Publication
SynTiSeD - Synthetic Time Series Data Generator
Michael Meiser; Benjamin Duppe; Ingo Zinnikus
In: 11th Workshop on Modelling and Simulation of Cyber-Physical Energy Systems (MSCPES) - Proceedings. Workshop on Modelling and Simulation of Cyber-Physical Energy Systems (MSCPES), located at Cyber-Physical Systems and Internet-of-Things Week, May 9, San Antonio, TX, USA, Pages 1-6, ISBN 979-8-3503-3682-5, IEEE Xplore, 5/2023.
Abstract
Recently, an increasing number of Artificial Intelligence services have been developed for a variety of domains. Machine Learning and especially Deep Learning services require a large amount of data to provide their functionality. Since data collection is typically complex and difficult, there is often not enough data available. Machine learning services such as anomaly detection or disaggregation algorithms are also being developed in the smart living domain. In practice, however, only a few energy datasets are publicly available, as the collection of such data is expensive and time-consuming due to the equipment required. One way to generate more smart meter data is to use a simulation. Developing such a simulation that is capable of generating meaningful data is a complex task. Therefore, in this paper, we present the Synthetic Time Series Data Generator (SynTiSeD), a multi-agent-based simulation tool that generates meaningful synthetic energy data based on real-world data. Furthermore, SynTiSeD allows generating data of critical situations, which are important for the development of such services, but which cannot be provoked in the real world. For transferability, we demonstrate that Nonintrusive Load Monitoring algorithms trained on synthetic data generated by SynTiSeD provide meaningful results that are even better than those of models trained on real data.