Civil & Environmental Engineering
Permanent URI for this communityhttp://hdl.handle.net/1903/2221
Browse
2 results
Search Results
Item OCCUPANT BEHAVIOR IN BUILDING ENERGY MANAGEMENT: BEHAVIORAL CHARACTERIZATION, INTERVENTION AND FORECASTING(2018) Lu, Yujie; Cui, Qingbin; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)With the advent of the climate change and global warming, there is a need to adopt a diversified approach to address climate change; this is especially the case of promoting building energy conservation. This dissertation is one of the first studies that focuses on the occupant behavior in the building energy conservation, in particular three dimensions. First, this study aims to propose a behavior-based model that investigates impact of renters’ rebound effect on building retrofit saving amount and to design the shared saving scheme among major stakeholders during their decision-making process. With demonstration of a real retrofitting project in a university campus, the rebound effect was identified to significantly extend the payback period of retrofit contracts and such the prolonged duration is partially determined by renters’ risk attitudes towards monetary incentives. Second, the study compares two message delivering means, paper-based (e.g. stickers) versus instant messaging tool (e.g. WeChat), as a platform for sharing energy-saving information and promoting occupant energy conservation in China. It was found that WeChat is the most effective intervention in reducing energy consumption, but the effects are short-lived. Using stickers, comparatively, produces more sustained results with long-term engagement of households. The changes in certain occupant energy behaviors are also correlated with individuals’ perception of responsibility and quality of life to explain the heterogeneity of individual behaviors. Third, the study examines the interaction effect between occupant personality, energy behavior and intervention strategies with algorithms that can identify the optimal intervention strategy tailored for each household. This is followed by an improved Support Vector Regression (SVR) model that is capable of predicting household electricity consumption under optimal intervention strategies according to occupant behavior and personality traits. The proposed intervention lead to an average reduction of 12.1% in monthly household energy consumption compared with conventional behavioral interventions. The methods and algorithms developed from this study are pioneer works providing implications to measure the influence of occupant behaviors on energy saving amounts, to enrich and diversify behavioral intervention strategies, and to design incentives, programs and policies that effectively regulate occupant behaviors, collectively contributing to the demand-side energy management in buildings.Item Multi-level, Multi-stage and Stochastic Optimization Models for Energy Conservation in Buildings for Federal, State and Local Agencies(2016) Champion, Billy Ray; Gabriel, Steven A; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Energy Conservation Measure (ECM) project selection is made difficult given real-world constraints, limited resources to implement savings retrofits, various suppliers in the market and project financing alternatives. Many of these energy efficient retrofit projects should be viewed as a series of investments with annual returns for these traditionally risk-averse agencies. Given a list of ECMs available, federal, state and local agencies must determine how to implement projects at lowest costs. The most common methods of implementation planning are suboptimal relative to cost. Federal, state and local agencies can obtain greater returns on their energy conservation investment over traditional methods, regardless of the implementing organization. This dissertation outlines several approaches to improve the traditional energy conservations models. Any public buildings in regions with similar energy conservation goals in the United States or internationally can also benefit greatly from this research. Additionally, many private owners of buildings are under mandates to conserve energy e.g., Local Law 85 of the New York City Energy Conservation Code requires any building, public or private, to meet the most current energy code for any alteration or renovation. Thus, both public and private stakeholders can benefit from this research. The research in this dissertation advances and presents models that decision-makers can use to optimize the selection of ECM projects with respect to the total cost of implementation. A practical application of a two-level mathematical program with equilibrium constraints (MPEC) improves the current best practice for agencies concerned with making the most cost-effective selection leveraging energy services companies or utilities. The two-level model maximizes savings to the agency and profit to the energy services companies (Chapter 2). An additional model presented leverages a single congressional appropriation to implement ECM projects (Chapter 3). Returns from implemented ECM projects are used to fund additional ECM projects. In these cases, fluctuations in energy costs and uncertainty in the estimated savings severely influence ECM project selection and the amount of the appropriation requested. A risk aversion method proposed imposes a minimum on the number of “of projects completed in each stage. A comparative method using Conditional Value at Risk is analyzed. Time consistency was addressed in this chapter. This work demonstrates how a risk-based, stochastic, multi-stage model with binary decision variables at each stage provides a much more accurate estimate for planning than the agency’s traditional approach and deterministic models. Finally, in Chapter 4, a rolling-horizon model allows for subadditivity and superadditivity of the energy savings to simulate interactive effects between ECM projects. The approach makes use of inequalities (McCormick, 1976) to re-express constraints that involve the product of binary variables with an exact linearization (related to the convex hull of those constraints). This model additionally shows the benefits of learning between stages while remaining consistent with the single congressional appropriations framework.