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Hartmut Schmeck

Publications and source records attributed to Hartmut Schmeck.

2 recordsLinked to original sources

Open Energy Services -- Forecasting and Optimization as a Service for Energy Management Applications at Scale

This article aims at facilitating the widespread application of Energy Management Systems (EMSs), especially on buildings and cities, in order to support the realization of future carbon-neutral energy systems. We claim that economic viability is a severe issue for the utilization of EMSs at scale and that the provisioning of forecasting and optimization algorithms as a service can make a major contribution to achieve it. To this end, we present the \emph{Energy Service Generics} software framework that allows the derivation of fully functional services from existing forecasting or optimization code with ease. This work documents the strictly systematic development of the framework, beginning with a requirement analysis, from which a sophisticated design concept is derived, followed by a description of the implementation of the framework. Furthermore, we present the concept of the \emph{Open Energy Service} community, our effort to continuously maintain the service framework but also provide ready-to-use forecasting and optimization services. Finally, an evaluation of our framework and community concept, as well as a demarcation between our work and the current state of the art, is presented.

cs.SE

The Influence of Differential Privacy on Short Term Electric Load Forecasting

There has been a large number of contributions on privacy-preserving smart metering with Differential Privacy, addressing questions from actual enforcement at the smart meter to billing at the energy provider. However, exploitation is mostly limited to application of cryptographic security means between smart meters and energy providers. We illustrate along the use case of privacy preserving load forecasting that Differential Privacy is indeed a valuable addition that unlocks novel information flows for optimization. We show that (i) there are large differences in utility along three selected forecasting methods, (ii) energy providers can enjoy good utility especially under the linear regression benchmark model, and (iii) households can participate in privacy preserving load forecasting with an individual re-identification risk < 60%, only 10% over random guessing.

cs.CR