Title: Crowdsourcing Predictors of Residential Electric Energy Usage,
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 dataset.