Joaquim L. Viegas

TL;DR: A typology for the categorization of solutions for detection of non-technical losses is proposed and the sources and possible attack/vulnerability points are identified.

TL;DR: This paper proposes a process for the classification of new residential electricity customers using a combination of smart metering and survey data and model-based feature selection, allowing an easy interpretation of the derived models.

TL;DR: The proposed clustering-based novelty detection method for identification of non-technical losses, using the Gustafson-Kessel fuzzy clustering algorithm, achieves a true positive rate of 63.6% and false negative rate of 24.3%, outperforming other state-of-the-art unsupervised learning methods.

TL;DR: The use of mixed fuzzy clustering (MFC) algorithm to derive Takagi–Sugeno (T–S) fuzzy models (FMs) is proposed, which outperform FCM-based T–S FMs in four out of five datasets and k-nearest neighbors classifiers in five out ofFive datasets.

TL;DR: This paper proposes a platform for intelligent automation relying on a gateway or middleware between field devices, enterprise databases, and DSSs in real-time scenarios, and presents an implementation of the platform in the pharmaceutical industry, providing interoperability and real- time reaction capability to changes to an industrial prototype using dynamic scheduling algorithms.