Improving Building Sustainability: Lighting Life Cycle Optimization and Management, and HVAC Demand Response

Residential and commercial buildings represent 39% of global energy carbon emissions. In the U.S., buildings consume 40% of the total energy consumption and thus represent a substantial energy saving opportunity. Additionally, building energy flexibility, or the ability to reduce or move demand to a different time, is playing an increasingly important role in grid modernization and renewable integration by helping to balance supply. Material efficiency is another foundation to sustainability, as many energy-efficient and renewable technologies depend on the use of specialty materials, which are dwindling in supply and many face geopolitical conflicts. This dissertation advances methods of life cycle analysis and data analytics while addressing some of these issues and opportunities in three key aspects – how to choose better products, how to better manage products at their end of life, and how to use energy more effectively. Chapter 2 and 3 examine the keep vs. replace conundrum by studying the replacement of residential and commercial lighting, in which the rapidly changing LED technology creates unclear tradeoffs with incumbent lighting in terms of cost, energy savings, and emissions. The results suggest that while LED lighting offers competitive performance and life cycle cost as fluorescent lighting, there is less advantage (or benefit) for immediate LED adoption in a lower use, upfront cost-sensitive, or slowly decarbonizing grid situation. Chapter 4 evaluates the life cycle impacts of recovering rare earth and critical metals from spent linear fluorescent and LED fixtures, respectively. This chapter also assesses the impacts of extended use and modular (component) replacement to assess the value of reverse logistics (reuse, remanufacturing, and recycling). The results show that both types of metal extraction create net environmental impacts, which can be mitigated with process optimization and waste preprocessing to increase extraction efficiency. While modular replacement leads to overall lower environmental burdens, full replacement can offer incentive for LED recycling as their metal-heavy housing structure and heat sink are attractive to recyclers. Chapter 5 performs piecewise log-linear-Fourier regressions on whole-home smart meter data and outdoor temperature data to disaggregate the thermostatically controlled loads from whole-home consumption and to estimate the technical thermal demand response potentials in the Midwest. The results suggest that single family buildings, being the higher energy users and larger customer base than multi-family, can provide higher per customer and aggregated demand flexibility. However, multi-family buildings, particularly those with a central HVAC system, may have the advantage of pooled demand across multiple units and should therefore be considered accordingly. By examining the three decision-making questions related to technology and product selection (Chapter 2 - 3), waste management and material recovery (Chapter 4), and energy use and demand response (Chapter 5), the research helps inform decision making for building managers and energy consumers, and provide industry with insights regarding product design, reverse logistics, and demand response program recruitment.PHDMech Eng & Nat Res Env PhDUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163086/1/lixiliu_1.pd

Assessment of applications of optimisation to building design and energy modelling

Buildings account for around 35% of the world’s carbon emissions and strategies to reduce carbon emissions have made much use of building energy modelling. Optimisation techniques promise new ways of achieving the most cost effective and efficient solutions more quickly and with less input from engineers and building physicists. However, there is limited research into the practical applications of these techniques to building design practice. This thesis presents the results of case-based research into the practical application of design stage optimisation and calibration methods to energy efficient building fabric and services design using building energy modelling. The application during early stage design of a Non-dominating Sorting Genetic Algorithm 2 (NSGA2) to a building energy model EnergyPlusTM. The exercise was used to determine if the application of NSGA2 yielded a significant improvement in the selection of building services technology and building fabric elements. The use of NSGA2 enabled significant (£400,000) capital cost savings without degrading the comfort or energy performance. The potential capital cost savings significantly outweighed the cost of the engineering time required to carry out the additional analysis. Three optimisation techniques were applied to three case study buildings to select appropriate model parameters to minimise the difference between modelled and measured parameters and hence calibrate the model. An heuristic approach was applied to the Institute for Life Sciences Building 1 (ILS1) at Swansea University. Latin Hypercube Monte Carlo (LHMC) was applied to the Arup building at 8 Fitzroy St London and compared directly with the results from an approach using Self Adaptive Differential Evolution (SADE). Poor Building Management System data quality was found to significantly limit the potential to calibrate models. Where robust data was available it was however found to be possible to calibrate EnergyPlus simulations of complex real world buildings using LHMC and SADE methods at levels close to that required by professional bodies

A triage approach to streamline environmental footprinting : a case study for liquid crystal displays

Thesis (S.M. in Technology and Policy)--Massachusetts Institute of Technology, Engineering Systems Division, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 64-69).Quantitative environmental performance evaluation methods are desired given the growing certification and labeling landscape for consumer goods. Challenges associated with existing methods, such as life cycle assessment (LCA), may be prohibitive for complex goods such as information technology (IT). Conventional LCA is resource-intensive and lacks harmonized guidance for incorporating uncertainty. Current methods to streamline LCA may amplify uncertainty, undermining robustness. Despite high uncertainty, effective and efficient streamlining approaches may be possible. A methodology is proposed to identify high-impact activities within the life cycle of a specific product class for a streamlined assessment with a high degree of inherent uncertainty. First, a screening assessment is performed using Monte Carlo simulations, applying existing activity (materials and processes), impact, and uncertainty data, to identify elements with the most leverage to reduce overall environmental impact uncertainty. This data triage is informed by sensitivity analysis parameters produced by the simulations. Targeted data collection is carried out for key activities until overall uncertainty is reduced to the point where a product classes' impact probability distribution is distinct from others within a specified error rate. In this thesis, we find that triage and prioritization are possible despite high uncertainty. The methodology was applied to the case study of liquid crystal display (LCD) classes, producing a clear hierarchy of data importance to reduce uncertainty of the overall impact result. Specific data collection was only required for a subset of processes and activities (22 out of about 50) to enable discrimination of LCDs with a low error rate (9%). Most of these priority activities relate to manufacturing and use phases. The number of priority activities targeted may be balanced with the level to which they are able to be specified. It was found that ostensible product attributes alone are insufficient to discriminate with low error, even at high levels of specificity. This quantitative streamlining method is ideal for complex products for which there is great uncertainty in data collection and modeling. This application of this method may inform early product design decisions and enable harmonization of standardization efforts.by Melissa Lee Zgola.S.M.in Technology and Polic

Life Cycle & Technoeconomic Modeling

This book aims to perform an impartial analysis to evaluate the implications of the environmental costs and impacts of a wide range of technologies and energy strategies. This information is intended to be used to support decision-making by groups, including researchers, industry, regulators, and policy-makers. Life cycle assessment (LCA) and technoeconomic analysis can be applied to a wide variety of technologies and energy strategies, both established and emerging. LCA is a method used to evaluate the possible environmental impacts of a product, material, process, or activity. It assesses the environmental impact throughout the life cycle of a system, from the acquisition of materials to the manufacture, use, and final disposal of a product. Technoeconomic analysis refers to cost evaluations, including production cost and life cycle cost. Often, in order to carry out technoeconomic analysis, researchers are required to obtain data on the performance of new technologies that operate on a very small scale in order to subsequently design configurations on a commercial scale and estimate the costs of such expansions. The results of the developed models help identify possible market applications and provide an estimate of long-term impacts. These methods, together with other forms of decision analysis, are very useful in the development and improvement of energy objectives, since they will serve to compare different decisions, evaluating their political and economic feasibility and providing guidance on potential financial and technological risks

Prognostics and health management of light emitting diodes

Prognostics is an engineering process of diagnosing, predicting the remaining useful life and estimating the reliability of systems and products. Prognostics and Health Management (PHM) has emerged in the last decade as one of the most efficient approaches in failure prevention, reliability estimation and remaining useful life predictions of various engineering systems and products. Light Emitting Diodes (LEDs) are optoelectronic micro-devices that are now replacing traditional incandescent and fluorescent lighting, as they have many advantages including higher reliability, greater energy efficiency, long life time and faster switching speed. Even though LEDs have high reliability and long life time, manufacturers and lighting systems designers still need to assess the reliability of LED lighting systems and the failures in the LED. This research provides both experimental and theoretical results that demonstrate the use of prognostics and health monitoring techniques for high power LEDs subjected to harsh operating conditions. Data driven, model driven and fusion prognostics approaches are developed to monitor and identify LED failures, based on the requirement for the light output power. The approaches adopted in this work are validated and can be used to assess the life of an LED lighting system after their deployment based on the power of the light output emitted. The data driven techniques are only based on monitoring selected operational and performance indicators using sensors whereas the model driven technique is based on sensor data as well as on a developed empirical model. Fusion approach is also developed using the data driven and the model driven approaches to the LED. Real-time implementation of developed approaches are also investigated and discussed

Sustainable Building and Indoor Air Quality

This Special Issue addresses a topic of great contemporary relevance; in developed countries, most of peoples’ time is spent indoors and, depending on each person, the presence in the home ranges from 60% to 90% of the day, and 30% of that time is spent sleeping. Taking into account these data, indoor residential environments have a direct influence on human health. In addition to this, in developing countries, significant levels of indoor pollution make housing unsafe, with a detrimental impact on the health of inhabitants. Housing is therefore a key health factor for people all over the world, and various parameters such as air quality, ventilation, hygrothermal comfort, lighting, physical environment, and building efficiency, among others, can contribute to healthy architecture, and the conditions that can result from the poor application of these parameters

Intelligent Decision Support System for Energy Management in Demand Response Programs and Residential and Industrial Sectors of the Smart Grid

This PhD thesis addresses the complexity of the energy efficiency control problem in residential and industrial customers of Smart electrical Grid, and examines the main factors that affect energy demand, and proposes an intelligent decision support system for applications of demand response. A multi criteria decision making algorithm is combined with a combinatorial optimization technique to assist energy managers to decide whether to participate in demand response programs or obtain energy from distributed energy resources

Energy Efficiency and Indoor Environment Quality

This Special Issue addresses a topic of great relevance. In developed countries, there is a higher prevalence of people choosing to spend time indoors. Data show that the time a person spends at home ranges from 60% to 90% of the day, and 30% of that time is spent sleeping, though this varies depending on the individual. Taking into account these data, indoor residential environments have a direct influence on human health. Furthermore, in developing countries, significant levels of indoor pollution make housing unsafe, impacting the health of its inhabitants. Housing is therefore a key health factor for people all over the world: various parameters such as air quality, ventilation, hygrothermal comfort, lighting, physical environment, and building efficiency can contribute to healthy architecture; poor application of these parameters can result in conditions that negatively impact health