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

TL;DR

This work presents a framework for provisioning forecasting and optimization algorithms as web services to enable large-scale, cost-effective energy management. It systematically derives requirements from application domains, designs a service framework (Energy Service Generics), and implements a Python-based reference system (ESG) that decouples forecasting/optimization code from EMS via a scalable, asynchronous API. An Open Energy Services community is proposed to sustain framework development and publish ready-to-use services, with an evaluation demonstrating end-to-end service derivation, client interaction, and scalability up to thousands of requests. The approach promises to lower development and operation costs, facilitate interoperability, and accelerate deployment of EMS at scale for carbon-neutral energy systems.

Abstract

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.

Figures (7)

• Figure 1: Typical high-level architecture of an
• Figure 2: High-level architecture of an utilizing selected forecasting and optimization services
• Figure 3: Stakeholders involved in the development process of forecasting or optimization services
• Figure 4: Components of a service derived with the service framework
• Figure 5: Internal architecture of a service with processes of components and communication between processes