Crowdsourcing Predictors of Residential Electric Energy Usage
IEEE Systems Journal, , , 2017
Abstract: Crowdsourcing has been successfully applied in
many domains including astronomy, cryptography and biology.
In order to test its potential for useful application in a Smart Grid
context, this paper investigates the extent to which a crowd can
contribute predictive hypotheses to a model of residential electric
energy consumption. In this experiment, the crowd generated
hypotheses about factors that make one home different from
another in terms of monthly energy usage. To implement this
concept, we deployed a web-based system within which 627
residential electricity customers posed 632 questions that they
thought predictive of energy usage. While this occurred, the
same group provided 110,573 answers to these questions as they
accumulated. Thus users both suggested the hypotheses that drive
a predictive model and provided the data upon which the model
is built. We used the resulting question and answer data to
build a predictive model of monthly electric energy consumption,
using random forest regression. Because of the sparse nature of
the answer data, careful statistical work was needed to ensure
that these models are valid. The results indicate that the crowd
can generate useful hypotheses, despite the sparse nature of the
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).