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Energy demand attributable to the operation of supermarkets on-site is thought to be responsible for 1% of UK greenhouse gas emissions. In use data show a performance gap approaching a factor of three for overall energy use, with a gap of a factor of six in energy demand for heating. This performance gap indicates significant faults in the conventional modelling route. Current building regulations in the UK require the "building related" energy use of new commercial buildings to comply with particular requirements. Supermarket buildings are therefore modelled according to these protocols to establish their predicted energy demand. The impact on this predicted energy demand of the exclusion of process energy (eg for refrigeration) from these protocols is explored by modelling a supermarket retail floor with heat transfers related to refrigerated cabinets, and comparing the sensitivities of such models with those of models compliant with regulatory protocols. Whereas models compliant with regulatory protocols indicate an advantage of limiting the level of insulation and airtightness, and allowing stratification, to facilitate heat loss through the store envelope; models that include heat transfers around the refrigerated cabinets are found to show that energy demand may be decreased by up to 40% by doubling both insulation and airtightness, and by destratification. This will, however, only apply if rates of air change in buildings in use match those modelled. This shows the importance of including heat transfers around refrigerated cabinets in design modelling, so that appropriate decisions may be taken with respect to building envelope parameters. Compliance modelling protocols should be changed to reflect this. In order to facilitate this change and enable modelling of refrigerated cabinets within a compliance model through a few simple inputs, a set of data and associated algorithms is derived and offered for inclusion in compliance modelling tools.

This thesis describes the development of a Reduced Data Dynamic Energy Model (RdDEM) for simulating the energy performance of UK houses. The vast quantity of Energy Performance Certificate (EPC) data stored at the national scale provides an unprecedented data source for energy modelling. The majority of domestic energy models developed for the UK houses in recent years, including the Standard Assessment Procedure (SAP) model used for generating EPCs, employ BREDEM (Building Research Establishment Domestic Energy Model) based steady state calculation engines. These models fail to represent the transient behaviours that occur between building envelope and systems with external weather conditions and occupants. Consequently, there is an ongoing debate over the suitability of such models for policy making decisions; which has raised the interest in dynamic energy models to overcome these shortcomings. The RdDEM eliminates the main drawback associated with dynamic energy modelling, namely the large amount of required input data compared to steady-state models, by enhancing a reduced set of data which was originally collected for EPCs. A number of new inferences and methodological enhancements were tested and implemented in the RdDEM using a sample of semi-detached houses. In this way, SAP equivalent input data could be converted automatically for use in dynamic energy modelling software, EnergyPlus. Simulations of indoor air temperatures and space heating energy demand from the RdDEM were compared to those from SAP for 83 semi-detached houses. The comparison was also carried out with more detailed models, on a sub-set of the modelled dwellings. Finally, the predicted energy savings that resulted from energy efficiency improvements of the dwellings were compared and estimated potential for saving energy from the RdDEM was quantified. ii The results show that it is technically feasible to develop dynamic energy models of these houses using equivalent inputs. In the majority of cases, the RdDEM predicted lower indoor air temperatures than SAP, and consequently the energy demands were lower. The RdDEM predicted annual space heating demand to be lower than SAP in 72% of the houses, however the difference was less than 10% in 94% of the houses. The RdDEM predicted slightly higher (< 2%) energy saving potentials compared to SAP when the same set of energy saving measures were implemented in both models. The development of these new methods for automatically creating SAP equivalent inputs from reduced data but for use in a dynamic energy model offers new opportunities for inter-model comparisons as well as a dynamic alternative to the SAP when variations in energy demand and indoor air temperatures are required.

Climate change projections estimate a rise of approximately 3 °C by the 2080‘s for most of the UK (under a medium emissions scenario at 50% probability level, 1961-1990 baseline). Warming is of particular concern for urban areas due to the issues of urban densification and the Urban Heat Island (UHI) effect. To combat warming, one adaptation strategy that has been suggested for urban areas is increasing the proportion of greenspace, such as parks, gardens, street tree plantings, and green roofs. While a number of studies have investigated the cooling effect of greenspace in terms of park size, proximity to a park, or area covered by tree canopy, little is yet known about the specific types of greenspace that contribute to its cooling effectiveness and how this relates to building energy demand. This thesis employs an interdisciplinary approach to model fine-scale changes to greenspace for a temperate northern UK city, linking the resulting microclimate changes to building energy consumption in commercial buildings. Using the urban microclimate model ENVI-met, two study areas (one urban one suburban) were modelled with seven different greenspace scenarios (a base case representing current field conditions, +5% new trees, +5% mature trees, +5% hedges, addition of a green roof on the largest building, changing all current greenspace to grass only, and changing all current greenspace to asphalt only) for a summer day in July 2010. The models were calibrated based on measured air temperature data and then analysed for microclimate changes due to each greenspace scenario. Both the modelled and measured microclimate data were then used to inform a series of building energy models using IES-VE 2012 for three commercial building types, estimating summer cooling and winter heating trade-offs due to greenspace effects. For the most effective scenario of adding 5% mature trees to the urban case study, the microclimate modelling estimates a maximum hourly air temperature reduction of nearly 0.7 °C at 5 pm and surface temperature reductions up to 1.7 °C at 3 pm. In the suburban case study, a 5% increase in mature deciduous trees can reduce mean hourly surface temperatures by 1 °C between 10 am and 5 pm, while the worst case scenario of replacing all current vegetation (20% of the study area) with asphalt results in increased air temperature of 3.2 °C at mid-day. The building energy modelling estimates a reduction of 2.7% in July chiller energy due to the combination of reduced UHI peak hours and eight additional trees (four on the north side and four on the south side) of a three-storey shallow plan building. These energy savings increase to 4.8% under a three-day period of peak UHI conditions. While winter boiler energy usage shows large reductions for a building in an urban location with a low proportion of greenspace (as compared to a suburban location), this benefit is marginal when analysed in terms of carbon trade-offs between summer cooling and winter heating requirements.

Buildings and the built environment account for a significant portion of the global energy use and greenhouse gas emissions, and reducing the energy demand in this sector is crucial for a sustainable energy transition. This highlights the need for accurate and large-scale estimations and predictions of the future energy demand in buildings. Urban building energy modeling (UBEM) is an analytical tool for precise and high-quality energy modelling of city-scale building stocks, which is growing in interest as a useful tool for researchers and decision-makers worldwide. This thesis contributes to the understanding and future development in the field of UBEM and multi-variate cluster analysis. Based on a review of contemporary literature, possible improvements and knowledge gaps regarding UBEM are identified. The majority of UBEM studies are developed for similar applications, and some challenges are close to universal. Difficulties in data acquisition and the identification and characterisation of building archetypes are frequently addressed. Drawing on conclusions from the review, a clustering methodology for identifying building archetypes for hybrid UBEM was developed. The methodology utilised the k-means cluster analysis algorithm for multiple diverse parameters, including socio-economic indicators, and is based on open data sets which eliminates data acquisition issues and allows for easy adaptation. Building archetypes were successfully identified for two large data sets, and proved to be representative of the sample building stock. The results of the analysis also show that the error metric values diverge after a certain number of clusters, for multiple runs of the algorithm. This property of the algorithm in combination with the use of both existing and novel error metrics provide a reliable method for determining the optimal number of clusters. The methodology developed in this thesis enables for an improved modelling process, as a part of a complete UBEM.

TECNALIA Research &amp; Innovation is the first privately funded applied research centre in Spain and one of the leading such centres in Europe. A renowned technological agent in the development of innovative and sustainable solutions for the energy and environmental challenges of industry and society, TECNALIA addresses the complex challenges of energy supply chain and energy systems. Contributing to these efforts, the project builds a model of the energy system in Basque Country, which is characterized by (1) high representation of industry; the most energy intensive sector (about 45% in the energy demand ) (2) the high consumption of fossil fuels (about 83% of Basque energy use in 2010). These challenges (and others) along with the compliance with EU targets to reduce GHGs emissions, to promote renewables and implement measures for energy savings and efficient use of energy, are key drivers to simulate different policy-based scenarios to study and analyze the impact of these measures over different time frames. The aim of this thesis is prepare energy scenarios for the Basque Country for 2050, taking into account different low-carbon pathways and integrating a life-cycle perspective which includes not only the impact during the use and operation phase of energy systems, but also the impacts during the other life cycle phases (manufacturing, installation, end of life).

The purpose of this thesis is to generate a deeper understanding of the representation of hydropower in long-term models. This is done by mapping and modelling (cascading) hydropower in Sweden with the Open Source energy MOdelling SYStem (OSeMOSYS). The first part of the thesis builds on a literature review and provides an introduction to hydropower in Sweden. The second part focuses on implementing the storage equations in OSeMOSYS. These are applied by modelling hydropower at various levels of detail to evaluate the result when the depth of detail of the storage modelling is increased. First, a model of Sweden without hydropower storage is modelled. Then, two models were set up which include storage; one with one hydropower storage for all of Sweden, one with nine rivers with storage. Finally, two models considering cascading hydropower with storage were developed; where the first is an expansion of the model with one storage for all of Sweden and the second model examine two rivers more thorough. The remain-ing power system is represented in a stylised fashion, compliant with prevailing long-term energy modelling techniques. The implications of the different levels of detail are compared and discussed. The comparisons show that it is important to consider the lev-el of detail when looking at the short-term effects of long-term energy models.

A modeling tool was developed capable of evaluating the thermal performance of a commercial building, for the purpose of objectively quantifying the impacts of both operational changes and technological retrofits. The modeling tool was created using a steady state energy balance approach, discretized into half hour time steps to capture the time varying characteristics of the rate of heat transfer through the building envelope, the ventilation systems, appliance heat gains, heat generated by electricity consumption, solar energy transfer and space heating through exhaust gas energy recovery with the TEG POWER system. Several experimental facilities were used to validate the modeling tool, and to provide inputs to the case studies presented. Data from two separate commercial baking operations was collected, and was shown to be in agreement with the model predictions with a 7% error. Several energy conservation measures were simulated, including switching to idealized methods of exhaust ventilation, sealing and insulating appliances, shutting down appliances during unoccupied hours, and the inclusion of exhaust gas energy harvesting. Implementing all four conservation measures at a single restaurant had the effect of reducing electricity consumption by 14% or approximately 17,700 kWh (64 GJ), and reducing natural gas consumption by 60% or approximately 18,200 m3 (608 GJ) annually. In contrast, proceeding directly to the energy harvesting solution, and bypassing other conservation measures, only allowed for 20% of the total potential energy savings to be realized. If the concepts identified are implemented across 2000 comparable restaurants in Ontario, there is a potential to reduced electricity consumption by 44.4 million kWh and natural gas consumption by 33.7 million cubic meters annually. The measures would effectively eliminate 65,500 metric tonnes of CO2 emissions every year. / Thesis / Master of Applied Science (MASc)

The mining industry in South Africa has long been a crucial contributor to the Gross Domestic Product (GDP) starting in the 18th century. In 2010, the direct contribution towards the GDP from the mining industry was 10% and 19.8% indirect. During the last decade global financial uncertainty resulted in commodity prices hitting record numbers when Gold soared to a high at $1900/ounce in September 2011, and thereafter the dismal decline to a low of $1200/ounce in July 2013. Executives in these markets have reacted strongly to reduce operational costs and focussing on better production efficiencies. One such a cost for mining within South Africa is the Operational Expenditure (OPEX) associated with electrical energy that has steadily grown on the back of higher than inflation rate escalations. Companies from the Energy Intensive User Group (EIUG) witnessed energy unit prices (c/kWh) and their percentage of OPEX grow to 20% from 7% in 2008. The requirement therefore is for more accurate energy budget forecasting models to predict what energy unit price escalations (c/kWh) occur along with the required units (kWh) at mines or new projects and their impact on OPEX. Research on statistical models for energy forecasting within the mining industry indicated that the historical low unit price and its notable insignificant impact on OPEX never required accurate forecasting to be done and thus a lack of available information occurred. AngloGold Ashanti (AGA) however approached Deloittes in 2011 to conclude a study for such a statistical model to forecast energy loads on one of its operations. The model selected for the project was the Monte Carlo analysis and the rationale made sense as research indicated that it had common uses in energy forecasting at process utility level within other industries. For the purpose of evaluation a second regression model was selected as it is well-known within the statistical fraternity and should be able to provide high level comparison to the Monte Carlo model. Finally these were compared to an internal model used within AGA. Investigations into the variables that influence the energy requirement of a typical deep level mine indicated that via a process of statistical elimination tonnes broken and year are the best variables applicable in a mine energy model for conventional mining methods. Mines plan on a tonnage profile over the Life of Mine (LOM) so the variables were known for the given evaluation and were therefore used in both the Monte Carlo Analysis that worked on tonnes and Regression Analysis that worked on years. The models were executed to 2040 and then compared to the mine energy departments’ model in future evaluations along with current actuals as measured on a monthly basis. The best comparison against current actuals came from the mine energy departments’ model with the lowest error percentage at 6% with the Regression model at 11% and the Monte Carlo at 20% for the past 21 months. This, when calculated along with the unit price path studies from the EIUG for different unit cost scenarios gave the Net Present Value (NPV) reduction that each model has due to energy. A financial analysis with the Capital Asset Pricing Model (CAPM) and the Security Market Line (SML) indicated that the required rate of return that investors of AGA shares have is 11.92%. Using this value the NPV analysis showed that the mine energy model has the best or lowest NPV impact and that the regression model was totally out of line with expectations. Investors that provide funding for large capital projects require a higher return as the associated risk with their money increases. The models discussed in this research all work on an extrapolation principle and if investors are satisfied with 6% error for the historical 2 years and not to mention the outlook deviations, then there is significance and a contribution from the work done. This statement is made as no clear evidence of any similar or applicable statistical model could be found in research that pertains to deep level mining. Mining has been taking place since the 18th century, shallow ore resources are depleted and most mining companies would therefore look towards deeper deposits. The research indicates that to some extent there exist the opportunity and some rationale in predicting energy requirements for deep level mining applications. Especially when considering the legislative and operational cost implications for the mining houses within the South African economy and with the requirements from government to ensure sustainable work and job creation from industry in alignment with the National Growth Path (NGP). For this, these models should provide an energy outlook guideline but not exact values, and must be considered along with the impact on financial figures. / MBA, North-West University, Potchefstroom Campus, 2014