Print Email Facebook Twitter Machine Learning for Digital Twins to Predict Responsiveness of Cyber-Physical Energy Systems Title Machine Learning for Digital Twins to Predict Responsiveness of Cyber-Physical Energy Systems Author Snijders, R. Pileggi, P.P. Broekhuijsen, B.J. Verriet, J. Wiering, M. Kok, J.K. Publication year 2020 Abstract Cyber-Physical Systems are becoming more autonomous, interconnected, complex and adaptive, and are expected to operate in highly dynamic environments. This is especially challenging for energy ecosystems that are increasingly difﬁcult to control and maintain as the number of participating manufacturers and users grows. Digital Twins help analyze and predict these systems in the form of digital reﬂections that operate in parallel with the physical system. In this paper, we use Machine Learning to improve the predictive power of Digital Twins for Cyber-Physical Energy Systems. Speciﬁcally, we use a Temporal Convolutional Neural Network model to learn the temporal patterns in the system and predict its responsiveness to speciﬁc power setpoint instructions. Real-life data from ten batteries were used to predict the behavior over time. Compared to the baseline model that uses the prior probability of response and the average response rate within the conﬁgured time window, the model predicts the batteries’ responsiveness more accurately. The more temporal information is used as input for prediction, the better the model performs in both precision and recall. The results show that this compensates for the lack of information when fewer metrics are used. The use of Machine Learning for Digital Twins can help maintain a heterogeneous energy ecosystem, while minimizing the need to acquire or disclose detailed information Subject Machine learningDigital twinCyber-Physical energy systemTemporal convolution neural network To reference this document use: http://resolver.tudelft.nl/uuid:de52381f-9bac-47dd-91f2-5be831c11067 TNO identifier 878055 Publisher IEEE Source 8th Workshop on Modeling and Simulation of Cyber-Physical Energy Systems 22 april Sydney Australia Document type conference paper Files To receive the publication files, please send an e-mail request to TNO Library.