Blockchain Papers

Olusegun Adebayo

No abstract is available for this record.

Madalina-Mihaela Buzau, Javier Tejedor-Aguilera, Pedro Cruz-Romero, Antonio Gómez‐Expósito

Non-technical losses (NTL) in electricity utilities are responsible for major revenue losses. In this paper, we propose a novel end-to-end solution to self-learn the features for detecting anomalies and frauds in smart meters using a hybrid deep neural network. The network is fed with simple raw data, removing the need of handcrafted feature engineering. The proposed architecture consists of a long short-term memory network and a multi-layer perceptrons network. The first network analyses the raw daily energy consumption history whilst the second one integrates non-sequential data such as its contracted power or geographical information. The results show that the hybrid neural network significantly outperforms state-of-the-art classifiers as well as previous deep learning models used in NTL detection. The model has been trained and tested with real smart meter data of Endesa, the largest electricity utility in Spain.

Daniel Ginsberg, Claus‐Peter Fritzen

Load monitoring and damage identification are important tasks in the field of Structural Health Monitoring. Reconstructing unknown force inputs or system parameters usually involves the solution of an inverse problem which is mostly ill-posed. In the last decades a lot of effort has been spent in solving these problems separately. However, unknown loads and damage both have influence on the structural vibration pattern. The difficulty of simultaneous identification of external forces and structural damage is to distinguish these influences by using only the measured vibration effects. The use of prior knowledge of the unknown quantities is advisable for solving the combined inverse problem and to obtain meaningful solutions. In this contribution a sparsity-based reconstruction method in time domain is developed for identifying the unknown structural force excitation and damage parameter simultaneously by using output-only acceleration data. Sparsity means the majority of the solution vector is zero and only a very few elements are nonzero. Here damage is interpreted as additional load on the damaged structural element (virtual distortion). A numerical proof-of-concept experiment of a quadratic aluminum plate is presented. It shows that the proposed reconstruction method is able to identify the unknown external force and damage parameter by using a significant lower number of accelerometers than present methods.