AAAI Publications, Workshops at the Thirtieth AAAI Conference on Artificial Intelligence

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Cost-Effective Feature Selection and Ordering for Personalized Energy Estimates
Kirstin Early, Stephen Fienberg, Jennifer Mankoff

Last modified: 2016-03-29

Abstract


Selecting homes with energy-efficient infrastructure is important for renters, because infrastructure influences energy consumption more than in-home behavior.Personalized energy estimates can guide prospective tenants toward energy-efficient homes, but this information is not readily available. Utility estimates are not typically offered to house-hunters, and existing technologies like carbon calculators require users to answer (prohibitively) many questions that may require considerable research to answer. For the task of providing personalized utility estimates to prospective tenants, we present a cost-based model for feature selection at training time, where all features are available and costs assigned to each feature reflect the difficulty of acquisition. At test time, we have immediate access to some features but others are difficult to acquire (costly). In this limited-information setting, we strategically order questions we ask each user, tailored to previous information provided, to give the most accurate predictions while minimizing the cost to users. During the critical first 10 questions that our approach selects, prediction accuracy improves equally to fixed order approaches, but prediction certainty is higher.

Keywords


dynamic question ordering; residential energy consumption

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