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The threatening impacts of climate change are driving a global revolution towards cleaner sources of energy. In South Africa, strategies for energy security and emissions reduction are focusing on renewables, wind energy being one of the most promising ones. The construction of wind energy projects has attached limitations in the identification of suitable areas that respect the environment and are technically feasible. Herein, site selection criteria has been grouped into the Site Identification group (SIG), and the Resource & Energy Generation group (R&E). The SIG incorporates technical, environmental and restricted criteria within a spatial frame; while R&E accounts for the wind resource, estimated energy generation and fitting to energy demand profiles under a spatial-temporal frame. The average wind resource is usually found to be analysed together within the technical factors to determine the feasibility of a site; however for this study, a different and independent treatment of the wind resource and its energy generation profile was undertaken. It consists of evaluating the unique hourly wind power profile of each site against the energy consumption profile for the same period. The need is for selecting places with the smallest variation between the electricity produced and the electricity demanded. The Production to Demand Difference (PDD) has been chosen as the indicator of such variations. Therefore, the new purpose is to identify spots where the combination of the PDD and the results from the SIG become smaller with time. The Mean Difference (MD) is also taken into account to obtain further information regarding the trends of the differences. Geoprocessing, overlays and mathematical combinations of datasets are all performed under a GIS environment. / Dissertation (MEng)--University of Pretoria, 2015. / tm2015 / Chemical Engineering / MEng / Unrestricted

The aim of this thesis is to briefly introduce the reader to the problem of development of the electricity consumption, to show the possibilities of its prediction, and provide an example of electricity consumption analysis, which may serve as a basis for long-term forecast. The first part of this work is devoted to brief overview of the development of consumption of electrical energy in the Czech Republic and factors associated with that development. The main events of Czech electrification history are outlined, as well as factors influencing the magnitude of the demand for electricity in the recent times. There are described the possibilities of the influence of foreign exchange and price of the electricity, net losses, GDP, the consumption of gas, the number of inhabitants, or the structure of the economy. This work further describes the development of the consumption of electric energy abroad and comparison of the electricity consumption in the Czech Republic to its neighboring countries, in which there are apparently influence by variety of factors that affect the consumption development trend. For more complex overview, the electricity consumption of most of European states is being noted, and near the end of the chapter the development in poorest countries and in quickly growing economies is shown. The fourth chapter mentions methods for short and middle-term prediction of consumption of electricity. There also is more consistent description of possibilities useful for long-term prediction, for which the use of the results of statistics analysis is possible. The description of observed data that are used to find the dependences of different factors is made in the following part. Further the approach of the statistics analysis used in this thesis is shown, and the key terms are explained. The dates concerning the consumption of Czech Republic are analyzed, followed by the states with similar trend of the consumption and finally other European states. Sixth chapter examines the possible uses of the outcomes of statistical analyses for long-term prediction of electricity consumption. The conclusion sums up the knowledge acquired during the research concerning problem of electricity consumption and my own analysis of data.

Electricity is the most preferred source of energy, because of its quality and convenience of usage. It is probably one of the most vital infrastructural inputs for economic development of a country. Indeed it is the fulcrum which can leverage the future pace of growth and development. These reasons have made the electric power industry one of the fastest growing sectors in most developing countries and particularly in India. Therefore it is not surprising to observe the economic growth of a country being related to the increase in electricity consumption. In India, the growth rate of demand for power is generally higher than that of Gross Domestic Product (GDP). However, to achieve this kind of growth in electricity supply, the capital investments required are very huge. Even though the electricity sector generally gels a major share in the budgetary allocations in India, this is inadequate to add the required quantum of new generation capacity to keep pace with the increase in demand for electricity. Additional constraints like capital scarcity in the public sector, lack of enthusiasm among the private and foreign investors, and strong opposition from the environmentalists have further contributed to this slow pace of new generating capacity addition. This has resulted in severely constrained systems in India. The main focus of the present research work is on the development of an integrated approach for electricity planning using a mathematical modeling framework in (he context of resource constrained systems. There are very few attempts in the literature to integrate short, medium and long term issues in electricity planning. This is understandable from the point of view of unconstrained electricity systems where this type of integration is unnecessary since such systems have a luxury of surplus capacity to meet the current demand and capacity additions are required only for meeting predicted future increase in demand. However, in the case of constrained electricity systems, which are characterized by shortages, this kind of integration is very essential. These systems have to manage with inadequate capacity in the present, plan capacity additions to bridge the existing gap and to meet future increase in demand, and always explore the possibility of adding capacity with short gestation period. The integrated approach is expected to achieve effective supply-demand matching on a continuous basis encompassing both the short term and long term horizons. To achieve this, we have considered three alternatives- existing supply, new supply and non-supply (rationing) of electricity. The electricity system of the state of Karnataka, which is severely constrained by both limited capital and energy resources, has been selected for this purpose. As a first step, the supply and demand situation has been studied in the context of resource constraints. In terms of supply, both existing and future additions are studied in detail with respect to the potential created, generation types, import potential, technical constraints, energy and power shortages, planned and proposed capacity additions by both public and private sectors, etc. The demand patterns have been studied by introducing a new concept of "Representative Load Curves (RLCs)". These RLCs are used to model the temporal and structural variations in demand for electricity. Also, appropriate non-supply options (rationing measures) for effective management of shortages are identified. Incorporating this information, an integrated mathematical model, which is expected to generate a target plan for a detailed generation scheduling exercises and a requirement plan for a regular generation expansion planning, has been developed. The other important alternative "Demand-Side-Management (DSM)", which could be considered as an effective option to achieve efficient supply-demand matching has not been included in the present research work. The major reason for not including the DSM alternatives is due to the difficulty in integrating these in the modelling approach adopted here. In the present approach we have used typical daily load curves (RLCs) to represent the demand for electricity. These are aggregate load curves and do not contain any sector-wise or end-use-wisc details. On the other hand, DSM alternatives are end-use focused. To incorporate DSM alternatives, we should have information on end-usc-wisc power demand (kW or MW), savings potential, time-of-use, etc. For this purpose it may be required to have end-use-wisc daily load curves. This information is not available and a separate detailed survey may be required to generate these load curves. This, we felt, is out of the scope of this present research work and a separate study may be required to do this. Therefore, we restricted our focus to supply planning alone. A detailed literature review is conducted to understand different types of modeling approaches to electricity planning. For the present study, however, the review of literature has been restricted to the methods of generation expansion planning and scheduling. In doing so, we attempted to bring out the differences in various approaches in terms of solution methods adopted, alternatives included and modifications suggested. Also, we briefly reviewed the literature on models for power and energy rationing, because management of shortages is an important aspect of the present study. Subsequently, a separate section is devoted to present an overview of the non-supply of electricity and its economic impacts on the consumers. We found that the low reliability of the electrical system is an indicator of the existence of severe shortages of power and energy, which cause non-supply of electricity to the consumers. The overview also presented a discussion on reasons for non-supply of electricity, and the types of non-supply options the utilities adopt to over come these shortages. We also attempted to explain what we mean by non-supply of electricity, what are its cost implications, and the methods available in the literature to estimate these costs. The first objective of the research pertains to the development of a new approach to model the varying demand for electricity. Using the concept of Representative Load Curves (RLCs) we model the hourly demand for a period of four years, 1993-94, 1994-95, 1995-96 and 1996-97, to understand the demand patterns of both unconstrained and constrained years. Multiple discriminant analysis has been used to cluster the 365 load curves into nine RLCs for each of the four years. The results show that these RLCs adequately model the variations in demand and bring out the distinctions in the demand patterns existed during the unconstrained and constrained years. The demand analysis using RLCs helped to study the differences in demand patterns with and without constraints, impacts of constraints on preferred pattern of electricity consumption, success of non-supply options in both reducing the demand levels and greatly disturbing the electricity usage patterns. Multifactor ANOVA analyses are performed to quantify the statistical significance of the ability of the logically obtained factors in explaining the overall variations in demand. The results of the ANOVA analysis clearly showed that the considered factors accounted for maximum variations in demand at very high significance levels. It also brought out the significant influence of rationing measures in explaining the variations in demand during the constrained years. Concerning the second objective, we explained in detail, the development of an integrated mixed integer-programming model, which we felt is appropriate for planning in the case of resource constrained electricity systems. Two types of integrations are attempted (i) existing supply, non-supply and new supply options for dynamically matching supply and demand, (ii) operational and strategic planning in terms of providing target plans for the former and requirement plans for the latter. Broadly, the approach addresses the effective management of existing capacity, optimal rationing plan to effectively manage shortages and rationally decide on the new capacity additions both to bridge the existing gap between supply and demand, and to meet the future increases in demand. There is also an attempt to arrive at an optimal mix of public and private capacity additions for a given situation. Finally, it has been attempted to verify the possibility of integration of captive generation capacity with the grid. Further, we discussed in detail about the data required for the model implementation. The model is validated through the development of a number of scenarios for the state of Karnataka. The base case scenario analyses are carried out for both the unconstrained and constrained years to compare the optimal allocations with actual allocations that were observed, and to find out how sensitive are the results for any change in the values of various parameters. For the constrained years, a few more scenarios are used to compare the optimal practice of managing shortages with to what has been actually followed by the utility. The optimal allocations of the predicted demand to various existing supply and non-supply options clearly showed that the actual practice, reflected by the actual RLCs, are highly ad hoc and sub-optimal. The unit cost comparisons among different scenarios show that the least cost choice of options by the utility does not necessarily lead to good choices from the consumers’ perspective. Further, a number of future scenarios are developed to verify the ability of the model to achieve the overall objective of supply-demand matching both in the short and long term. For this purpose both the short horizon annual scenarios (1997-98 to 2000-01) and long horizon terminal year scenarios (2005-06 and 2010-11) are developed assuming capacity additions from only public sector. Overall, the results indicated that with marginal contributions from non-supply options and if the public sector generates enough resources to add the required capacity, optimal matching of supply and demand could be achieved. The scenario analyses also showed that it is more economical to have some level of planned rationing compared to having a more reliable system. The quantum of new capacity additions required and the level of investments associated with it clearly indicated the urgent need of private sector participation in capacity additions. Finally, we made an attempt to verify the applicability of the integrated model to analyse the implications of private sector participation in capacity additions. First, a number of scenarios are developed to study the optimal allocations of predicted hourly demand to private capacity under different situations. Secondly, the impacts of privatisation on the public utility and consumers are analysed. Both short term and long term scenarios are developed for this purpose. The results showed the advantage of marginal non-supply of electricity both in terms of achieving overall effective supply-demand matching and economic benefits that could be generated through cost savings. The results also showed the negative impacts of high guarantees offered to the private sector in terms of the opportunity costs of reduced utilization of both the existing and new public capacity. The estimates of unit cost of supply and effective cost of supply facilitated the relative comparison among various scenarios as well as finding out the merits and demerits of guarantees to private sector and non-supply of electricity. The unit cost estimates are also found to be useful in studying the relative increase in electricity prices for consumers on account of privatization, guarantees and reliable supply of electricity. Using the results of scenario analyses, likely generation expansion plans till the year 2010-11 are generated. The analyses have been useful in providing insights into fixing the availability and plant load factors for the private sector capacity. Based on the analysis, the recommended range for plant utilization factor is 72.88 - 80.57%. The estimated generation losses and the associated economic impacts of backing down of existing and new public capacity on account of guarantees offered to private sector are found to be significantly high. The analyses also showed that the backing down might take place mainly during nights and low demand periods of monsoon and winter seasons. Other impacts of privatization that studied are in terms of increased number of alternatives for the utility to buy electricity for distribution and the associated increase in its cost of purchase. Regarding the consumers, the major impact could be in terms of significant increase in expected tariffs. The major contributions of this thesis are summarized as follows: i. An integrated approach to electricity planning that is reported here, is unique in the sense that it considers options available under various alternatives, namely, existing supply, non-supply and new supply. This approach is most suited for severely constrained systems having to manage with both energy and capital resource shortages. ii. The integration of operational and strategic planning with coherent target plans for the former and requirement plans for the latter bridges the prevailing gap in electricity planning approaches. iii. The concept of Representative Load Curves (RLCs), which is introduced here, captures the hourly, daily and seasonal variations in demand. Together, all the RLCs developed for a given year are expected to model the hourly demand patterns of that year. These RLCs are useful for planning in resource constrained electricity systems and in situations where it is required to know the time variations in demand (e.g. supply-demand matching, seasonal scheduling of hydro plants and maintenance scheduling). RLCs are also useful in identifying the factors influencing variations in demand. This approach will overcome the limitations of current method of representation in the form of static and aggregate annual load duration curves. iv. A new term, "non-supply of electricity" has been introduced in this thesis. A brief overview of non-supply presented here includes reasons for non-supply, type of non-supply, methods to estimate cost of non-supply and factors influencing these estimates. v. The integrated mixed integer programming model developed in the study has been demonstrated as a planning tool for- • Optimal hourly and seasonal scheduling of various existing supply, non-supply and new supply options • Estimation of supply shortages on a representative hourly basis using the information on resource constraints • Effectively planning non-supply of electricity through appropriate power/energy rationing methods • Estimation of the need for the new capacity additions both to bridge the existing gap and to take care of increase in future demand levels • Optimal filling of gaps between demand and supply on a representative hourly basis through new supply of electricity • Optimally arriving at the judicious mix of public and private capacity additions • Studying the impacts of private capacity on the existing and new public sector capacity, and on the consumers • Optimally verifying the feasibility of integrating the captive generation with the total system vi. The demand analysis using RLCs helped to bring out the differences in demand patterns with and without constraints, impacts of constraints on preferred pattern of electricity consumption, success of non-supply options in both reducing the demand levels and greatly disturbing the electricity usage patterns. Multifactor ANOVA analyses results showed that the logically obtained factors accounted for maximum variations in demand at very high significance levels. vii. A comparison of optimal (represented by optimal predicted RLCs) and actual (reflected by actual RLCs) practices facilitated by the model showed that the actual practice during constrained years is highly ad hoc and sub-optimal. viii. The results of the scenario analyses showed that it is more economical to have some amount of planned rationing compared to having a more reliable system, which does not allow non-supply of electricity. ix. The scenarios, which analysed the impacts of high guarantees offered to the private sector, showed the negative impacts of these in terms of reduced utilization of both the existing and new public capacity. x. Generation expansion plans till the year 2010-11 are developed using the results of various kinds of scenario analyses. Two groups of year-wise generation expansion plans are generated, one with only public sector capacity additions and the other with private sector participation. xi. The impacts of privatization of capacity additions are studied from the point of view of the utility and consumers in terms of expected increase in cost of purchase of electricity and tariffs. xii. The analyses are also made for developing some insights into fixing the availability and plant load factors for the private capacity. Based on the analysis, the recommended range for plant utilization factor is 72.88 - 80.57%. We believe that the integrated approach presented and the results obtained in this thesis would help utilities (both suppliers and distributors of electricity) and governments in making rational choices in the context of resource constrained systems. The results reported here may also be used towards rationalization of Government policies vis-a-vis tariff structures in the supply of electricity, planning new generation capacity additions and effective rationing of electricity. It is also hoped that the fresh approach adopted in this thesis would attract further investigations in future research on resource constrained systems.

Peak demand is an issue in power supply system when demand exceeds the available capacity. Continuous growth in peak demand increases the risk of power failures, and increases the marginal cost of supply. The contribution of the residential sector to the system peak is quite substantial and has been a subject of discussion internationally. For example, a study done in New Zealand in 2007 attributed about half of system peak load to the residential sector. International research has attributed a significant influence of human behaviour on households energy use. “Demand Response” is a demand side management tool aimed at achieving peak energy demand reduction by eliciting behaviour change. It encompasses energy needs analysis, information provision to customers, behaviour induction, smart metering, and new signalling and feedback concepts. Demand response is far advanced in the industrial and commercial demand sectors. In the residential sector, information barriers and a lack of proper understanding of consumers’ behaviour have impeded the development of effective response strategies and new enabling technologies in the sector. To date, efforts to understanding residential sector behaviour for the purpose of peak demand analysis has been based on pricing mechanism. However, not much is known about the significance of other factors in influencing household customers’ peak electricity demand behaviour. There is a tremendous amount of data that can be analyzed and fed back to the user to influence behaviour. These may include information about energy shortages, supply security and environmental concerns during the peak hours. This research is intended to begin the process of understanding the importance of some of these factors in the arena of peak energy consumption behaviour. Using stated preference survey and focus group discussions, information about household customers’ energy use activities during winter morning and evening peak hours was collected. Data about how customers would modify their usage behaviour when they receive enhanced supply constraint information was also collected. The thesis further explores households’ customer demand response motivation with respect to three factors: cost (price), environment (CO2-intensity) and security (risk of black-outs). Householders were first informed about the relationship between these factors and peak demand. Their responses were analyzed as multi-mode motivation to energy use behaviour change. Overall, the findings suggest that, household customers would be willing to reduce their peak electricity demand when they are given clear and enhanced information. In terms of motivation to reduce demand the results show customers response to the security factor to be on par with the price factor. The Environmental factor also produced a strong response; nearly two-thirds of that of price or security. A generic modelling methodology was developed to estimate the impact of households’ activity demand response on the load curve of the utility using a combination of published literature reviews and resources, and own research work. This modelling methodology was applied in a case study in Halswell, a small neighbourhood in Christchurch, New Zealand, with approximately 400 households. The results show that a program to develop the necessary technology and provide credible information and understandable signals about risks and consequences of peak demand could provide up to about 13% voluntary demand reduction during the morning peak hours and 8% during the evening peak hours.

The offshore wind potential of the Caribbean Sea has barely been exploited. Currently, the offshore wind power industry in Latin America and the Caribbean region is still at very early stages, leaving aside an important resource that otherwise could contribute to satisfy the growing energy demand of the zone. In this study the possibilities arising from a massive exploitation of the wind resource in the Caribbean Sea are assessed. The objective is to investigate if the resources contained in it would be sufficient to satisfy the energy demand of Latin America and the Caribbean, which is foreseen to be about 1900 TWh/year by 2020. To address this question, the “Infinite wind farm” concept is used as a simple way to model the meteorological behaviour and the wind speed in the area. The model is utilized in combination with the bathymetric data of the Caribbean Sea and with a simple economic analysis, to evaluate what the requirements to satisfy the energy demand would be in terms of area, number of turbines, and levelized cost of energy (LCoE). The assessment is performed utilizing different turbine sizes, and inter-turbine separations to find the combination that minimizes the LCoE. It is found that the energy demand of Latin America and the Caribbean could be satisfied using only 125000 km2 (4.5% of the total Caribbean Sea area) of waters shallower than 25m at a cost of 69 €/MWh, if the turbines were separated 6.5D from one another and if they had a rotor diameter of 250m. In that case, 47760 turbines should be installed using only conventional monopile foundations.

Face à des enjeux économiques, politiques et environnementaux, les compagnies électriques développent des outils incitatifs visant à réduire la demande des consommateurs, en particulier lors des périodes de pointe de demande. Chez le client résidentiel, ces outils sont pour l’essentiel de nature tarifaire (tarification dynamique ou horo-saisonnière), ou informative (feedbacks sur la consommation). Lorsqu’un ou plusieurs de ces outils sont employés par les acteurs de la filière électrique, on attend une réaction du consommateur, sous forme d’un effacement, d’un report ou d’une réduction globale de la consommation. C’est ce phénomène de réaction que nous appelons la demand response. Afin d’évaluer l’efficacité de ces dispositifs, les compagnies développent des protocoles d’études, notamment des expérimentations de terrain (pilotes) durant lesquels les offres de la demand response sont implémentées sur un échantillon de population. Ces études donnent lieu à des analyses que nous avons compilées afin de tenter d’en extraire des enseignements généralisables. Nous mettons en avant les limites méthodologiques de ces démarches longues et coûteuses et dont la généralisation des résultats est parfois remise en question. Dans l’optique de proposer un éventuel substitut aux pilotes, nous avons examiné le potentiel des méthodes de l’économie expérimentale pour étudier la demand response. La discipline a pour objet d’évaluer l’efficacité d’institutions comme les marchés, mais aussi d’étudier les comportements et leurs déterminants. Est-il possible d’employer les méthodes de l’économie expérimentale afin d’analyser le comportement du consommateur résidentiel face à des incitations à réduire sa demande en électricité, en particulier lors des périodes de pointe ? L’économie expérimentale peut-elle se substituer aux pilotes de terrain ? L’économie expérimentale a déjà contribué à maintes reprises à la compréhension des marchés de gros de l’électricité. Mais jamais l’aspect « consommateur final » de la demand response n’a été abordé, ni dans son acceptabilité, ni dans son efficacité : c’est là que se situe notre contribution qui alimente cette thèse. Nous avons élaboré un protocole visant à mesurer l’acceptabilité de différentes offres de demand response, et mené une série de sessions expérimentales. Nous avons montré que l’acceptabilité des contrats de tarification de pointe est plus grande pour ces consommateurs que celle des contrats de type « bonus à l’effacement », du fait de l’asymétrie des comportements face aux risques de pertes et de gains probabilistes. De plus, une économie substantielle sur la facture électrique est nécessaire pour déclencher une motivation suffisante pour induire des changements de comportements. Quoi qu’il en soit, les offres tarifaires incitatives sont perçues comme un facteur de stress difficile à intégrer aux contraintes du quotidien de certains foyers. Les résultats montrent que l’économie expérimentale peut apporter des éléments utiles et robustes sur les briques fondamentales des comportements des ménages face à ces offres innovantes. Le type et la granularité des enseignements diffèrent de ceux obtenus par les pilotes. Ces derniers testent des solutions plus abouties industriellement ; ils sont relativement contraints et apportent des réponses en termes de modification de consommations in situ. Il s’est avéré difficile, voire impossible d’encapsuler les routines de consommation électrique des clients au sein du laboratoire. Le lien distendu entre l’instant de l’achat d’électricité et la réception de la facture n’est pas aisément modélisable lors de nos sessions expérimentales. Au-delà des résultats de l’expérimentation, nous montrons que l’économie expérimentale, si elle ne peut constituer un substitut partiel aux pilotes de terrain, peut intervenir en tant que méthodologie exploratoire à l’amont ou en complément de ces derniers, facilitant l’approche préalable des comportements des clients résidentiels. / Facing economic, political and environmental stakes, electricity providers are nowadays developing incentive tools, in order to reduce consumer’s demand, particularly during peak demand periods. For residential customers, these tools can be tariffs (dynamic pricing of time-of-use tariffs), or informative devices or services (feedbacks on historical or real-time consumption, given on various media). They might go along with automatization systems that can help cutting of some electric devices when needed. In order to evaluate the capacity of these settings among their customers, electricity utilities are developing quite a few studies, which are mainly field experiment often called pilots. During these pilots, demand response tools are implemented on a population sample. These long and expensive studies lid to quantitative and qualitative analysis. We have compiled about 40 of them and extract from this survey some generalizable teachings. We have shown what these results were and highlighted pilot programs’ methodological limits. In order to propose a substitute to these heavy experimentations, we assessed the capacity or experimental economics. This relatively new discipline’s objective is to evaluation the efficiency of institutions, like markets, but also to study what animate economic agents’ behaviour, e.g. preferences, beliefs, cognitive biases, willingness to pay… We were also able to elaborate an experimental protocol dedicated to the evaluation of some demand response contracts’ acceptability. The results collected during 14 experimental sessions gave us some innovative clues and insight on these contracts acceptability. But, beyond these results, we have demonstrated that even if experimental economics can’t obviously be a substitute for field experiments, it can represent an interesting exploratory methodology. To sum up the experimental economics can take part of residential customers’ behaviour understanding, performing upstream or in complement of pilot programs.

Factors such as economical development, rapid increase in population and climate change increased electricity demand in Turkey as well as in other countries. Thus, using the correct methods to estimate short, medium and long term electricity demand forms a basis for the countries to develop their energy strategy. In this study, monthly electricity demand of Turkey is estimated. First, the effect of natural gas price and consumption to electricity demand and elasticities are searched with a simple regression model. Although, natural gas is known as a substitute of electricity, natural gas consumption and natural gas over electricity price ratio are found to be nearly inelastic. Second part includes two models and cointegration relation is investigated in nonstationary industry production index, electricity consumption per capita and electricity prices series in the first one. An error correction model is then formed with an additional average temperature variable and 12 months electricity demand is forecasted. In the second one, heating degree-days and cooling degree-days are used instead of the average temperature variable and a new error correction model is formed. The first model performs better than the second one, indicating the seasonality of electricity consumption during a year. The results of both models are also compared with previous studies to investigate the effect of different weather variables.

Residential and light-commercial buildings comprise a significant portion of buildings in the United States. They account for a large fraction of the total amount of energy used in the U.S., and they also represent environments where people spend the majority of their time. Thus, the design, construction, and operation of these buildings and their systems greatly affect energy consumption and exposures to airborne pollutants of both indoor and outdoor origin. However, there remains a need to improve knowledge of some key source and removal mechanisms of indoor and outdoor pollutants in residential and light-commercial buildings, as well as their connections to energy use and peak electricity demand. Several standardized field test methods exist for characterizing energy use and indoor air quality in actual buildings, although few explicitly address residential and light-commercial buildings and they are generally limited in scope. Therefore, the work in this dissertation focuses on improving methods to characterize three particular building components for their impacts on exposures to indoor pollutants and their implications for energy consumption: (1) central forced-air heating and cooling (HAC) systems, (2) HAC filters, and (3) building envelopes. Specifically, the research in this dissertation is grouped to fulfill two primary objectives of developing and applying novel methods to: (1) characterize and evaluate central air-conditioning systems and their filters as pollutant removal devices in residential and light-commercial buildings, and to explore their implications for energy consumption, and (2) characterize and evaluate the ability of two particular outdoor pollutants of concern (ozone and particulate matter) to infiltrate indoors through leaks in building envelopes. The research in this dissertation is divided into four primary investigations that fulfill these two objectives. The first investigation (Investigation 1a) addresses Objective 1 by first providing a detailed characterization of a variety of operational characteristics measured in a sample of 17 existing central HAC systems in occupied residential and light-commercial buildings in Austin, Texas, and exploring their implications for exposure to indoor pollutants, energy use, and peak electricity demand. Among the findings in this study, central air-conditioning systems in occupied residential and light-commercial buildings did not operate most of the time, even in the hot and humid climate of Austin, Texas (i.e., ~25% of the time on average in the summer). However, average recirculation rates still make central air-conditioning systems competitive as particle removal mechanisms, given sufficient filtration efficiency. Additionally, this investigation used a larger, much broader, dataset of energy audits performed on nearly 5000 single-family homes in Austin to explore common inefficiencies in the building stock. Residential and light-commercial air-conditioning systems are often inefficient; in fact, residential central air-conditioning systems in particular likely account for nearly 20% of peak electric demand in the City of Austin. As much as 8% of peak demand could be saved by upgrading all single-family homes in Austin to higher-efficiency equipment. The second investigation (Investigation 1b) also addresses Objective 1 by developing and applying a novel test method for measuring the in-situ particle removal efficiency of HAC systems and filters in residential and light-commercial buildings. Results from the novel test method as performed with three test filters and 0.3–10 μm particles in an unoccupied test house agreed reasonably well with results from other field and laboratory test methods. Low-efficiency filters did not increase particle removal much more than simply running the HAC system without a filter, and higher-efficiency filters provided greater than ~50% removal efficiency for most particles greater than 1–2 μm in diameter. The benefit of this test method is that it can be used to measure how filters perform in actual environments, how filter removal efficiency changes with actual dust loading, and how much common HAC design and installation issues, such as low airflow rates, duct leakage, fouled coils, and filter bypass airflow, impact particle removal in real environments. The third investigation (Investigation 2a) addresses Objective 2 by developing and applying a novel test methodology for measuring the penetration of outdoor ozone, a reactive gas, through leaks in exterior building envelopes using a sample of 8 single-family residences in Austin, Texas. These measurements represent the first ever measurements of ozone penetration factors through building envelopes of which I am aware, and penetration factors were lower than the usual assumption of unity (i.e., P = 1) in seven of the eight test homes (ranging from 0.62±0.09 to 1.02±0.15), meaning that some building envelopes provide occupants with more protection from indoor exposures to ozone and ozone reaction byproducts than others. Additionally, ozone penetration factors were correlated with some building characteristics, including the amount of painted wood siding on the exterior envelope and the year of construction, suggesting that simple building details may be used to predict ozone infiltration into homes. Finally, the fourth investigation (Investigation 2b) also addresses Objective 2 by refining and applying a test methodology for measuring the penetration of ambient particulate matter through leaks in building envelopes, and using a sample of 19 single-family residences in Austin, Texas to explore correlations between experimentally-determined particle penetration factors and standardized fan pressurization air leakage tests. Penetration factors of particles 20–1000 nm in diameter ranged from 0.17±0.03 to 0.72±0.08 across 19 homes that relied solely on infiltration for ventilation air. Particle penetration factors were also significantly correlated with results from standardized fan pressurization (i.e., blower door) air leakage tests and the year of construction, suggesting that occupants of older and leakier homes are exposed to more particulate matter of outdoor origin than those in newer tighter homes. Additionally, blower door tests may actually offer some predictive ability of particle penetration factors in single-family homes, which could allow for vast improvements in making easier population exposure estimates. Overall, the work in this dissertation provides new methods and data for assessing the impacts of central air-conditioning systems, filters, and building envelopes on human exposure to indoor pollutants and energy use in residential and light-commercial buildings. Results from these four primary investigations will allow building scientists, modelers, system designers, policymakers, and health scientists to make better informed decisions and assumptions about source and removal mechanisms of indoor pollutants and their impacts on building energy consumption and peak electricity demand. / text