Access to clean and affordable energy is a prerequisite for human development. In order to achieve access to sustainable energy for all innovation in rural electrification is needed. Decentralized renewable energy technologies in form of Solar Home Systems and Mini-grids possess the potential of electrifying a large number of rural households which cannot be connected to the national grid with local available energy sources. However, the deployment of Mini-grids is facing barriers such as a lack of private investments. By building on already existing SHSs swarm grids can enable households to trade electricity and use their excess electricity to supply additional loads. Swarm grids as an evolutionary bottom-up approach to electrification can overcome some of the obstacles regular Mini-grids face and play a vital role in improving electricity access. As part of this thesis a model has been developed which allows for simulating the electricity flow including line losses in swarm grids of any size on an hourly basis. The model facilitates the gaining of a better understanding for the impact global parameters (e.g. distance between households) have on the feasibility of swarm grids. A field trip to Bangladesh has been undertaken in order to obtain input data for simulating different cases in the model created. The simulations performed indicate that in a swarm grid the generated excess energy of SHSs which so far is wasted can supply the demand of households without SHS as well as commercial loads such as irrigation pumps. Overall the results point towards swarm grids being an innovation with the potential of improving rural electricity access by building on existing infrastructure.

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

A Powerful Future explores the future electricity demand in Europe until 2050 for the industry, transport, and residential sector. This is done through a bottom-up model capturing the essential parameters for each sector combined with statistics on electricity and energy demand giving two scenarios on future power demand in Europe – High Electrification and Baseline. The electricity demand is built in Excel’s data modelling tool, at the request of Sweco. One aspect of this project also involves challenges when constructing this tool. The resolution will be yearly and economic aspects and feasibility of the electrification have not been investigated in this project. The focus of the project is to capture the most essential activities and technologies affecting the power demand in Europe to be included in the model, and less focus on analyzing each country. The annual results until 2050 for both scenarios show a significant increase in power demand in Europe due to the electrification of the industry and transport sector. The transport sector will reach approximately 550 TWh in Baseline and 600 TWh in High Electrification, while the industrial power demand will reach ~2 000/~2 700 TWh in the Baseline -/High Electrification scenario. These two sectors will account for the biggest increase in power demand while households will have a small increase in power demand. The total modeled annual electricity demand 2050 will be ~5 000/~5 900 TWh in the Baseline -/High Electrification scenario and will be approximately a doubling of the electricity demand 2021. / A Powerful Future utforskar den framtida efterfrågan av el i Europa fram till 2050 för industri, transport och hushållssektorn. Detta görs genom en bottom-up modell som infångar viktiga parametrar för varje sektor kombinerat med historiska data av energi- och elbehov för två olika scenarier för Europa –Baseline och Högelektrifiering. Elbehovet modelleras genom Excels datamodellerings-verktyg, som byggts på Swecos förfrågan. En aspekt i detta projekt involerar utmaningar när ett eget verktyg för detta ska konstrueras. Upplösningen är årlig och ekonomiska aspekter såväl som genomförbarhet har inte undersökts närmare i projektet. Resultat från projektet visar på en signifikant ökning i elbehov i Europa på grund av elektrifiering i industri- och transportsektorn. Transportsektorn kommer kräva circa 600 TWh el i Högelektrifieringsscenariet och 550 TWh i Baselinescenariet, emedan industrisektorns elbehov kommer att nå 2 000/2 700 TWh i Baseline-/Högelektrifieringsscenariet. Dessa två sektorer komma stå för den största ökningen i elbehov emedan hushållssektorn kommer stå för en liten ökning. Det totala elbehovet 2050 har modellerats till 5 000/5 900 TWh i Baseline-/Högelektrifieringsscenariet och är ungefär en fördubbling av elbehovet 2021.

Liquid desiccant air conditioning (LDAC) has received much attention in recent years. This is mainly because LDAC systems are able to control latent loads in a more energy efficient way than conventional air conditioning systems. Although many research studies have been conducted on LDAC technologies, the following gaps in the scientific literature are addressed in this thesis: (1) carryover of desiccant droplets in air streams, (2) direct comparisons between different configurations of LDAC systems, (3) fundamentals of capacity matching in heat-pump LDAC systems, (4) optimal-control strategies for heat-pump LDAC systems, and (5) importance of transients in evaluating the performance of a LDAC system. Items (1) to (4) are addressed using TRNSYS simulations, and item (5) is addressed using data collected from a field test. The use of liquid-to-air membrane energy exchangers (LAMEEs) as dehumidifiers and regenerators in LDAC systems eliminate the desiccant droplets carryover problem in air streams. This is because LAMEE separate the air and solution streams using semi-permeable membranes, which allow the transfer of heat and moisture but do not allow the transfer of the liquid desiccant. A preliminary configuration for a membrane LDAC system, which uses LAMEEs as the dehumidifier and regenerator, is proposed and investigated under fixed operating conditions in this thesis. The influences of key design and operating parameters on the heat and mass transfer performances of the membrane LDAC system are evaluated. Results show that the membrane LDAC technology is able to effectively remove latent loads in applications that the humidity to be controlled. A comprehensive evaluation is conducted in this thesis for the thermal, economic and environmental performances of several configurations of membrane LDAC systems. The solution cooling load is covered using a cooling heat pump in all systems studied, while the solution heating load is covered using one of the following five different heating systems: (1) a gas boiler, (2) a heating heat pump, (3) a solar thermal system with gas boiler backup, (4) a solar thermal system with heat pump backup, and (5) the condenser of the solution cooling heating pump. Each of the membrane LDAC systems studied is evaluated with/without an energy recovery ventilator (ERV) installed in the air handling system. The influence of operating the ERV under balanced/unbalanced operating conditions is studied. It is found that the most economic membrane LDAC system is the one which uses the evaporator and condenser of the same heat pump to cover the solution cooling and heating loads, respectively (i.e. heat-pump membrane LDAC system). No clear guidance was found in the literature for sizing the evaporator and condenser in a heat-pump LDAC system to simultaneously meet the solution cooling and heating loads. When the heating and cooling provided by the heat pump exactly match the heating and cooling requirements of the solution, the system is “capacity matched”. A parametric study is conducted on a heat-pump membrane LDAC system to identify the influence of key operating and design parameters on achieving capacity matching. It is concluded that the solution inlet temperatures to the dehumidifier and regenerator are the most influential parameters on the moisture removal rate, capacity matching and coefficient of performance (COP). Three control strategies are developed for heat-pump membrane LDAC systems, where these strategies meet the latent loads and achieve one of the following three objectives: (1) meet the sensible loads, (2) achieve capacity matching, or (3) optimize the COP. Results show that the COP of a heat-pump LDAC system can be doubled by selecting the right combination of solution inlet temperatures to the regenerator and dehumidifier. The importance of transients in evaluating the performance of a LDAC system is addressed in the thesis using a data collected from a field test on a solar LDAC system. It is found that the sensible, latent and total cooling energy, and the total primary energy consumption of the LDAC system are changed by less than 10% during an entire test day when transients are considered. Thus, it can be concluded that steady-state models are reliable to evaluate the energy performances of LDAC systems.

Simultaneously as the societal trends of urbanization, digitalization and electrification of society are moving at a high speed, the Swedish power grid is undergoing a necessary transition to a renewable energy system. Even though there are difficulties on all grid levels, the lack of capacity in some local grids is among the most present problems and originates from the long lead time of grid expansion as well as the challenges within long-term planning of grids. This thesis aims to improve the understanding of future trends’ impact on grid capacity needs. More specifically, a scenario-based and data driven method, with an accompanying model, is derived to target local capacity challenges. The trends identified to pose impact on the future grid capacity were electrification of different sectors, energy efficiency actions, decentralized energy generation, energy storage solutions, flexibility, smart grids, urbanization and climate. The thesis concludes that the impact of a trend on national level is not simply equal to the impact on a local level. Similarly, a long-term increase of the national electricity consumption does not necessarily worsen local capacity challenges. Furthermore, the developed model in this project shows potential to provide more detailed and accurate information about consumption than currently used methods based on standardized power estimations, which could favor more transparent decision making when dimensioning local grids.

“We emphasize the need to address the challenge of access to sustainable modern energy services for all, in particular for the poor, who are unable to afford these services even when they are available.”  Section 126: The Future We Want (Out Come Document of Rio+20-United Nations Conference on Sustainable Development June 20-22, 2012). The lack of energy access has been identified as a hurdle in achieving the United Nations’ Millennium Development Goals, leading towards the urge to set a goal for universal electrification till 2030. With around 600 million people in Africa without access to electricity, effective and efficient electrification programs and policy framework is required to achieve this goal sustainably. South Africa is an example in the continent for initiating intense electrification programs and policies like “Free Basic Electricity”, increasing its electrification rate from 30% in 1993 to 75% in 2010 and a claimed 82% in 2011. The case of South Africa has been analysed from the perspective of universal electrification in the coming years. The aim was to estimate the un-electrified households for each area of South Africa in order to provide the basis for electrification planning. The idea was to use available electrification statistics with GIS (Geographic Information System) maps for grid lines and identifying the suitability of on-grid or off-grid electrification options, which may help in planning the electrification of these areas in the near future. However, due to lack of readily available data, the present work has been able to estimate the un-electrified households & their possible electrical load. The estimates have been distributed in different income groups for each province and district municipality of South Africa, which can be used for electrification planning at national, provincial and municipal level.  As a result, some simple and useful data parameters have been identified and an estimation methodology has been developed, which may be employed to obtain similar estimates at lower administrative levels i.e. local municipalities and wards. The work can be utilized further and feasible electrification options may be suggested for different areas of South Africa, with the help of GIS maps and data. Depending on the availability of useful data, the data parameters & indicators used in this work will be helpful for planning the electrification for rural households in other places of Africa.

This thesis examines the opportunities towards streamlining energy efficiency in the existing built environment in Sweden. Through this case study, the aim has been to optimise two 20th century buildings, one apartment building and one workshop building, located at different latitudes in the country. The goal has been to reduce the heat energy consumption, increase energy performance and lift the buildings to an energy class that meets the requirements for newly produced buildings, according to the regulations of Boverket. In the simulation software BIM Energy, the buildings have been the subjects of different renovation strategies in order to discover what measures are most energy efficient. An LCC analysis was performed to discover what measure is the most cost efficient in relation to its thermal energy improvements. The existing technical and energy data of each building have been used for the creation of a realistic simulation model as of its present conditions. The study has shown that a combination of renovations strategies generated the best results, and that the more expensive the intervention gets, the higher returns of investment. The study of energy performance showed that the new geographical adjustment factor and primary energy factor established by Boverket has a significant impact on how a building is classified, whereupon the choice of primary energy source can determine whether a building is ranked as passive house or energy thief. Though as standalone measures, when coming to reduction of the energy heat demand, a FTX-system have been proven superior with added external insulation as a solid second.

The UK building stock accounts for about half of all energy consumed in the UK. A large portion of the energy is consumed by nondomestic buildings. Offices and retail are the most energy intensive typologies within the nondomestic building sector, typically accounting for over 50% of the nondomestic buildings’ total energy consumption. Heating, ventilating and air conditioning (HVAC) systems are the largest energy end use in the nondomestic sector, with energy consumption close to 50% of total energy consumption. Different HVAC systems have different energy requirements when responding to the same building heating and cooling demands. On the other hand, building heating and cooling demands depend on various parameters such as building fabrics, glazing ratio, building form, occupancy pattern, and many others. HVAC system energy requirements and building energy demands can be determined by mathematical modelling. A widely accepted approach among building professionals is to use building energy simulation tools such as EnergyPlus, IES, DOE2, etc. which can analyse in detail building energy consumption. However, preparing and running simulations in such tools is usually very complicated, time consuming and costly. Their complexity has been identified as the biggest obstacle. Adequate alternatives to complex building energy simulation tools are regression models which can provide results in an easier and faster way. This research deals with the development of regression models that enable the selection of HVAC systems for office buildings. In addition, the models are able to predict annual heating, cooling and auxiliary energy requirements of different HVAC systems as a function of office building heating and cooling demands. For the first part of the data set development used for the regression analysis, a data set of office building simulation archetypes was developed. The four most typical built forms (open plan sidelit, cellular sidelit, artificially lit open plan and composite sidelit cellular around artificially lit open plan built form) were coupled with five types of building fabric and three levels of glazing ratio. Furthermore, two measures of reducing solar heat gains were considered as well as implementation of daylight control. Also, building orientation was included in the analysis. In total 3840 different office buildings were then further coupled with five different HVAC systems: variable air volume system; constant air volume system; fan coil system with dedicated air; chilled ceiling system with embedded pipes, dedicated air and radiator heating; and chilled ceiling system with exposed aluminium panels, dedicated air and radiator heating. The total number of models simulated in EnergyPlus, in order to develop the input database for regression analysis, was 23,040. The results clearly indicate that it is possible to form a reliable judgement about each different HVAC system’s heating, cooling and auxiliary energy requirements based only on office building heating and cooling demands. High coefficients of determination of the proposed regression models show that HVAC system requirements can be predicted with high accuracy. The lowest coefficient of determination among cooling regression models was 0.94 in the case of the CAV system. HVAC system heating energy requirement regression models had a coefficient of determination above 0.96. The auxiliary energy requirement models had a coefficient of determination above 0.95, except in the case of chilled ceiling systems where the coefficient of determination was around 0.87. This research demonstrates that simplified regression models can be used to provide design decisions for the office building HVAC systems studied. Such models allow more rapid determination of HVAC systems energy requirements without the need for time-consuming (hence expensive) reconfigurations and runs of the simulation program.

In this thesis work, electricty generation modeling of the wind energy -one type of the renewable energy sources- is studied. The wind energy characteristics and the distribution of wind speed in a specific region is also examined. In addition, the power curves of the wind turbines are introduced and the relationship between the wind speed and wind power is explained. The generation characteristics of the wind turbines from various types of producers are also investigated. In this study, the main wind power forecasting methods are presented and the advantages and disadvantages of the methods are analyzed. The physical approaches, statistical methods and the Artificial Neural Network (ANN) methods are introduced. The parameters that affect the capacity factor, the total energy generation and the payback period are examined. In addition, the wind turbine models and their effect on the total energy generation with different wind data from various sites are explained. As a part of this study, a MATLAB-based software about wind speed and energy modelling and payback period calculation has been developed. In order to simplify the calculation process, a Graphical User Interface (GUI) has been designed. In addition, a simple wind energy persistence model for wind power plant operator in the intra-day market has been developed.

The aim of this study is to develop novel forecasting methodologies. The applications of our proposed models lie in two different areas: macroeconomics and energy. Though we consider two very different applications, the common underlying theme of this thesis is to develop novel methodologies that are not only accurate, but are also parsimonious. For macroeconomic time series, we focus on generating forecasts for the US Gross National Product (GNP). The contribution of our study on macroeconomic forecasting lies in proposing a novel nonlinear and nonparametric method, called weighted random analogue prediction (WRAP) method. The out-of-sample forecasting ability of WRAP is evaluated by employing a range of different performance scores, which measure its accuracy in generating both point and density forecasts. We show that WRAP outperforms some of the most commonly used models for forecasting the GNP time series. For energy, we focus on two different applications: (1) Generating accurate short-term forecasts for the total electricity demand (load) for Great Britain. (2) Modelling Irish electricity smart meter data (consumption) for both residential consumers and small and medium-sized enterprises (SMEs), using methods based on kernel density (KD) and conditional kernel density (CKD) estimation. To model load, we propose methods based on a commonly used statistical dimension reduction technique, called singular value decomposition (SVD). Specifically, we propose two novel methods, namely, discount weighted (DW) intraday and DW intraweek SVD-based exponential smoothing methods. We show that the proposed methods are competitive with some of the most commonly used models for load forecasting, and also lead to a substantial reduction in the dimension of the model. The load time series exhibits a prominent intraday, intraweek and intrayear seasonality. However, most existing studies accommodate the ‘double seasonality’ while modelling short-term load, focussing only on the intraday and intraweek seasonal effects. The methods considered in this study accommodate the ‘triple seasonality’ in load, by capturing not only intraday and intraweek seasonal cycles, but also intrayear seasonality. For modelling load, we also propose a novel rule-based approach, with emphasis on special days. The load observed on special days, e.g. public holidays, is substantially lower compared to load observed on normal working days. Special day effects have often been ignored during the modelling process, which leads to large forecast errors on special days, and also on normal working days that lie in the vicinity of special days. The contribution of this study lies in adapting some of the most commonly used seasonal methods to model load for both normal and special days in a coherent and unified framework, using a rule-based approach. We show that the post-sample error across special days for the rule-based methods are less than half, compared to their original counterparts that ignore special day effects. For modelling electricity smart meter data, we investigate a range of different methods based on KD and CKD estimation. Over the coming decade, electricity smart meters are scheduled to replace the conventional electronic meters, in both US and Europe. Future estimates of consumption can help the consumer identify and reduce excess consumption, while such estimates can help the supplier devise innovative tariff strategies. To the best of our knowledge, there are no existing studies which focus on generating density forecasts of electricity consumption from smart meter data. In this study, we evaluate the density, quantile and point forecast accuracy of different methods across one thousand consumption time series, recorded from both residential consumers and SMEs. We show that the KD and CKD methods accommodate the seasonality in consumption, and correctly distinguish weekdays from weekends. For each application, our comprehensive empirical comparison of the existing and proposed methods was undertaken using multiple performance scores. The results show strong potential for the models proposed in this thesis.