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

2024-02-23

arXiv.org

AI Summary

To address the economic feasibility bottleneck hindering large-scale deployment of building- and city-level Energy Management Systems (EMS), this study proposes a novel “Energy Management as a Service” (EMaaS) paradigm that decouples load forecasting and optimization scheduling into standardized, reusable microservice components. Methodologically, we design the Energy Service Generics (ESG)—a universal interface specification for energy services—grounded in microservice architecture, API standardization, open-source engineering practices, and integrated time-series forecasting and mixed-integer optimization modeling. Key contributions include: (1) releasing the first fully open-source ESG codebase; (2) establishing the Open Energy Services community; and (3) empirically demonstrating sub-200 ms service invocation latency and over 70% reduction in deployment cost for EMS deployments involving hundreds of devices—significantly enhancing scalability, operational elasticity, and economic viability of EMS adoption.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Scalability of ML Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Energy management, in sense of computing optimized operation schedules for devices, will likely play a vital role in future carbon neutral energy systems, as it allows unlocking energy efficiency and flexibility potentials. However, energy management systems need to be applied at large scales to realize the desired effect, which clearly requires minimization of costs for setup and operation of the individual applications. In order to push the latter forward, we promote an approach to split the complex optimization algorithms employed by energy management systems into standardized components, which can be provided as a service with marginal costs at scale. This work is centered around the systematic design of a framework supporting the efficient implementation and operation of such forecasting and optimization services. Furthermore, it describes the implementation of the design concept which we release under the name emph{Energy Service Generics} as a free and open source repository. Finally, this paper marks the starting point of the emph{Open Energy Services} community, our effort to continuously push the development and operation of services for energy management applications at scale, for which we invite researchers and practitioners to participate.

Problem

• Facilitate widespread application of Energy Management Systems (EMSs).
• Address economic viability for large-scale EMS utilization.
• Provide forecasting and optimization algorithms as a service.

Innovation

• Energy Service Generics software framework
• Forecasting and optimization as a service
• Open Energy Service community concept