Research, conception and development of an open platform for knowledge-based Smart Energy Services (SENERGY)
01.07.2018 – 30.06.2020
This project is supported by the European Regional Development Fund (ERDF) and the Free State of Saxony.
Application-oriented research on innovative energy technologies (InET)
Networked devices, e.g. smart home solutions, and corresponding digital services increasingly define everyday life. They play an important role in the energy industry, offering added value for end customers whilst energy providers reposition themselves as energy service providers. Smart energy services aim at uncovering hidden energy-saving potentials for companies and private households, lowering energy costs as well as providing added value (e.g. a more comfortable daily life) for customers while adapting to the respective user requirements. As a result traditional energy providers and IT service providers can acquire new sources of income in the residential environment ranging from energy management, health, safety, comfort to entertainment. Sensors and actuators provide the foundation for aforementioned smart services by gathering environmental data and executing tasks. Thus the central question tackled by the EU funded project ‘Research, conception and development of an open platform for knowledge-based Smart Energy Services (SENERGY)’ is how the development of smart services based on networked devices can be aided by software and therefore be simplified for small and medium-sized enterprises (SMEs).
Aimed at creating an open platform for smart energy services by providing the appropriate software tools, the SENERGY project intends to support the process of sensor data integration, data analysis and automated task execution based on gathered information. Enabling SMEs to integrate and process vast amounts of energy data attained from the IoT in order to develop predictive analysis services by leveraging machine learning. The perceived customer benefit and therefore customer demand is to be increased by creating cross-sectoral application possibilities of smart services (e.g. energy management, health, security, convenience and entertainment). As such the project goal is to combine suitable concepts, methods and software tools to build a platform, that enables the development of smart energy services and innovative business models.
The approach is based on a hybrid platform architecture, which comprises three stages (semantic data integration, machine learning and adaptive business processes) confined by a methodological framework Smart Energy Service Engineering.
Semantic data integration: Next to the syntactic interoperability needed for connecting various intelligent devices SENERGY aims to achieve cross-device semantic integration. This implies applying metadata to data for interpretation and possible context derivation. As a result of linking semantic aspects, complex ontologies (knowledge representation) must be created to serve as the basis for gaining context information used by smart energy services and applications. In this regard existing semantic technologies ought to be customized, extended and integrated with the SENERGY platform. Furthermore, data gathered from smart devices is to be semantically combined with additional data sources (e.g. weather, product, traffic and geological) to improve data analysis and usage.
Machine learning on energy data: With regard to the analysis of large amounts of data, the aim is to establish predictive and prescriptive methods based on machine learning in addition to the descriptive and diagnostic methods already commonplace in businesses and furthermore introducing this approach of knowledge generation to SMEs located in Saxony. Thereby increasing the informational value and benefit gained from historical or real-time data for businesses and customers. This can be illustrated by the analysis of energy consumption and production data in order to create more accurate forecasting and decision models for optimized load and production management, as well as detailed customer segmentation for developing realistic load profiles to optimize energy procurement from suppliers. Concerning end customers, it's necessary to create automated solutions enabling the parameterization of digital energy services according to customer needs and preferences by leveraging machine learning.
Adaptive business processes: Using context information and decision models gained from data analyses, timely feedback on the business process level is to be provided when state changes are detected and thus targeted actions can be triggered (e.g. energy-efficiency measures). In this regard it is intended to support the modeling, implementation, execution and monitoring of adaptive business processes, relying on sensors and actuators as resources, by software and methodological means. Business processes define the structure and control flow of smart energy services and can be described as a internal, technical representation. To achieve the required adaptive behavior it's necessary to examine concepts which allow for actions and their temporal sequence to be dynamically defined and or adjusted during run time based on contextual knowledge (i.e service composition).
Hybrid platform architecture: The project SENERGY aims at developing a platform architecture that enables knowledge-based Smart Energy Services to be executed centralized on a computer network as well as decentralized in local networks. The centralized approach addresses the core idea of cloud computing, in which local data is transferred via the internet to a cloud platform for further processing. This requires a high reliability and availability of the central system, a stable internet connection and a high level of trust by users towards the platform operator (e.g. regarding data protection and data security). The decentralized approach addresses data processing and execution of digital services at the "edge of the network" (fog computing), e.g. by user devices such as gateways or smartphones. This leads to more user acceptance and offers further advantages because services and applications can be used independently not requiring constantly available central instance. In contrast, user devices often seem to be limited in terms of computing power, making them unsuitable for more complex tasks. In this regard, it is intended to combine the advantages of cloud and fog computing within a hybrid platform architecture in order to support a wider range of applications, while considering data protection and security-related aspects.
Smart Energy Service Engineering: The development of digital services requires a systematic process in which different actors are directly or indirectly involved in value creation. SENERGY therefore aims to develop a methodological framework that will serve as a basis for connecting manufacturers of intelligent devices, energy companies (e.g. suppliers, network and metering point operators), IT service providers as well as end customers, integrating these entities into a service production process according to the open-innovation approach. In this context, the individual life cycle phases of knowledge-based Smart Energy Services must be identified, described and supported by means of information technology. The defined goal is to develop concepts that are legally/economically sustainable and enable a free or commercial deployment and usage of knowledge-based Smart Energy Services enabling innovative business models.