Modern buildings are equipped with various complex automation systems that produce vast amounts of data. The operation of these systems has significant influence on energy demands and user comfort in the premises. Therefore, an optimal configuration of the systems to increase energy efficiency is desired by the operators of the buildings. An automated decision-making process that evaluates and analyzes the bulk of available data can help to achieve these objectives. Hence, this work introduces a framework for the automatic generation of recommended configuration changes to increase energy efficiency and user comfort. The approach makes extensive use of semantic building information modeling as basis for the selection of potentially useful measures as well as for their assessment. Expected consequences of configuration changes are forecasted via simulation. Additionally, current results of an application of the proposed solution in an office building are discussed.

Today's buildings provide continuously growing amounts of data monitored from diverse sensors. With regard to the similarly increasing energy needs of buildings, accurate evaluation and analysis of these data can be used in order to improve energy efficiency and reduce overall energy consumption of buildings. Hence, this work introduces a comprehensive framework based on well-known data analytics methods to process the bulk of data and extract exploitable knowledge for further usage. The approach includes the descriptive evaluation and interpretation of sensed data, the prediction of future energy needs based on a set of potential actions, and the prescription of energy efficiency measures in the form of user instructions and configuration files for building automation systems. Additionally, identified use case scenarios are described, which will be used for evaluation of the presented data analytics framework. Furthermore, current results as well as ongoing work and expected future results are discussed in this work in progress.

This research investigates a space model server for indoor location-based services which require fine-grained spatial data. Functional and non-functional requirements for a space model server that supports such services are identified. A system architecture is proposed that scales well in terms of model size, query loads and types of services. The space model reuses a schema for network-based space layouts which supports high spatial resolution and efficient local queries. Properties of a query language used to extract partial models from the space model server are investigated. System implementation and validation are discussed.