A Systematic Energy Information Collection Methodology for Improved Energy Analytics

• Applicant: Professor Dr.-Ing. Andreas Reinhardt
• Subject Area: Security and Dependability, Operating-, Communication- and Distributed Systems
• Term: from 2017 to 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 393719143

Edifices worldwide are increasingly fitted with sensing systems to measure electrical energy consumptions, such as smart meters or building automation systems. They establish the foundation for energy analytics, i.e., the extraction of higher-level information from collected consumption data. The analysis of collected data, e.g., by means of signal processing or machine learning techniques, allows for the provision of services like the prediction of future consumption patterns or the disaggregation of a household total demand into the contributions of individual appliances. Despite the strong reliance on consumption data with a high information content, however, a methodology defining how to instrument the environment in order to attain data of appropriate quality has not emerged to date. We will hence address this challenge - finding a widely applicable sensing methodology to enable energy analytics at high accuracy - within this project. As a prerequisite for the determination of the sensing methodology, we will develop methods to quantify the information content in a collection of energy data. They will allow us to evaluate to which extent energy analytics can be conducted based on the given data. Subsequently, we will derive a methodology for the concerted deployment of sensors to collect electrical energy and/or power data, and possibly also additional environmental parameters. Our methodology will primarily specify requirements to the spatial and temporal resolution of the sensing points to deploy (i.e., required sampling rate and number of sensors), but also relate the expected information gain to the cost of the sensing infrastructure. The practical relevance of our work is ensured by primarily operating on data sets collected in real-world scenarios, either within the project itself or by other research groups.

• DFG Programme: Research Grants