Pirathayini, SrikanthaS.E. Kababji2021-08-302021-08-302020-07S. E. Kababji and P. Srikantha, "A Data-Driven Approach for Generating Synthetic Load Patterns and Usage Habits," in IEEE Transactions on Smart Grid, vol. 11, no. 6, pp. 4984-4995, Nov. 2020.1949-3053http://dx.doi.org/10.1109/TSG.2020.3007984http://hdl.handle.net/10315/38535Today's electricity grid is rapidly evolving to become highly connected and automated. These advancements have been mainly attributed to the ubiquitous communication/computational capabilities in the grid and the Internet of Things paradigm that is steadily permeating modern society. Another trend is the recent resurgence of machine learning which is especially timely for smart grid applications. However, a major deterrent in effectively utilizing machine learning algorithms is the lack of labelled training data. We overcome this issue in the specific context of smart meter data by proposing a flexible framework for generating synthetic labelled load (e.g., appliance) patterns and usage habits via a non-intrusive novel data-driven approach. We leverage on recent developments in generative adversarial networks (GAN) and kernel density estimators (KDE) to eliminate model-based assumptions that otherwise result in biases. The ensuing synthetic datasets resemble real datasets and lend to rich and diverse training/testing platforms for developing effective machine learning algorithms pertaining to consumer-side energy applications. Theoretical and practical studies presented in this paper highlight the viability and superior performance of the proposed framework.en© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.smart gridsmachine learning algorithmsdemand-side managementstatistical learningArticlehttps://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5165411https://www.ieee.org/https://ieeexplore.ieee.org/document/9136769