The Internet of Things (IoT) is revolutionising how energy is delivered from energy producers and used throughout residential households. Optimising the residential energy consump-tion 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.
Digital twin simulation has many applications, but one of the most important applications is in automotive industry. It helps in fleet management, cost management and most importantly increases the operational efficiency of the physical asset. The data from the previous asset’s life cycle is also important for the current asset to be efficient. There are many challenges faced by the automotive industry for which digital twinning might be a solution.
Today, digital twins are proliferating rapidly across various industries. While there may be multiple ways to define a digital twin, at its core, a digital twin
is a virtual representation of the key attributes of
a physical object or process that can be tracked and updated along the lifecycle to achieve design and operational efficiencies. It’s more important, therefore, to focus on the intended business outcomes and the ways a digital framework enables them.
In recent years, industrial and research communities have devoted considerable effort
to the digitalisation of AECOO processes with the main objective of achieving a new paradigm for any Design and Construction project which should allow the highly desired improvement of the productivity in the sector.
While the term sounds like something out of a bad science fiction novel,
“digital twins” are revolutionizing the ways that businesses use artificial intelligence, data analytics, and the Internet of Things (IoT). So what is a digital twin exactly, and why should they be an essential part of your IoT projects?
GosuncnWelink Technology Co., Ltd. (referred to as GosuncnWelink) is an IoT company under the Gosuncn Group (300098), focusing on Cellular IoT products and solutions, providing customers with secure and reliable IoT communication products and services.
GosuncnWelink's products cover various standard and packaged cellular M2M modules, Telematics terminals, smart Broadband products, and have been recognized by many international industries. Through independent research and development of IoT communication products and solutions, the company provides innovative solution applications for international OEM terminal vendors, solution providers, system integrators, IoT service providers, MNOs, and other partners all around the world.
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