comfort degradation below 18°C. A 0.75m3 TES with a 95°C water temperature could provide 4 hour HDS, shifting 13.9kWh to 47.7kWh (mean 27.2kWh) to off peak times, in all 150m2 Base Case archetypes with 100% mean thermal comfort but with the 60 day minimum temperature occasionally dropping below the 18°C thermal comfort limit in the detached built form. Improving the thermal insulation of the buildings was found to be the best way to improve the effectiveness of HDS with TES, in terms of the demand shift period achievable with minimal thermal comfort impact. A 4 hour HDS with 100% thermal comfort is possible in all 90m2 floor area buildings with a 0.25m3 tank and a water storage temperature of 75°C provided that the thermal insulation is as per 2010 building regulation. Recommendations for further research include: 1) creating larger number of archetype models to reflect the housing stock; 2) using heat pumps as the heat source so that the mean effect on the grid from electric heating loads can be predicted; 3) taking into account the costs associated with taking up HDS with TES, in terms of capital expenses and space requirement for housing the TES system; 4) considering alternative methods of heat storage such as latent heat storage to enhance the storage capacity per unit volume; and 5) incorporating zonal temperature control, for example, only heating rooms that are occupied during the demand shift period, which could ensure better thermal comfort in the occupied space and extend the demand shift period.

Practicing engineers need an integrated building, HVAC and control simulation tool for optimum HVAC design and retrofit. Various tools are available to the researchers, but these are not appropriate for the consulting engineer. To provide the engineer with a tool which can be used for typical HVAC projects, new models for building, HVAC and control simulation are introduced and integrated in a user-friendly, quick-to-use tool. The new thermal model for buildings is based on a transfer matrix description of the heat transfer through the building shell. It makes provision for the various heat flow paths that make up the overall heat flow through the building structure. The model has been extensively verified with one hundred and three case studies. These case studies were conducted on a variety of buildings, ranging from a 4m2 bathroom, to a 7755 m2 factory building. Eight of the case studies were conducted independently in the Negev Desert in Israel. The thermal model is also used in a program that was custom-made for the AGREMENT Board (certification board for the thermal performance of new low-cost housing projects). Extensions to the standard tool were introduced to predict the potential for condensation on the various surfaces. Standard user patterns were incorporated in the program so that all the buildings are evaluated on the same basis. In the second part of this study the implementation of integrated simulation is discussed. A solution algorithm, based on the Tarjan depth first-search algorithm, was implemented. This ensures that the minimum number of variables are identified. A quasi-Newton solution algorithm is used to solve the resultant simultaneous equations. Various extensions to the HVAC and control models and simulation originally suggested by Rousseau [1] were implemented. Firstly, the steady-state models were extended by using a simplified time-constant approach to emulate the dynamic response of the equipment. Secondly, a C02 model for the building zone was implemented. Thirdly, the partload performance of particular equipment was implemented. Further extensions to the simulation tool were implemented so that energy management strategies could be simulated. A detailed discussion of the implications of the energy management systems was given and the benefits of using these strategies were clearly illustrated, in this study. Finally, the simulation tool was verified by three case studies. The buildings used for the verification ranged from a five-storeyed office and laboratory building, to a domestic dwelling. The energy consumption and the dynamics of the HVAC systems could be predicted sufficiently accurately to warrant the use of the tool for future building retrofit studies / Thesis (PhD)--University of Pretoria, 1997. / gm2014 / Mechanical and Aeronautical Engineering / unrestricted

To help solve the climate crisis, municipalities are increasingly modifying their building codes and offering incentives to create greener buildings in their cities. But, city planners find it difficult to set and assess these policies, as most municipalities do not have the types of data used in urban building energy modelling (UBEM) that would allow their planners to forecast the impacts of various building policies. This thesis offers techniques for operating in this data-poor environment, presenting best practices for developing data-driven archetypes with machine learning, demonstrating inference of parameter values to improve archetypes by using surrogate modelling and genetic algorithms, and a demonstration of techniques for assessing residential retrofit impact in a data-limited environment, where data is neither detailed enough to create an in-depth single archetype study, nor broad enough to create an UBEM model. It will be shown that inference techniques have potential, but need a certain amount of detailed data to work, though far less than traditional UBEM techniques. For performing residential retrofit, it will be shown the lack of ideal detailed data does not present an overwhelming obstacle to drawing useful conclusions and that meaningful insight can be extracted despite the lack of precision. Overall, this thesis shows a data-poor environment, while challenging, is a viable environment for both research and policy modelling. / Graduate