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The building sector accounts for about 30% of the global final energy consumption. Most of the consumed energy originates from fossil fuels. The operation of buildings is known to suffer from various deficiencies, degrading their energy performance. An untapped potential lies, therefore, in the optimization of building operation to significantly reduce CO2 emissions and to increase the cost effectiveness and user comfort. Over the past 40 years, extensive research has been carried out to investigate and develop methods for building performance optimization based on measured data from building services, such as heating, ventilation, air conditioning, and lighting systems. The ongoing digitalization trend in the building sector offers the opportunity to easily access large amounts of high-quality measurement data and semantic building information as digital descriptions. This facilitates the development and implementation of automated routines for the continuous supervision and optimization of building operation, including reliable fault detection and diagnosis and model-predictive control. This review article is focused on three major research topics in the field of energy-efficient buildings, namely, semantic interoperability between heterogeneous and complex systems, methods for fault detection and diagnosis, and model-predictive control.

Energy performance optimization in buildings: A review on semantic interoperability, fault detection, and predictive control