Extracting Urban Microclimates from Electricity Bills
Authors: Thuy Vu, D. Stott Parker
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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 paper we describe analysis of about 5000 block group totals a summary of EC in census-defined neighborhoods that suggests ways to scale analysis for all of the data.We analyzed data from the UCLA Energy Atlas (Pincetl 2015) (www.energyatlas.ucla.edu) at the California Center for Sustainable Communities (CCSC), in cooperation with LADWP, containing 2006-2011 monthly electricity consumption histories for Los Angeles County. |
| Researcher Affiliation | Academia | Thuy Vu and D. S. Parker UCLA Computer Science Department Los Angeles, CA 90095-1595 |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any links or explicit statements about the availability of open-source code for the methodology it describes. |
| Open Datasets | Yes | Data for this work was obtained from the UCLA Energy Atlas developed at CCSC (California Center for Sustainable Communities), Institute of the Environment & Sustainability, UCLA. Energy Atlas research was funded by Los Angeles County, with access to data obtained from the California Public Utilities Commission. Copyright c 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.We analyzed data from the UCLA Energy Atlas (Pincetl 2015) (www.energyatlas.ucla.edu) at the California Center for Sustainable Communities (CCSC), in cooperation with LADWP, containing 2006-2011 monthly electricity consumption histories for Los Angeles County. |
| Dataset Splits | No | The paper describes the dataset used (UCLA Energy Atlas, 2006-2011 monthly electricity consumption histories) and various analyses (clustering, regression), but it does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, cloud resources) used for running the experiments. |
| Software Dependencies | No | The paper describes the algorithms and models used (e.g., k-means, Gaussian Process Regression) and cites relevant theoretical works, but it does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper describes the analytical methods used (e.g., clustering of 72 monthly values, Gaussian Process Regression), but it does not provide specific experimental setup details such as hyperparameter values, model initialization, or training schedules. |