Platform energy consumption and responsiveness are two major considerations for mobile systems since they determine the battery life and user satisfaction, respectively. In this work, the models for power consumption, response time, and energy consumption of heterogeneous mobile platforms are presented. Then, these models are used to optimize the energy consumption of baseline platforms under power, response time, and temperature constraints with and without introducing new resources. It is shown, the optimal design choices depend on dynamic power management algorithm, and adding new resources is more energy efficient than scaling existing resources alone. The framework is verified through actual experiments on Qualcomm Snapdragon 800 based tablet MDP/T. Furthermore, usage of the framework at both design and runtime optimization is also presented.
This thesis attempts to achieve the research objectives by examining the LEED certified buildings on the Arizona State University (ASU) campus in Tempe, AZ, from two complementary perspectives: the Macro-level and the Micro-level. Heating, cooling, and electricity data were collected from the LEED-certified buildings on campus, and their energy use intensity was calculated in order to investigate the buildings' actual energy performance. Additionally, IEQ occupant satisfaction surveys were used to investigate users' satisfaction with the space layout, space furniture, thermal comfort, indoor air quality, lighting level, acoustic quality, water efficiency, cleanliness and maintenance of the facilities they occupy.
From a Macro-level perspective, the results suggest ASU LEED buildings consume less energy than regional counterparts, and exhibit higher occupant satisfaction than national counterparts. The occupant satisfaction results are in line with the literature on LEED buildings, whereas the energy results contribute to the inconclusive body of knowledge on energy performance improvements linked to LEED certification. From a Micro-level perspective, data analysis suggest an inconsistency between the LEED points earned for the Energy & Atmosphere and IEQ categories, on one hand, and the respective levels of energy consumption and occupant satisfaction on the other hand. Accordingly, this study showcases the variation in the performance results when approached from different perspectives. This contribution highlights the need to consider the Macro-level and Micro-level assessments in tandem, and assess LEED building performance from these two distinct but complementary perspectives in order to develop a more comprehensive understanding of the actual building performance.
This paper focuses on the factors influencing Energy Management Behaviour (EMB) at the individual level. By reviewing academic literature, conducting surveys in Beijing, Shanghai and Guangzhou, the author builds an integrated behavioural energy management model of the Chinese energy consumers. This paper takes the vague term of EMB and redefines it as a function of two separate behavioural concepts: Energy Management Intention (EMI), and the traditional Energy Saving Intention (ESI).
Secondly, the author conducts statistical analyses on these two behavioural concepts. EMI is the main driver behind an individual’s EMB. EMI is affected by Behavioural Attitudes, Subjective Norms, and Perceived Behavioural Control (PBC). Among these three key factors, PBC exerts the strongest influence. This implies that the promotion of the energy management concept is mainly driven by good application user experience (UX). The traditional ESI also demonstrates positive influence on EMB, but its impact is weaker than the impacts arising under EMI’s three factors. In other words, the government and manufacturers may not be able to change an individual's energy management behaviour if they rely solely on their traditional promotion strategies. In addition, the study finds that the government may achieve better promotional results by launching subsidies to the manufacturers of these kinds of applications and smart appliances.
The dissertation utilizes Interval Data (ID) and establishes three different frameworks to identify electricity losses, predict electricity consumption and detect anomalies using data mining, deep learning, and mathematical models. The process of energy analytics integrates with the computational science and contributes to several objectives which are to
1. Develop a framework to identify both technical and non-technical losses using clustering and semi-supervised learning techniques.
2. Develop an integrated framework to predict electricity consumption using wavelet based data transformation model and deep learning algorithms.
3. Develop a framework to detect anomalies using ensemble empirical mode decomposition and isolation forest algorithms.
With a thorough research background, the first phase details on performing data analytics on the demand-supply database to determine the potential energy loss reduction potentials. Data preprocessing and electricity prediction framework in the second phase integrates mathematical models and deep learning algorithms to accurately predict consumption. The third phase employs data decomposition model and data mining techniques to detect the anomalies of institutional buildings.
Vehicle trips presently account for approximately 50% of San Francisco’s greenhouse gas emissions (San Francisco County Transportation Authority, 2008). City and county officials have developed aggressive strategies for the future of passenger transportation in the metropolitan area, and are determined to move away from a “business as usual” future. This project starts with current-state source data from a life-cycle comparison of urban transportation systems (Chester, Horvath, & Madanat, 2010), and carries the inventoried emissions and energy usage through by way of published future scenarios for San Francisco.
From the extrapolated calculations of future emissions/energy, the implied mix of transportation modes can be backed out of the numbers. Five scenarios are evaluated, from “business as usual” through very ambitious “healthy environment” goals. The results show that when planners and policymakers craft specific goals or strategies for a location or government, those targets, even if met, are unlikely to result in the intended physical outcomes. City and state governments would be wise to support broad strategy goals (like 20% GHG reduction) with prioritized specifics that can inform real projects leading to the goals (for instance, add 5 miles of bike path per year through 2020, or remove 5 parking garages and replace them with transit depots). While these results should not be used as predictions or forecasts, they can inform the crafters of future transportation policy as an opportunity for improvement or a cautionary tale.
This LCA used data from a previous LCA done by Chester and Horvath (2012) on the proposed California High Speed Rail, and furthered the LCA to look into potential changes that can be made to the proposed CAHSR to be more resilient to climate change. This LCA focused on the energy, cost, and GHG emissions associated with raising the track, adding fly ash to the concrete mixture in place of a percentage of cement, and running the HSR on solar electricity rather than the current electricity mix. Data was collected from a variety of sources including other LCAs, research studies, feasibility studies, and project information from companies, agencies, and researchers in order to determine what the cost, energy requirements, and associated GHG emissions would be for each of these changes. This data was then used to calculate results of cost, energy, and GHG emissions for the three different changes. The results show that the greatest source of cost is the raised track (Design/Construction Phase), and the greatest source of GHG emissions is the concrete (also Design/Construction Phase).
The ultimate goal of this LCA is to give Arizona State University specific advice on possible changes in lighting systems that will reduce environmental impacts and support ASU’s sustainability efforts. The aim is to assess the potential for a decrease in specific environmental impacts (CO2 emissions and energy use) and economic impact (cost) from changing to a different type of lighting in a prototypical classroom in Wrigley Hall. The scope of this assessment is to analyze the impacts of T8 lamps lasting 50,000 hours. Thus, a functional unit was defined as 50,000 hours of use, maintaining roughly 825 lumens. To put this in perspective, 50,000 hours is equivalent to 8 hours of use per day, 365 days per year, for approximately 17.1 years.