Computing Research for Sustainability (2012) / Chapter Skim
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2 Elements of a Computer Science Research Agenda for Sustainability
2 Elements of a Computer Science Research Agenda for Sustainability
Pages 51-85
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From page 51... ...
A chief goal of computer science (CS) in sustainability can be viewed as that of informing, supporting, facilitating, and sometimes automating decision making -- decision making which leads to actions that will have significant impacts on achieving sustainability objectives.
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From page 52... ...
All of the steps described above must be done in an iterative fashion. Given that most sustainability challenges involve complex, interacting systems of systems undergoing constant change, all aspects of sensing, modeling, and action need to be refined, revised, or transformed as new information and deeper understandings are gained.
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From page 53... ...
This overlap with established research areas has positive implications -- in particular, the fact that research communities are already established making it unnecessary to develop entirely new areas of investigation. At the same time, the committee believes that there is real opportunity in these areas for significant impacts on global sustainability challenges.
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From page 54... ...
Finally, one can search for the areas in which innovation and the development of fundamentally new computer science techniques, tools, and methodologies are needed to meet sustainability challenges. While endorsing approaches across this spectrum, the committee urges emphasis on solutions that have the potential for significant impacts and urges the avoidance of simply developing or improving technology for its own sake.
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From page 55... ...
for process control and Building Automation and Control Networks (BACnet) for building automation, readings are obtained over a standardized protocol, but their interpretation remains entirely determined by the context, placement, and role of the device in the larger process.
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From page 56... ...
The factors described above have changed the role of instrumentation and measurement from a subsidiary element of the system design process to an integrative, largely independent process of design and provisioning of physical information services. For many sustainability challenges, methodologies are needed that can start with an initial model that is based on modest amounts of data collected during the design process; those methodologies would then include the development of an incremental plan for deploying sensors that progressively improves the model and exploits the improvements to achieve the goals of the system.
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From page 57... ...
Coping with Self-Defining Physical Information Rather than simply drawing its semantics and interpretation from its embedding in a particular system, each physical information service could be used for a variety of purposes outside the context of a particular system and hence should have an unambiguous meaning. The most basic part of this problem is the conversion from readings to physical units and the associated calibration coefficients and correction function.2 The much more significant part of the problem is capturing the context of the observation that determines its meaning.3 For example, in a building environment, supply air, return air, chilled water supply, chilled water return, outside air, mixing valve inputs, economizer points, zone set point, guard band, compressor oil, and refrigerated measurement all have physical units of temperature, but these measurements all have completely 2These aspects have been examined and partially solved over the years with electronic data sheets, such as the IEEE [Institute of Electrical and Electronics Engineers]
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From page 58... ...
To provide these capabilities in general rather than as a result of a design and engineering process for each specific domain or setting, however, requires either significant innovation in the techniques deployed or the development of new techniques. There are, for instance, well- developed techniques for defining the meaning, context, and interpretation of information directly affected by human actions, where these aspects are inherently related to the generation process.4 To cope with many large-scale sustainability challenges, similar capabilities need to be developed for physical or non-human-generated information.
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From page 59... ...
The Design and Capacity Planning of Physical Information Services Once the physical deployment of the instrumentation capability is decoupled from the design and implementation of the enclosing system, many new research questions arise. Each consumer of physical information may require that information at different timescales and levels of resolution.
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From page 60... ...
These tools need to provide the designer with feedback on such things as the marginal benefit of additional sensing and additional network links, the robustness of the design to future information needs, and so forth. In summary, all aspects of capacity planning present in highly engineered systems, such as data centers and massive Internet services, arise in the context of the physical information service infrastructure.
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From page 61... ...
In addition, computer science is widely applied to discretized forms of continuous processes, including computational science simulation and modeling, multimedia, and human-computer interfaces. In both regimes, substantial data mining, inference, and machine learning are employed to extract specific insights from a vast body of often low-grade, partially related information.
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From page 62... ...
Big Data Notions regarding the coming wave of "big data" -- the vast amounts of structured and unstructured data created every day, growing larger than traditional tools can cope with -- and how science in general must cope with it were articulated in The Fourth Paradigm: Data-Intensive Scientific Discovery,8 which posits an emerging scientific approach, driven by data-intensive problems, as the evolutionary step beyond empiricism, analyses, and simulation. Useful data sets of large size or complicated structure exceed today's capacity to search, validate, analyze, visualize, synthesize, store, and curate the information.
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From page 63... ...
For example, the smart grid will grow in terms of complexity and uncertainty, especially as renewables are made a more significant element of the energy mix. This increasing complexity will create an increasingly complex system of equations that will need to be solved on a shrinking timescale in order to create secure and dispatchable energy over larger geographies.
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From page 64... ...
Supervised and unsupervised machine learning techniques, as well as those used for gesture recognition, intrusion detection, and preference characterization, may be applied to infer quantities of interest from 10See http://ebird.org/content/ebird/. 11See http://neoninc.org/budburst/.
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From page 65... ...
As one significant example, many aspects of climate change center on the increase in global surface temperature. Although there is now broad scientific consensus that human activity is a significant contributor to global climate change, there is much continuing debate about the exact nature of the phenomenon and (more importantly)
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From page 66... ...
Coping with Multisource Data Streams Most early machine learning and predictive data-mining tools were designed for the analysis of a single data set under the assumption that the data directly measure the desired input-output relationship; the goal was to learn a mapping from inputs to outputs. Increasingly these techniques 12The Goddard Institute for Space Studies (GISS)
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From page 67... ...
A further challenge raised by these problems is how to validate hidden-variable models. Traditional statistical methods rely on goodness of fit of a highly restricted parametric model; modern machine learning methods rely on having separate holdout data to test the model.
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From page 68... ...
Xs Zs Ysj Wsj j=1,…,J s=1,…,S FIGURE 2.1.1 Probabilistic graphical model representation of the occupancy/detection model. Observed variables are shaded; S is the number of sites; J is the number of visits to each site.
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From page 69... ...
Current practice in machine learning modeling harkens back to the days of batch processing. Each iteration of model development and evaluation takes several days, because the data are so voluminous that the management tools, algorithms, and visualization methods require several hours to run.
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From page 70... ...
. This section discusses three interrelated topics: multiscale models, the combining of mechanistic and statistical models, and optimization under uncertainty.
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best be handled? Combining Statistical and Mechanistic Models The second of three interrelated modeling challenges is that of combining statistical and mechanistic models.
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Many algorithms for evaluating mechanistic models employ adaptive meshes; how can statistical methods be integrated with mesh adaptation? The fitting of statistical models typically requires evaluating the mechanistic model hundreds or thousands of times.
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From page 73... ...
And finally, decisions must be made based not just on expected outcomes, but also on the uncertainty associated with the various alternatives. 16 There are three areas that pose interesting research challenges for computer science with respect to uncertainty: assessment, representation, and propagation of uncertainty; robust-optimization methods; and models of sequential decision making.
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From page 74... ...
Additional work is needed to develop such representations and to provide support for automating the end-to-end assessment of uncertainty. For example, it should be possible to automate end-to-end Monte Carlo uncertainty assessment.
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From page 75... ...
Another research challenge is to develop robust-optimization methods that are applicable to the kinds of complex nonlinear models that arise in sustainability applications. Optimal Sequential Decision Making Most sustainability challenges will not be addressed by a decision made at a single point in time.
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. Recently, approximate dynamic programming methods have been developed in the fields of machine learning and operations research.
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From page 77... ...
This integration would allow the machine learning methods to tailor their predictive accuracy to those regions of time and space that are of greatest importance to the optimization process and could lead to large improvements in the quality of the resulting decisions. Formulating problems in terms of sequential decision making can sometimes make the problems more tractable.
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From page 78... ...
Issues such as human-inthe-loop training of machine learning systems, the interpretability of model results, and the possible use (or abuse) of large volumes of sensed data become particularly salient with a human-centered viewpoint.
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From page 79... ...
Reliably validated carbon reductions, for instance, are important not just to global progress; they would be also invaluable for guiding sustainability efforts at a macro level.27 This report emphasizes opportunities for research, in addition to the data and privacy challenges mentioned earlier, on human-centered systems both at the individual level and beyond (at the organizational and societal levels)
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From page 80... ...
Similarly, online curricula for students in kindergarten through grade 12 and for adults can explore, for instance, ongoing scientific and policy discussions related to sustainability; and educational initiatives can contribute to societal changes needed to meet sustainability goals. In addition to opportunities with respect to tools for engaged citizens generally, there are also promising areas of research in helping scientists provide more effective input into these broader discussions and debates on sustainability and potential initiatives.
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From page 81... ...
However, this raises fundamental research problems ranging from the creation of these sensors to our ability to use the data effectively despite the inherent uncertainties that arise from its production. Design for Sustainability Techniques developed to design for manufacturing, design for mass customization, and user-centric design can expand on the understanding of what it means to design for sustainability.
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From page 82... ...
At the same time, this possibility raises challenging research questions regarding appropriate amounts of information, how to deal with the inherent uncertainties in the data, techniques for evaluating such systems, coupling with other systems on the supply side (e.g., the smart grid) , and important value questions regarding fairness, representativeness, security, and privacy.
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From page 83... ...
One challenge for this line of work is recognizing that there are huge uncertainties about the future and thus also in developing tools and infrastructure that are flexible, adaptable, and appropriate. Using Information from Resource-Usage Sensing Recent work has opened the possibility of providing rich, highly disaggregate information to households, small groups, and organizations regarding resource usage.
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. How can these systems be coupled with smart grid technology on the supply side?
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From page 85... ...
Chapter 3 explores ways of conducting and managing research so that computer science research can have an even greater impact on sustainability challenges. 30For example, Patel and others have developed comparatively lightweight methods to acquire reasonably fine-grained data in homes; see J
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