Abstract
This paper describes an energy management system that is being developed in the AnyPLACE project to support new energy services, like demand response, in residential buildings. In the project end-user interfaces are designed and implemented to allow the input of preferences regarding the flexible use of shiftable and thermal appliances. Monitoring and self-learning algorithm are used to allow additional information to be collected and an automation platform is available for the management and control of appliances. An energy management algorithm is presented that processes end-user preferences and devices characteristics to produce an optimal dispatch considering demand response incentives. Results show the successful implementation of an optimized energy scheduling.

Abstract
This paper presents the development framework for an energy management platform that is being developed within the AnyPLACE project. In order to ensure that end-users become active participants in services like demand response, a combined approach is necessary in terms of monitoring, automation, and user interfacing. The success in engaging the end-user, as the centerpiece of the energy management challenge, is vital in taking advantage of a more efficient use of energy, as it is shown in this paper. The proposed framework can be run in a single board computer.

Abstract
Smart grids technologies are enablers of new business models for domestic consumers with local flexibility (generation, loads, storage) and where access to data is a key requirement in the value stream. However, legislation on personal data privacy and protection imposes the need to develop local models for flexibility modeling and forecasting and exchange models instead of personal data. This paper describes the functional architecture of an home energy management system (HEMS) and its optimization functions. A set of data-driven models, embedded in the HEMS, are discussed for improving renewable energy forecasting skill and modeling multi-period flexibility of distributed energy resources.

Abstract
Energy management is a key tool that will enable consumers to optimize their energy use according to different objectives. Allow users to insert their energy use preferences combined with the effective configuration and control of existing devices (loads and micro generation) is the basis, in this paper, to design adaptable energy optimization algorithms that are capable of outputting feasible, understandable and useful actions, automated and/or manual, for the activation of the existing portfolio of flexible devices. This paper presents an advanced energy management system as an innovative platform that intends to accomplish real energy optimization schemes to support demand response, promote the energy efficiency and contribute towards renewable integration.

Abstract
Home energy management systems (HEMSs) are important platforms to allow consumers the use of flexibility in their consumption to optimise the total energy cost. The optimisation procedure embedded in these systems takes advantage of the nature of the existing loads and the generation equipment while complying with user preferences such as air temperature comfort configurations. The complexity in finding the best schedule for the appliances within an acceptable execution time for practical applications is leading not only to the development of different formulations for this optimisation problem, but also to the exploitation of non-deterministic optimisation methods as an alternative to traditional deterministic solvers. This study proposes the use of genetic algorithms (GAs) and the cross-entropy method (CEM) in low-power HEMS to solve a conventional mixed-integer linear programming formulation to optimise the total energy cost. Different scenarios for different countries are considered as well as different types of devices to assess the HEMS operation performance, namely, in terms of outputting fast and feasible schedules for the existing devices and systems. Simulation results in low-power HEMS show that GAs and the CEM can produce comparable solutions with the traditional deterministic solver requiring considerably less execution time.