Extracting Urban Microclimates from Electricity Bills

Authors

  • Thuy Vu University of California, Los Angeles
  • D. Stott Parker University of California, Los Angeles

DOI:

https://doi.org/10.1609/aaai.v31i1.11171

Keywords:

computational sustainability, energy management, Gaussian Process Random Field

Abstract

Sustainable energy policies are of growing importance in all urban centers.Climate — and climate change — will play increasingly important roles in these policies.Climate zones defined by the California Energy Commissionhave long been influential in energy management.For example, recently a two-zone division of Los Angeles(defined by historical temperature averages) was introduced for electricity rate restructuring.The importance of climate zones has been enormous,and climate change could make them still more important. AI can provide improvements on the ways climate zones are derived and managed.This paper reports on analysis of aggregate household electricity consumption (EC) data from local utilities in Los Angeles,seeking possible improvements in energy management. In this analysis we noticed that EC data permits identificationof interesting geographical zones  — regions having EC patterns that are characteristically different from surrounding regions.We believe these zones could be useful in a variety of urban models.

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Published

2017-02-12

How to Cite

Vu, T., & Parker, D. S. (2017). Extracting Urban Microclimates from Electricity Bills. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). https://doi.org/10.1609/aaai.v31i1.11171

Issue

Section

Special Track on Computational Sustainability