Mischa Schmidt

IMS interoperability and conformance aspects [IP Multimedia Systems (IMS) Infrastructure and Services]

In this article, we take a closer look at interoperability and conformance aspects when using 3GPP IMS, ETSI TISPAN NGN, and related OMA service enablers. Considering the increasingly important role of 3GPP IMS within mobile and NGN (next generation network) contexts, we highlight the importance of interoperability and conformance testing of IMS products

Selective decryption of outsourced IoT data

Outsourcing of IoT-data is a common concept when using third-party services, e.g. an external building management service is given access to the buildings sensor measurements. However, the measurements captured from building systems often need to be protected from unauthorized access, because they are linked to persons, processes or business secrets, and therefore data protection requirements apply. Nevertheless, due to the large data volumes, external storage is desirable from a business perspective. We propose the concept of a Security Broker based on symmetric encryption schemes. It offers a key management scheme to flexibly create short decryption keys for time intervals or specific ranges of measurement values. We further show how the scheme can be generalized or a combination of the schemes can be applied. We implemented a prototype in Java and analyzed its performance. The evaluation proved the mechanisms applicability for mainstream applications running on off-the-shelf computing equipment.

Smart buildings as Cyber-Physical Systems: Data-driven predictive control strategies for energy efficiency

Abstract Due to its significant contribution to global energy usage and the associated greenhouse gas emissions, existing building stocks energy efficiency must improve. Predictive building control promises to contribute to that by increasing the efficiency of building operations. Predictive control complements other means to increase performance such as refurbishments as well as modernizations of systems. This survey reviews recent works and contextualizes these with the current state of the art of interrelated topics in data handling, building automation, distributed control, and semantics. The comprehensive overview leads to seven research questions guiding future research directions.

Context sensitive indoor temperature forecast for energy efficient operation of smart buildings

This paper analyzes the potential of knowledge discovery from sensed data, which enables real-time systems monitoring, management, prediction and optimization in smart buildings. State of the art data driven techniques generate predictive short-term indoor temperature models based on real building data collected during daily operation. The most accurate results are achieved by the Bayesian Regularized Neural Network technique. Our results show that we are able to achieve a low relative predictive error for each room temperature in the range of 1.35% - 2.31% with low standard deviation of the residuals.

Energy Efficiency Gains in Daily Grass Heating Operation of Sports Facilities through Supervisory Holistic Control

In recent reflections on environmental impacts of buildings, medium to large scale sports stadiums have gained substantial attention. These stadiums of e.g.~professional soccer teams are characterized by special system installations like grass heating systems serving the crucial commercial asset(s) and by event-driven usage patterns. Public buildings of this size imply situation-specific operational modes combined with high levels of safety and comfort requirements. In this paper we provide experimental verification of the energy savings potential of a professional soccer stadiums grass heating system during day-to-day operation. Our supervisory holistic control based on state of the art information and communication technology (ICT) is verified by seven experiments which we executed within the real operational setup of the Commerzbank Arena in Frankfurt, Germany. Our experiments operated different control strategies of increasing complexity. In winter 2014/2015 we achieved weather normalized energy savings of more than 56% compared to the last heating season. In an average heating season this would amount to savings of approximately 780 MWh and 150 t CO_2. At the same time we violated minimum temperature targets less than 6% of the time. These results stress the feasibility and benefits of applying holistic context-aware control strategies to large scale legacy consumption systems using supervisory ICT platforms. We demonstrate significant efficiency improvements and establish a new energy baseline that future control strategy evolutions will have to benchmark against.

Cyber-Physical System for Energy-Efficient Stadium Operation: Methodology and Experimental Validation

The environmental impacts of medium to large-scale buildings receive substantial attention in research, industry, and media. This article studies the energy savings potential of a commercial soccer stadium during day-to-day operation. Buildings of this kind are characterized by special purpose system installations like grass heating systems and by event-driven usage patterns. This work presents a methodology to holistically analyze the stadium’s characteristics and integrate its existing instrumentation into a Cyber-Physical System, enabling to deploy different control strategies flexibly. In total, seven different strategies for controlling the studied stadium’s grass heating system are developed and tested in operation. Experiments in winter season 2014/2015 validated the strategies’ impacts within the real operational setup of the Commerzbank Arena, Frankfurt, Germany. With 95% confidence, these experiments saved up to 66% of median daily weather-normalized energy consumption. Extrapolated to an average heating season, this corresponds to savings of 775MWh and 148t of CO2 emissions. In winter 2015/2016 an additional predictive nighttime heating experiment targeted lower temperatures, which increased the savings to up to 85%, equivalent to 1GWh (197t CO2) in an average winter. Beyond achieving significant energy savings, the different control strategies also met the target temperature levels to the satisfaction of the stadium’s operational staff. While the case study constitutes a significant part, the discussions dedicated to the transferability of this work to other stadiums and other building types show that the concepts and the approach are of general nature. Furthermore, this work demonstrates the first successful application of Deep Belief Networks to regress and predict the thermal evolution of building systems.

Enabling complex building energy visualization by integrative event-driven data provisioning

Constantly increasing energy efficiency requirements for buildings call for top-quality systems and services for optimizing the building energy life cycle. The deep integration of ICT systems in heterogeneous building environment demands new approaches, advanced technical algorithms and sophisticated tools. In this paper we introduce our Intelligent Energy Management Platform (INTELLEM), an integrative solution for monitoring, analyzing and visualizing energy performance across multiple buildings. We verified our architectural approach by implementing a prototype capturing real data streams of considerable volume over a prolonged period of time, proving that our event-driven approach ensures the responsiveness of the whole system. We implemented a rich Web-interface for visualization, comprising a set of components for comprehensive analysis of building energy performance.