Urban energy systems are often studied in a very similar way in the sense that the characteristics of the underlying physical infrastructure are weighted as the main determinants of energy use predictions, while the behavior of the human population in relation to this systemthe so-called ``energy consumers''in time and urban spaces is effectively neglected. The spatial and temporal variations in infrastructure-population interactivity greatly complicate urban energy systems; the unremitting growth in population and advances in technology mean that the dynamic interrelationship between the population and urban environment will continue to grow exponentially, resulting in increasing uncertainties, unreliable predictions and poor management decisions given the inadequacy of existing approaches. In this dissertation, I explore the interdependencies of spatiotemporal fluctuations of human mobility as an indicator for human activities and energy use in urban areas in three main studies. First, I show that the fluctuations of intra-urban human mobility and energy use have an underlying structure across both time and space, and that human mobility can indeed be used as a predictor for energy use in both dimensions. Second, I examine how one of the dominant drivers of this structure, namely individuals' location-based activities, influence patterns in energy supply and demand across building types (i.e. residential and commercial buildings) and show how variations in the human mobility networks of two distinct urban populations (the so-called returners and explorers) can explain fluctuations in energy use. Third, I introduce an integrated approach for predicting urban energy use across time and space by incorporating these interdependencies. Generating predictive models that capture the spatiotemporal variations in these determinants in urban settings, as suggested in this research, will contribute to our understanding of how variations in urban population activities for particular times and locations influence can be applied to estimate energy use patterns in surrounding areas. / PHD / Today’s cities are the most complex built environments in human history, containing 54% of the world population and responsible for up to 80% of the world’s total energy consumption. As a result of population growth and advances in technology, the interdependencies between infrastructure, services, and individuals in urban spaces continue to increase, presaging an ambiguous future with challenges we are not yet aware of. In this research, I developed the concept of <i>urban spatiotemporal flux</i> to study the interdependencies between energy use and human activities using human mobility at various spatial and temporal scales to address the urgent need to incorporate the resulting fluctuations in energy use into future energy predictions. Intra-city human activities change more rapidly and exhibit higher levels of dynamic characteristics than the simple physical locations identified in current master plans. Previous research has tended to focus on predicting energy consumption at different spatial levels as a function of the physical characteristics of buildings or cities, often relying on sensor-based data-driven approaches. There has been some effort to explore the predictability of human mobility by building human mobility-based predictive models across applications such as traffic and travel demand predictions, human activity predictions, next place locations, epidemics and the spread of viruses, and air pollution. The two perspectives are rarely in conversation with each other, however, with only minimal integration of our understanding and predictions for different urban spatial and temporal scales. The technology that has become an integral part of everyday life in today’s smarter urban environments now allows us to use human beings as “sensors” that provide useful data for predictions of energy use. Using tens of millions of yearly individual positional records across thousands of spatial divisions, along with millions of corresponding measures of energy use from energy meters in Greater London and the City of Chicago, I discovered that fluctuations in urban energy consumption are likely governed by the structure of human mobility networks and are dominated by certain populations and buildings types, among other factors. Intra-urban human mobility and energy use are not spatially randomly distributed across urban settings; instead, there is an underlying structure that explains their dependency. Temporal manifestations of these fluctuations suggest a continuous spatiotemporal relationship between human mobility and energy use, which confirms that the values observed in one location depend to some extent on what is happening at adjacent locations at around the same time. This dependency represents a strong connection with the returner populations’ mobility and residential buildings’ energy use and there is an associated spatial spillover effect. Future energy efficiency strategies should thus reflect these spatiotemporal dependencies, enabling planners to create new and more effective ways for both different building types and the mobility networks of the urban population to play major roles in energy related strategies, as well as helping to identify the fluctuating determinants that represent additional evidence of a spatiotemporal structure.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/89611 |
| Date | 30 November 2016 |
| Creators | Mohammadi, Neda |
| Contributors | Civil Engineering, Taylor, John E., Dewulf, Geert, de la Garza, Jesus M., Garvin, Michael J. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
Page generated in 0.0023 seconds