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The energy sector is one of the most active application domains being forced to re-think the current practice and apply data-management based IT solutions to provide a scalable and sustainable supply and distribution of energy. Challenges range from energy production by seamlessly incorporating renewable energy resources over energy distribution and monitoring to controlling energy consumption. Decisions are based on huge amounts of empirically collected data from smart meters, new energy sources (increasingly RES - renewable energy sources such as wind, solar, hydro, thermal, etc), new distributions mechanisms (Smart Grid), and new types of consumers and devices, e.g., electric cars.

The energy sector is in transition–being forced to rethink the current practice and apply data-management based IT solutions to provide a scalable and sustainable supply and distribution of energy. Novel challenges range from renewable energy production over energy distribution and monitoring to controlling and moving energy consumption. Huge amounts of “Big Energy Data,” i.e., data from smart meters, new renewable energy sources (RES–such as wind, solar, hydro, thermal, etc), novel distributions mechanisms (Smart Grid), and novel types of consumers and devices, e.g., electric cars, are being collected and must be managed and analyzed to yield their potential.

The building energy (BE) management plays an essential role in urban sustainability and smart cities. Recently, the novel data science and data-driven technologies have shown significant progress in analyzing the energy consumption and energy demand datasets for a smarter energy management. The machine learning (ML) and deep learning (DL) methods and applications, in particular, have been promising for the advancement of accurate and high-performance energy models. The present study provides a comprehensive review of ML- and DL-based techniques applied for handling BE systems, and it further evaluates the performance of these techniques. Through a systematic review and a comprehensive taxonomy, the advances of ML and DL-based techniques are carefully investigated, and the promising models are introduced. According to the results obtained for energy demand forecasting, the hybrid and ensemble methods are located in the high-robustness range, SVM-based methods are located in good robustness limitation, ANN-based methods are located in medium-robustness limitation, and linear regression models are located in low-robustness limitations. On the other hand, for energy consumption forecasting, DL-based, hybrid, and ensemble-based models provided the highest robustness score. ANN, SVM, and single ML models provided good and medium robustness, and LR-based models provided a lower robustness score. In addition, for energy load forecasting, LR-based models provided the lower robustness score. The hybrid and ensemble-based models provided a higher robustness score. The DL-based and SVM-based techniques provided a good robustness score, and ANNbased techniques provided a medium robustness score.

Der Paradigmenwechsel zur Entwicklungsmethodik innerhalb verteilter Kommunikationssysteme schlägt sich auch in der Telematik zum Anwendungspotential und Systemkomplexität nieder. Dies liefert eine neue Auffasung dessen, was den transparenten Datenkanal ausmacht.