Digitalization of the building sector has become a critical topic of interest, prioritizing the decarbonization goals set for this decade. To achieve net zero emissions by 2050 according to the Paris Agreement, all new buildings and 20% of existing buildings are expected to be fully decarbonized by the end of 2030. The digital twin is an emerging concept where a physical asset and its digital copy interact in real-time, enabling various benefits such as the possibility for monitoring and analysis. Various studies have been conducted to understand how digital twins can bring value in decarbonizing buildings. In the context of evolving electricity grids, there is an evident gap in research on digital twins for grid-interactive buildings. These buildings can deliver power back into the grid and participate in the ancillary market, thus helping to avoid demand peaks, also providing flexibility. This paper conducts a systematic literature review using PRISMA guidelines on digital twins for smart grid interactive buildings, which the existing literature lacks. The study then develops a classification framework to understand the difference between a digital twin and other digital representations in the context of gridinteractive buildings based on the existing literature. It identifies and critically compares the best software tools and artificial intelligence techniques that can be used for creating a digital twin for a grid interactive building with optimal precision and accuracy in real-time. Finally, the review results are analyzed and used to develop a recommendation roadmap for developing a digital twin for a grid-interactive building.

Global floor area is increasing every year which is subsequently leading to an increase in electricity and heating demand in buildings. Residential buildings have huge potential for energy savings and there is an immediate need to decarbonize them by the end of 2050. Machine learning is finding application across all fields and will thus have an important role to play in the building sector also. One of the important challenges that building owners need to tackle is occupancy detection in residential apartments which can help save considerable amounts of energy and costs. However, occupancy is highly variable, and it is difficult to quantify and predict occupancy because of the random and individualistic nature of humans. In addition, scalable approaches for occupancy detection should prioritize data from common and cost-effective sensors like temperature sensors. In contrast to existing literature which has stated that occupancy detection based on the data from a single environmental sensor is not appropriate for obtaining good results, this paper aims to detect occupancy in a real residential building using only indoor temperature as the feature to train the model. Different machine learning models and techniques are studied and tested to understand how the accuracy of occupancy detection can be increased. With the right techniques, it has been possible to obtain promising results in the form of an accuracy of 95% using machine learning models and only indoor temperature to train it.

Building and construction sector is responsible for 40% of the total energy consumption and 36% of the total greenhouse gas emissions in the European Union. Digital twin is an emerging digital tool that facilitates building management through data interactions using sensor readings between a physical building and its digital model and improves operation and enhances transparency. However, since the digital twin technologies are not mature and has several challenges associated with it, such as need for extensive data, it is necessary to conduct a systematic literature review on its application to buildings and smart grids. The majority of the current studies look into how digital twins can be used for the management of normal residential or commercial buildings that are connected to conventional electricity grids with little scope for bidirectional power flow. This study conducts a systematic literature review to map the current landscape of research on digital twins in grid-interactive buildings, with a focus on identifying the software tools used in the creation of digital twins for improving energy efficiency. The study uses scientific databases like Scopus and Web of Sciences and has been carried out in accordance with PRISMA guidelines that specify the different steps involved in the methodology to conduct systematic reviews. Autodesk Revit and Artificial Neural Networks emerged as the most common software and technique, based on previous works.

Building and construction sector is responsible for 40% of the total energy consumption and 36% of the total greenhouse gas emissions in the European Union. Digital twin is an emerging digital tool that facilitates building management through data interactions using sensor readings between a physical building and its digital model and improves operation and enhances transparency. However, since the digital twin technologies are not mature and has several challenges associated with it, such as need for extensive data, it is necessary to conduct a systematic literature review on its application to buildings and smart grids. The majority of the current studies look into how digital twins can be used for the management of normal residential or commercial buildings that are connected to conventional electricity grids with little scope for bidirectional power flow. This study conducts a systematic literature review to map the current landscape of research on digital twins in grid-interactive buildings, with a focus on identifying the software tools used in the creation of digital twins for improving energy efficiency. The study uses scientific databases like Scopus and Web of Sciences and has been carried out in accordance with PRISMA guidelines that specify the different steps involved in the methodology to conduct systematic reviews. Autodesk Revit and Artificial Neural Networks emerged as the most common software and technique, based on previous works.