Fraunhofer Energy Alliance
Fraunhofer Energy Alliance

Energy Digital

ENERGY DATA ANALYSIS

Energy Data Analysis

An important benefit of the collected and transmitted data is the option to analyze them. In the easiest case, links generate alarms and alerts. Larger amounts of data provide a significant added-value in terms of preparation, aggregation and representation. While Data Analytics is the process of analyzing values of the past, forecasts will have a much larger influence in future. Systems of the Fraunhofer Energy Alliance predict energy demands via model-based methods depending on season, weekday, time and other relevant parameters like meteorological constraints. The same is also true for Condition Monitoring. In this approach, operating data are collected in order to identify a significant change which is indicative of a developing fault.

Competencies »Energy Data Analysis« | Fraunhofer Energy Alliance

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Big Data Analytics

Member institutes use technologies from the field of Big Data Analytics in various projects. For example, a specially developed database of experience in the wind power sector offers benchmarking and also enables the reliability characteristics of wind farms, wind power plants and components to be determined. Within the framework of another extensive project, tools for the analysis of demand and potential of storage facilities in regional energy systems are being developed. Both the spatial and temporal variability of energy production and consumption is taken into account, thus creating a better understanding of the spatial characteristics. Our own high-performance server infrastructures allow our member institutes to efficiently analyze extensive data sets. Another approach is to use artificial intelligence (AI) and so-called phasor measurements to detect faults and anomalies in power grids automatically and in real time.

Projects

 

© Fraunhofer IOSB-AST/ Martin Käßler

DynaGridCenter

Artificial Intelligence (AI) for secure power grids: Here is how Fraunhofer researchers developed the compression techniques, algorithms and neural networks to make a power supply fit for the future.

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Fraunhofer USA (Boston)

As part of the US Administration’s effort to cut energy waste in US buildings and double energy productivity by 2030, the US Energy Department invested $14 million to dramatically increase the efficiency of US homes and buildings.

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CMS / SHM

Structural Health Monitoring (SHM) and Condition Monitoring (CMS) are technologies whose task is to continuously check the condition of a machine - for example in the context of a wind power plant. On the basis of the output characteristic values, damage can be detected at an early stage, thus significantly reducing maintenance times and costs. The Fraunhofer Energy Alliance includes SHM and CMS technologies in projects related to wind power, for example a data and process model for the operation and maintenance of wind power plants. Researchers at Fraunhofer are continuously developing new sensor technologies and algorithms for failure prediction for a new generation of Condition Monitoring Systems. CMS or SHM systems are also used in energy management systems for buildings.

Projects

 

© HyLITE Twitter

HyLITE

Joint project HyLITE: digital twin-centered services and applications for dynamic operation and protection of the future energy supply system.

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Visualization / Representation

Visualizations of data are used in a variety of different applications around renewable energies. For example, a computer simulation shows possible variants of a corridor course of a future power grid in rural areas. A tool for monitoring the condition of batteries in hybrid and electric vehicles was also developed, which visualizes the data in a user-specific way. With regard to the optimization of operating strategies for buildings, researchers at Fraunhofer have also developed a database for monitoring data, on the basis of which data can be visualized and cross-analyses of the energy performance of individual technologies can then be performed. The development of visualizations in the context of a renewable virtual power plant is also part of the extensive portfolio.

Projects

 

© Fraunhofer IEE

Virtual Power Plant IEE.vpp

A modular real-time system, which allows us to monitor, control, aggregate, and optimize renewable energy systems according to various strategies.

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© Fraunhofer IEE

Energy Meteorology Systems

Fraunhofer IEE's real-time power estimates and forecasts are the centrepiece of innovative solutions for reliable grid operation and planning with weather-dependent energy sources.

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RIGRID

The goal of the RIGRID project is to develop and test new interactive energy and infrastructure design tool for optimal planning and operation of new emerging energy infrastructures in rural areas.

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© Open Data Thüringen | virtualcitySYSTEMS GmbH

New Energy Economy For Muncipalities

New Fraunhofer tool calculates suitable energy mix for smaller communities, even proposing available funding opportunities.

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