Crowdsourcing Predictors of Residential Electric Energy Usage

TL;DR

This paper explores how crowdsourcing can generate useful hypotheses and data for predicting residential electric energy consumption, demonstrating the potential of collective input in smart grid modeling.

Contribution

It introduces a novel approach where a crowd both proposes hypotheses and provides data, leading to a predictive model of energy usage in a smart grid context.

Findings

Crowd-generated hypotheses are useful for modeling energy consumption.

A large number of questions and answers can inform predictive models.

Crowdsourcing can complement traditional data collection methods.

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.