Digital Twin-Driven Decision Making and Planning for Energy Consumption

Abstract

The Internet of Things (IoT) is revolutionising how energy is delivered from energy producers and used throughout residential households. Optimising the residential energy consumption is a crucial step toward having greener and sustainable energy production. Such optimisation requires a household-centric energy management system as opposed to a one-rule-fits all approach. In this paper, we propose a data-driven multi-layer digital twin of the energy system that aims to mirror households’ actual energy consumption in the form of a household digital twin (HDT). When linked to the energy production digital twin (EDT), HDT empowers the household-centric energy optimisation model to achieve the desired efficiency in energy use. The model intends to improve the efficiency of energy production by flattening the daily energy demand levels. This is done by collaboratively reorganising the energy consumption patterns of residential homes to avoid peak demands whilst accommodating the resident needs and reducing their energy costs. Indeed, our system incorporates the first HDT model to gauge the impact of various modifications on the household energy bill and, subsequently, on energy production. The proposed energy system is applied to a real-world IoT dataset that spans over two years and covers seventeen households. Our conducted experiments show that the model effectively flattened the collective energy demand by 20.9% on synthetic data and 20.4% on a real dataset. At the same time, the average energy cost per household was reduced by 10.7% for the synthetic data and 17.7% for the real dataset.

Shaping future low-carbon energy and transportation systems: Digital technologies and applications

“…In consumer-end demand management, O'Dwyer applied DT technologies to predict and dispatch regional energy assets, which enables local governments to manage energy efficiently [180] . A DT based method was proposed by Fathy to track and flatten the energy usage level to improve energy efficiency [181] .…”

“…A recent focus of sustainable energy efficiency improvement is innovative energy services based on intelligent recommendation system and DT [213] . Based on artificial neural network (ANN), gradient enhancement, and K-means clustering algorithm, a tool integrating DT and energy management was developed for providing prediction, scheduling, optimal control, and coordination services for multi-vector smart energy systems, so as to realize optimal decision-making under user-defined objectives [180] . The strong predictive power of DT technology for integrated energy systems can enable efficient coordination between energy vectors, which can significantly reduce the system cost [215] .…”

Applications of Digital Twins in Power Systems: A Perspective

“…In [13], a multi-layer digital twin approach is introduced to reduce energy demands peak by shifting loads. In this study, an Internet of Things (IoT) smart gateway is used to collect hourly data on all appliances' consumption.…”

A Digital Twin Architecture Model Applied with MLOps Techniques to Improve Short-Term Energy Consumption Prediction

“…A proposal found in the literature used a digital twin to model energy providers and residences [17]. It employed a reinforcement learning algorithm to optimize smart home appliances scheduling to flatten total household energy consumption to avoid peak demands and reduce the energy bill.…”

“…One opportunity is to integrate the gamification elements and other motivational factors to extend the gamified management platform proposed in [15] with a conversational interface based on a smart home digital twin ontology. Another opportunity is to use our smart home digital twin to investigate MLOps aspects when deploying reinforcement learning models as the ones presented in [17], in addition to the prediction models presented herein and found in the literature [16].…”