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

A large portion of the existing building stock in North America is comprised of post-World War II high-rise apartment buildings, particularly in the Greater Golden Horseshoe in Ontario. They are home to a large portion of the Canadian population. These buildings are nearly 50 years old and reaching the end of their useful lifespan. Significant deterioration has lead to life safety concerns, poor standards of living, and aesthetic degradation. They also consume a significant amount of energy resulting in contributing to Canada’s high per capita greenhouse gas emissions. This thesis investigates the impact of various retrofit strategies on the energy consumption, durability, and occupant comfort of the towers. The building enclosure is the primary focus. The impacts were analyzed using three approaches. Whole building energy consumption was simulated by adapting a spreadsheet based Building Energy and Loads Analysis (BELA) model, originally intended for office buildings. Heat flow and temperatures across the enclosures were modeled using a two-dimensional finite element model (Therm 5.2). A single, theoretical building dubbed the, “Archetype”, was developed to define the characteristics of a “typical” tower using details extracted from four sets of drawings for towers built in Toronto during the late 1960s. Various quantities and configurations of thermal insulation were added to the Archetype and the resulting effective thermal resistances were modeled. Adding insulation to the interior significantly reduces the effectiveness of any added thermal resistance. Insulating on the exterior allows the insulation around the balconies to reach 80% of its rated value, even without insulating the balconies. Energy efficiency measures (EEMs) including retrofitting the walls, windows, appliances, or HVAC equipment were simulated and it was found that each on its own did not have a major impact on annual energy consumption. Packages of EEMs were created and simulated. It was found that a basic and high-performance whole building retrofit packages would save approximately 40% and 55% of the annual energy consumption, respectively, based on the Archetype. An analysis and discussion of the enclosure retrofit impacts on freeze-thaw potential, interior surface and interstitial condensation, occupants’ thermal comfort, and passive thermal comfort was completed. An interior versus exterior enclosure retrofit comparison summary illustrated that an exterior enclosure retrofit has significant benefits relative to an interior retrofit including ease of construction, greater durability, and improved comfort. The difference in annual energy reduction between an interior and exterior enclosure retrofit was small.

Over the last 20 years, electricity markets around the world have gradually been deregulated, creating wholesale markets in which generating companies compete for the right to supply electricity, through an offering system. This thesis considers the optimisation of the offering process from the perspective of an individual generator, subject to intertemporal constraints including fuel limitations, correlated rest-of-market behaviour patterns and unit operational decisions. Contributions from the thesis include a Pre-Processing scheme that results in considerable computational benefits for a two-level Dynamic Programming method, in addition to the development of a new process that combines the techniques of Decision Analysis and Dynamic Programming.

Lithuania has limited domestic energy resources, and is therefore, heavily dependent on imports of oil products and natural gas. Lithuania imported around 90% of its oil and 100% of natural gas in 2009. Particularly, after the accession to the European Union (EU), and decommissioning of main electricity generation source Ignalina Nuclear Power Plant (NPP), energy security became one of the main concerns. Therefore, it is vital to evaluate different pathways the country could take in order to achieve desirable energy security, and ensure sustainable development of the energy system in Lithuania. The study was conducted using LEAP, the Long range Energy Alternatives Planning System, to develop energy policy analysis. Different scenarios presented in the report show how Lithuanian energy system would react in given different circumstances. Moreover, it demonstrates how implementation of existing energy projects separately or combined together would affect the level of energy security and sustainability in Lithuania. The research shows that current government policies could lead Lithuania to more secure and sustainable energy future. However, in a long run higher investments in renewable energy might be more environmentally and economically competitive alternative.

A large portion of the existing building stock in North America is comprised of post-World War II high-rise apartment buildings, particularly in the Greater Golden Horseshoe in Ontario. They are home to a large portion of the Canadian population. These buildings are nearly 50 years old and reaching the end of their useful lifespan. Significant deterioration has lead to life safety concerns, poor standards of living, and aesthetic degradation. They also consume a significant amount of energy resulting in contributing to Canada’s high per capita greenhouse gas emissions. This thesis investigates the impact of various retrofit strategies on the energy consumption, durability, and occupant comfort of the towers. The building enclosure is the primary focus. The impacts were analyzed using three approaches. Whole building energy consumption was simulated by adapting a spreadsheet based Building Energy and Loads Analysis (BELA) model, originally intended for office buildings. Heat flow and temperatures across the enclosures were modeled using a two-dimensional finite element model (Therm 5.2). A single, theoretical building dubbed the, “Archetype”, was developed to define the characteristics of a “typical” tower using details extracted from four sets of drawings for towers built in Toronto during the late 1960s. Various quantities and configurations of thermal insulation were added to the Archetype and the resulting effective thermal resistances were modeled. Adding insulation to the interior significantly reduces the effectiveness of any added thermal resistance. Insulating on the exterior allows the insulation around the balconies to reach 80% of its rated value, even without insulating the balconies. Energy efficiency measures (EEMs) including retrofitting the walls, windows, appliances, or HVAC equipment were simulated and it was found that each on its own did not have a major impact on annual energy consumption. Packages of EEMs were created and simulated. It was found that a basic and high-performance whole building retrofit packages would save approximately 40% and 55% of the annual energy consumption, respectively, based on the Archetype. An analysis and discussion of the enclosure retrofit impacts on freeze-thaw potential, interior surface and interstitial condensation, occupants’ thermal comfort, and passive thermal comfort was completed. An interior versus exterior enclosure retrofit comparison summary illustrated that an exterior enclosure retrofit has significant benefits relative to an interior retrofit including ease of construction, greater durability, and improved comfort. The difference in annual energy reduction between an interior and exterior enclosure retrofit was small.

This paper describes a methodology to assess the energy performance of residential buildings starting from the semantic 3D city model of Vienna. Space heating, domestic hot water and electricity demand are taken into account. The paper deals with aspects related to urban data modelling, with particular attention to the energy-related topics, and with issues related to interactive data exploration/visualisation and management from a plugin-free web-browser, e.g. based on Cesium, a WebGL virtual globe and map engine. While providing references to existing previous works, only some general and introductory information is given about the data collection, harmonisation and integration process necessary to create the CityGML-based 3D city model, which serves as the central information hub for the different applications developed and described more in detail in this paper. The work aims, among the rest, at developing urban decision making and operational optimisation software tools to minimise non-renewable energy use in cities. The results obtained so far, as well as some comments about their quality and limitations, are presented, together with the discussion regarding the next steps and some planned improvements.

This paper describes a methodology to assess the energy performance of residential buildings starting from the semantic 3D city model of Vienna. Space heating, domestic hot water and electricity demand are taken into account. The paper deals with aspects related to urban data modelling, with particular attention to the energy-related topics, and with issues related to interactive data exploration/visualisation and management from a plugin-free web-browser, e.g. based on Cesium, a WebGL virtual globe and map engine. While providing references to existing previous works, only some general and introductory information is given about the data collection, harmonisation and integration process necessary to create the CityGML-based 3D city model, which serves as the central information hub for the different applications developed and described more in detail in this paper. The work aims, among the rest, at developing urban decision making and operational optimisation software tools to minimise non-renewable energy use in cities. The results obtained so far, as well as some comments about their quality and limitations, are presented, together with the discussion regarding the next steps and some planned improvements.

There is interest concerning the energy performance of buildings in the United States. Buildings, whether residential, commercial or institutional, generally underperform in terms of energy efficiency when compared to buildings that are constructed following sustainably and energy efficiency standards. A substantial percentage of energy loss in these buildings is associated with the thermal efficiency of its envelope (exterior walls, windows roof, floors and doors). The objective of this study will evaluate the results of three energy modeling techniques developed to investigate the energy transfer through the envelope of existing campus buildings. The techniques employed are solving the heat transfer calculations using spreadsheets, using a stand-alone modeling software (OpenStudio) and using an integrated building energy modeling software (eQuest) employed in Autodesk Revit. The first technique is somewhat different from the other two because it does not require a 3D representation of the building to be generated as the first step in the modeling process. It is the application of a mathematical methodology employing heat transfer algorithms entered into the spreadsheet’s cells to estimate the heat transfer through the building envelope. Data needed for this technique are weather data of the buildings location, surface area of the building envelope, and the overall heat transfer coefficient (U-value) of each component of the building envelope. The OpenStudio technique involves a 3D representation of the building. The building is drawn on a 3D modeling computer program called SketchupPro, which communicates directly to the OpenStudio energy modelling interface. The building operations as well as the building characteristics, such as the composition and type of the elements that made up the building envelop, the thermal zone, occupancy schedule and the space type was inputted in the OpenStudio engine. The OpenStudio engine runs the simulation and generates a detail result about the energy usage and energy transfer in the building. The third method that employs AutoCAD Revit software is a standalone technique that does not require an external software for sketching the building model. Revit the ability to draw the model as well as perform the energy analysis at the same time with the aid of inbuilt eQuest modeling engine. The model in Revit is generated with the right building envelope characteristics as the existing building and the weather file. The process is somewhat similar to the OpenStudio technique; the main difference is the level of detail and limitation provided by both the energy modeling engine (eQuest and EnergyPlus). At the end of the simulation, the building energy modeling using Autodesk Revit presents a detailed result of the energy usage and energy flow in the building. The underlying reason of the comparison of three techniques is to understand the simplest, most efficient, accurate method to quantify heat transfer through the building envelope. By the end of this study, the most efficient technique for investigating the building envelope will be expected to be the EnergyPlus technique because of the usage simplicity, ability to take in a lot of details required for simulation and the periodical software updates.

Judicious modeling of an energy system can help provide insights as to how elements of the energy system might be configured in the longer term. The current and future electricity and water desalination systems of each GCC country were represented using a full-cost based optimization tool called MESSAGE and the following scenarios were examined: 1. The business as usual scenario (BAU): current energy system is extended into the future without any changes. The energy system structure and characteristics are kept the same. The fuel prices are also kept at the current subsidized levels. 2 - The netback-pricing scenario: all fuel costs are increased to the international market price. The freed amount of fuel is assumed to be available for export to the international market. Moreover, this scenario examines different carbon tax options of 0, 20,30 ,40 and 50 dollars per kilo tons of CO2 emissions. 3 - The Nuclear hub scenario: examines the idea of a “nuclear hub” state for the GCC region that can have all the “know-how” and logistics to provide sufficient nuclear energy for the GCC through the Interconnection Grid “GCCIG”. Results shows that fossil fuels will continue to play an important role in a least cost future for the region. This is due, in no small part, to the cheap natural gas resources in the GCC. Despite the high renewable energy technologies potential, their penetration – given the study assumptions - proved to be important, but limited in the GCC. On the other hand, nuclear energy shows clear economic potential.

Kenya is at the forefront of a socioeconomic transformation, aiming to turn into an industrialized middle income country by 2030. Kenya Vision 2030 has identified energy as a key foundation and one of the infrastructural “enablers” upon which the economic, social and political pillars of this long-term development strategy will be built. Predicting the future of energy systems however, involves risks due to various uncertainties. Therefore, systematic energy planning at national and sub-national/County level is highly recommended through the adoption of more realistic assumptions on the future evolution and profile of demand and robust pre-feasibility of prospective projects including the integration of renewable energy sources, which the country is endowed with. This thesis provides a comprehensive analysis of the energy sector for Kakamega County in Western Kenya. The current energy demand level was estimated for six selected sectors of the County namely Residential, Industrial, Transportation, Commercial, Public and Agricultural. Additionally, the renewable energy resources potential was assessed at local level using GIS and other available data. LEAP software was used in order to model and project the energy demand and supply based on three 15-year scenarios till 2030, developed to support the economic, social and environmental sustainability of the County. This study intended to create a framework aiming to facilitate sub-national energy planning in developing countries and it is expected that the findings will be complementary to already existing energy planning models but also the base for future research towards energy poverty elimination.