Monitoring and Prediction in Smart Energy Systems via Multi-timescale Nexting

TL;DR

This paper presents a multi-timescale nexting approach for real-time prediction in smart energy systems, enabling proactive control without additional sensors or models, demonstrated through electrical heating and building thermal simulations.

Contribution

The paper introduces a novel multi-timescale nexting architecture for human-in-the-loop energy control that operates without requiring system models or extra sensor data.

Findings

Effective short-term predictions in electrical heating systems.

Successful simulation of building thermal behavior using natural temperature data.

Demonstrated potential for real-time energy system monitoring.

Abstract

Reliable prediction of system status is a highly demanded functionality of smart energy systems, which can enable users or human operators to react quickly to potential future system changes. By adopting the multi-timescale nexting method, we develop an architecture of human-in-the-loop energy control system, which is capable of casting short-term predictive information about the specific smart energy system. The developed architecture does either require a system model nor additional acquisition of (sensor) data in the existing system configuration. Our first experiments demonstrate the performance of the proposed control architecture in an electrical heating system simulation. In the second experiment, we verify the effectiveness of our developed structure in simulating a heating system in a thermal model of a building, by employing natural EnergyPlus temperature data.