Alexander Wendt

Building energy management and data analytics

Energy efficiency in buildings depends on the way the building is operated. Therefore energy management is the key component for efficient operation. Data analysis of operation data helps to better understand the systems and detect faults and inefficiencies. The facility manager benefits from smart analysis that makes use of machine learning algorithms and innovative visualizations. This analysis is part of a bigger review of the current structure of building automation as it is used in todays buildings. The operation targets in energy efficiency are complex, ambiguous and contradictory: indoor comfort, energy efficiency, high availability and low costs cannot be met at the same time. In order to improve building operation, a novel model of automation is discussed. The foundation of this model is in cognitive automation, since each building is unique in its selection of energy sources, architecture, usage and location, which implies that the buildings control system has to be adapted individually. This paper connects the data-driven analysis of operation data with a cognitive concept to be used for operating the energy systems in a building and regarding goals on how to optimally operate while considering constraints about the limits of operation, using the complex, dynamic data from building automation.

ARS: An AGI Agent Architecture

The computational paradigm in Cognitive Science, which the AGI approach revives, provides a powerful methodology of examining human information processing by testing assumptions in computer simulations, and enables technical applications with human-like capabilities. Nevertheless, intensive interdisciplinary collaboration and the development of a holistic and integrated model remain ongoing challenges. This includes the consideration of the basis of rational cognition, in particular the significance of unconscious and affective processes in the human mind. We take these issues into consideration and integrate them into a holistic and integrated functional model of the human mind, implemented as an agent’s decision unit and evaluated in an Artificial Life simulation using an interdisciplinary methodology.

Usage of spreading activation for content retrieval in an autonomous agent

For cognitive agents that decide based on experiences, memory retrieval is a significant issue. In this article a contribution to this field is made by introducing the concept of psychic spreading activation for memory retrieval in an autonomous agent. Using the psychoanalytical foundation of that as a constraint, a variant of spreading activation was created to regard perceptional as well as motivational inputs and considering the emotional component of a situation. Results show how this type of memory retrieval can influence the decision making process.

ICT emulation platform setup demonstration of smart grid component prototype examples

The shift towards massively distributed energy generation demands more decentralized flexibility to meet strict power quality constraints of the electric grid. A cyber-physical system such as a smart grid can provide increased flexibility by utilizing additional information and communication technologies to better monitor the medium and low voltage distribution networks and to actively control grid-connected resources, ranging from loads to distributed generation, to electric mobility but at the cost of increased complexity. Essential future functionalities such as dynamic management of line use, fault detection and fast service restoration are only possible with appropriate sensors and actuators in place. These missing sensors and actuators on the distribution level are being developed today. This paper presents a standards based, low cost, open source, ICT emulation platform setup to test necessary networking concepts of these smart grid component prototypes already in various stages of development. Preliminary development results of the first example applications chosen: Customer Energy Management System, Smart Breaker, and Smart Meter, are shown in this work in progress paper.

Agent-Based cognitive architecture framework implementation of complex systems within a multi-agent framework

Due to their increasing complexity, the implementation of automation systems faces more challenges. Cognitive architectures have been designed to deal exactly with that. The purpose of a cognitive architecture is to find an action to every possible sensor data to move the system closer to a defined goal. The method is to utilize stored knowledge to reason about the best solution. The challenge for the implementation of such an architecture is to manage the overwhelming complexity of functionality and data. This paper presents the Agent-based Cognitive Architecture (ACONA) Framework, which aims at providing a general infrastructure, allowing the implementation of various cognitive architectures. It considers the encapsulation of functionality and provides a flexible composition of building blocks by applying a multi-agent approach.

A cognitive architecture for building automation

The operation of buildings holds the potential for significant energy savings and high flexibility during operation without affecting indoor comfort. The leverage for this potential is the building automation system, which can modify and optimize operation without the need of investments in energy systems or refurbishment of the envelope. The challenge is the uniqueness of each building and its energy systems. By introducing a cognitive architecture, it is possible to autonomously and adaptively optimize operation and allow for individual optimization. This work describes a cognitive architecture and its application to an optimization task for a renewable energy system.

Decision-making in the cognitive architecture SiMA

In a cognitive architecture, decision-making is the task that processes information from sensor data and stored knowledge to get appropriate action plans and actuator commands. Its aim is to make a decision in a given situation based upon available options and current goals of the system. In this paper, the decision-making process of the cognitive architecture SiMA is presented. Its unique features are the comprehensive evaluation of options, an application of case-based reasoning, as well as the management of resources by a two-step decision-making process. The implementation is verified through an artificial world implementation of a use case.

A multi-criteria exemplar model for holistic categorization in autonomous agents

When developing a model of perceptual categorization, a functional and holistic approach is required. Therefore, considering the influence of an agents current needs, perceptual categorization is modeled as the valuation of a stimulus regarding its potential to satisfy an agents needs. Based on an exemplar model, an activation-based model is developed that considers two kinds of categorization criteria, namely perceptual similarity and expectation, which represents bottom-up and top-down approaches of perception, respectively. The aggregation of these two criteria then corresponds to the integration of these approaches into a holistic model. A simplified simulation of the categorization model shows that the multi-criteria model leads to a more confident valuation of stimulus objects. In particular, the model is able to eliminate ambiguity in perceptual categorization. Although used equivalently, the simulation shows that the bottom-up similarity criterion is more significant than the top-down expectation-based criterion. In particular, the latter is only significant in case of ambiguity in an objects appearance.

Smart energy grids in Austria - innovative solutions and concepts

Through the increasing electricity generation from renewable energy sources the power infrastructure not only in Europe received new stimuli to find unique and novel solutions. Especially the integration of information exchange into the infrastructure lead to the definition of the actual research topic of smart grids. Austrian researchers and industry alike have built a strong community over the years that brought quite a number of interesting and innovative approaches and solutions to very different challenges the smart grid still has to face. This work will give an overview on the Austrian solutions and concepts for a smart energy grid.

Software architecture for a smart grids test facility

In the joint smart grids application project “Intelligent low voltage grid” of SIEMENS and Vienna University of Technology, households are emulated with transformers representing photovoltaic facilities and current sinks representing the loads. The purpose is to test new control concepts in small scale before a field trial. One example is the use of smart meter measurement for controlling a local transformers tap changer. For the interconnection and management of the emulation devices, a software infrastructure is needed. In this paper, the basic design of the software components and their interactions are presented. It is a flexible, extendable architecture which consists of a central server and multiple specialized clients. A communication service based on remote procedure calls was written, where Google Protobuf is used for the data exchange. In two applications, the realization of the concept is demonstrated.