Surrogate models have proved to be a suitable replacement for complex simulation models in various applications. Runtime considerations, complexity reduction and privacy concerns play a role in the decision to use a surrogate model. The choice of an appropriate surrogate model though is often tedious and largely dependent on the individual model properties. A tool can help to facilitate this process. To this end, we present a surrogate modeling process supporting tool that simplifies the process of generation and application of surrogate models in a co-simulation framework. We evaluate the tool in our application context, energy system co-simulation, and apply it to different simulation models from that domain with a focus on decentralized energy units.
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Big data processing in the Smart Grid context has many large-scale applications that require real-time data analysis (e.g., intrusion and data injection attacks detection, electric device health monitoring). In this paper, we present a big data platform for anomaly detection of power consumption data. The platform is based on an ingestion layer with data densification options, Apache Flink as part of the speed layer and HDFS/KairosDB as data storage layers. We showcase the application of the platform to a scenario of power consumption anomaly detection, benchmarking different alternative frameworks used at the speed layer level (Flink, Storm, Spark).
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