Smart Meter Data-Driven Evaluation of Operational Demand Response Potential of Residential Air Conditioning Loads

Published: 15-07-2020| Version 2 | DOI: 10.17632/5vvffh53r5.2
Contributor:
Ning Qi

Description

The ground truth data used in this paper are obtained from three different areas to verify the effectiveness and robustness of the proposed methods. The detailed data information is described as follows: 1) The ground truth data from the Pecan Street dataset are collected from real households in the Muller project in Austin, TX, USA. Muller project funded by the U.S. Department of Energy and the U.S. National Science Foundation are located on the site of the Austin’s former municipal airport, close to central Austin.The selected homes in the project received monitoring equipment that captures electricity use on less than or equal to 1 min intervals for the whole home and 6 to 22 major appliances. Data over one year from August 2015 to July 2016 are analyzed, which contain the application-level and the whole-house energy consumption data. The corresponding 1-hour level temperature data are collected from the nearest Mueller weather station. We down-sample the energy consumption data to the 1-hour level to maintain consistency with the resolution of the temperature data. After data cleaning, customers without air conditioners or with missing readings are omitted, and the data of 119 residential customers are selected for accuracy analysis. 2) The ground truth data from smart home dataset are collected from real households in the Smart Home project in the Western Massachusetts, USA. The goal of this project is to optimize home energy consumption. The project involves several different types of dataset, including apartment dataset of 114 single-family, home dataset of 7 household and solar panel dataset, etc. However, the apartment dataset only contains the aggregated electrical data which can not be used to verify the accuracy of the load decomposition. Therefore, data over one year from January 2016 to December 2016 of home B and home G with individual ACLs monitor are selected for robustness analysis. 3) The ground truth data from low voltage distribution area are collected from low voltage distribution boxes in a developed city, Jiangsu province, China. Power and corresponding temperature data over one year from 2017 to 2018 are used for local DR programs. The dataset involves different distribution areas (i.e., different aggregated DR customers), including garment factory, hotel, rural neighborhoods, etc. However, the sub-meter data of all the ACLs are unavailable, thus it will only be used for aggregated DR potential analysis.

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