Florian Allerding

Organic smart home: architecture for energy management in intelligent buildings

In this paper, we focus on a real world scenario of energy management of a smart-home. External signals, reflecting the low voltage grids state, are used to address the challenge of balancing energy demand and generation. A flexible energy management framework for smart buildings is presented, to control the intelligent appliances, decentralized power plants and electrical storages.

Integration of electric vehicles in smart homes - an ICT-based solution for V2G scenarios

In this paper, we present how electric vehicles (EV) can be flexibly integrated into the energy management of a smart home both in forms of consumer loads and electrical storage systems. Based on a draft for ISO 15118, we implemented an advanced version of a smart charge communication protocol which enables to flexibly control the charging and discharging processes of an EV in a sophisticated way, allowing to match the domestic load demand and the fluctuating energy supply of decentralized energy sources such as photovoltaic panels or a combined heat and power plant with the energy stored in the battery while at the same time guaranteeing a preset driving range adjusted by the user and thus limiting range anxiety.

Decentralized energy-management to control smart-home architectures

In this paper, we focus on a real world scenario of managing electrical demand sets of a smart-home. External signals, reflecting the low voltage grids state, are used to support the challenge of balancing energy demand and generation. To manage the smart-homes appliances and to integrate electric vehicles as energy storages decentralized control systems are investigated.

Electrical load management in smart homes using evolutionary algorithms

In this paper, we focus on a real world scenario of energy management of a smart home. External variable signals, reflecting the low voltage grids state, are used to address the challenge of balancing energy demand and supply. The problem is formulated as a nonlinear integer programming problem and a load management system, based on a customized evolutionary algorithm with local search, is proposed to control intelligent appliances, decentralized power plants and electrical storages in an optimized way with respect to the given external signals. The nonlinearities present in the integer programming problem makes it difficult for exact solvers. The results of this paper show the efficacy of evolutionary algorithms for solving such combinatorial problems.

User behavior prediction for energy management in smart homes

In this paper, we focus on the prediction of user interactions within a real world scenario of energy management for a smart home. External signals, reflecting the low voltage grids state, are used to address the challenge of balancing energy demand and generation. An autonomous system to aim at this challenge is proposed, in particular to coordinate decentralized power plants with the electrical load of the smart home. For that two prediction algorithms to estimate the future behavior of the smart home are presented: The Day Type Model and a probabilistic approach based on a first order Semi Markov Model. Some experimental results with real world data of the KIT smart home are presented.

Customizable Energy Management in Smart Buildings Using Evolutionary Algorithms

Various changes in energy production and consumption lead to new challenges for design and control mechanisms of the energy system. In particular, the intermittent nature of power generation from renewables asks for significantly increased load flexibility to support local balancing of energy demand and supply. This paper focuses on a flexible, generic energy management system for Smart Buildings in real-world applications, which is already in use in households and office buildings. The major contribution is the design of a “plug-and-play”-type Evolutionary Algorithm for optimizing distributed generation, storage and consumption using a sub-problem based approach. Relevant power consuming or producing components identify themselves as sub-problems by providing an abstract specification of their genotype, an evaluation function and a back transformation from an optimized genotype to specific control commands. The generic optimization respects technical constraints as well as external signals like variable energy tariffs. The relevance of this approach to energy optimization is evaluated in different scenarios. Results show significant improvements of self-consumption rates and reductions of energy costs.

A domain-specific language for the model-driven construction of advanced web-based dialogs

Complex dialogs with comprehensive underlying data models are gaining increasing importance in todays Web applications. This in turn accelerates the need for highly dynamic dialogs offering guidance to the users and thus reducing cognitive overload. Beyond that, requirements from the fields of aesthetics, Web accessibility, platform-independence, and Web service integration arise. To this end, we present an evolutionary, extensible approach for the model-driven construction of advanced dialogs. It is based on a Domain-specific Language (DSL) focusing on simplicity and fostering collaboration with stakeholders.

Decentralised Energy Management for Smart Homes

The electrical grid is expected to be faced with enormous challenges in the future by integrating fluctuating power plants using renewable resources (solar, wind) and by the increasing number of electrical vehicles. This article focuses on an energy management to balance electrical supply and demand in the energy grid. External signals, reflecting the low voltage grid’s state, are sent to smart-homes, which are able to adapt their energy demand automatically without restraining the smart-home’s resident. To manage the smart-home’s appliances and to effectively integrate electric vehicles as energy storages, decentralised measure- and control-systems are investigated. An hierarchically structured observer/controller architecture, inspired by the organic-computing, is proposed for the system implementation. The aim is a mostly self-organised system which reduces the interaction between the smart-home resident and its appliances to a minimum.