Zigeng Fang

The increasing penetration of distributed renewable energy sources and electric vehicles (EVs) introduces significant operational challenges for residential energy management systems (HEMS), including stochastic renewable generation, uncertain load demand, device coupling, and physical safety constraints. This paper proposes a digital-twin-aided safe multi-agent reinforcement learning framework for coordinated energy management in renewable-integrated residential systems. The proposed approach models the battery energy storage system and the EV as independent agents and employs a multi-agent soft actor–critic (MASAC) algorithm with a centralised critic to capture the interactions among distributed energy resources. To improve decision quality under uncertainty, a digital twin module is developed to maintain a virtual representation of the residential energy system, synchronise operational states, update degradation-sensitive parameters, and generate short-term predictive information on photovoltaic (PV) generation and household load. The updated digital twin states and forecasts are incorporated into the observations of the reinforcement learning agents. In addition, a safety projection layer is incorporated to improve operational feasibility during both training and deployment. The environment considers realistic residential characteristics, including time-of-use electricity prices, battery degradation, EV mobility patterns, and grid energy trading. Simulation results show that the proposed framework reduces daily energy costs compared with rule-based baselines while maintaining EV charging reliability and operational feasibility. These results highlight the potential of combining predictive information, safety-constrained action execution, and multi-agent reinforcement learning for intelligent residential energy management.

Effective scheduling and resource allocation are crucial elements for the success of construction projects. In recent decades, the adoption of ontological models has become a prevalent approach to capturing and structuring knowledge in this domain. This book chapter provides an in-depth review of recent research on creating, integrating, and applying ontological models for scheduling and resource planning. The chapter discusses recent developments and challenges of three main functions of ontology application in construction scheduling and resource planning. The literature review shows various benefits of using ontological models for construction projects. Despite the advantages, the chapter acknowledges the challenges of developing general, consistent, and maintainable ontological models and ensuring existing models’ usability, scalability, and compatibility with conventional planning systems. Following the review, a case study has been presented. This case study introduces the dynamic scheduling of crane operations, offering a novel approach to harness the full potential of ontological models while navigating the distinct challenges inherent. This book chapter provides a comprehensive overview of ontological models in the domain of time and resource management for construction projects and offers valuable insights to researchers and practitioners interested in optimising project outcomes through the utilisation of explicit or tacit knowledge and relevant data.

Digital Twins (DT), as one of the emerging trends in digitalisation, have attracted a large amount of research in the past few years due to its ability to provide more accurate models and better predictions for various energy-related applications. In the context of demand response (DR), the real-time bidirectional communication characteristic of DT allows for functions such as real-time energy forecast and decision-making aid, which provided high potential in DR applications due to the nature of DR being highly time-sensitive. The objective of this paper is to explore the concept and application of DT in the domain of DR, as there are currently limited numbers of review on this topic. It was identified that most work in this area shows promising results but is still exploratory. The main application of DT in the domain of DR is using DT as an enabling tool by leveraging the real-time and data processing function of DT for monitoring and decision-making, and providing a medium for better visualisation. Future studies can experiment with applying DT frameworks to larger case studies and apply more elaborate DT with higher maturity levels.

Ever-tighter asset management budgets and higher requirements for compliance aggravate the challenges in the modern practice of managing built assets. In this regard, digitalization technologies are increasingly important in built asset management. Demands and challenges arise from the built asset’s data-to-day operational management and an urgent organizational-level agenda for an adaptable and sustainable digital built asset management strategy (DBAMS). This chapter aims to provide a review and analysis of these demands and challenges by discussing the following topics: (1) the definitions of DBAMS; (2) the digital built asset management requirements; and (3) the interplay between construction business models and digital technologies. In the first section of the chapter, DBAMS will be defined with the help of closely related concepts, and core functions of DBAMS will be explained with some digital-related examples. Then, a strategic analysis will be conducted to review the digital built asset requirements from different stages in the project lifecycle and different perspectives (i.e., economic perspective). Finally, three traditional outsourcing business models (the shared service centres [SSCs], the public-private partnerships [PPPs], and the outsourcing model) for the construction industry will be reviewed against their two general ways of interaction with digital technologies. This chapter is intended to provide university students, academics, and practitioners in the built asset management domain with the up-to-date mindset, strategies, and practical examples of embracing new digitalized built asset management routines.

Despite the popularity of image-based classification techniques in identifying building materials and elements in building and construction sites, the real feasibility of them accommodating the needs of operation and maintenance (O&M) phase inspection-repair processes at the real project scale is still awaiting testing due to the vast number of project assets, hundreds of asset categories, and requirements of portfolio-based strategic asset management (SAM) tasks. The building’s O&M phase does not yet have a large-scale inspection-repair dataset created on images based on the actual workflow of building and infrastructure projects. This chapter describes a MobileNet1.0-based image classification method for optimising and automating a series of portfolio-based SAM service processes, including condition surveying, data validation, standardisation and integration. By examining the performance of image classification in the building O&M phase’s SAM tasks, the chapter examines whether the constructed MobileNet1.0 model can achieve satisfactory built assets category classification performance based on commercial surveying data with the enhancement of the online image training dataset. The constructed MobileNet1.0 model achieved satisfactory built assets category classification results for both building asset type category classification (with 85.6% level-1 test accuracy) and different building data attributes (e.g., built assets’ condition, activity cycle, failure type, etc.). The experiment results proved that the online enhanced image training dataset can further boost the on-site image classification performance. The results of this chapter demonstrate a broader application of image-based technologies in portfolio-based SAM and other building and infrastructure projects’ O&M phase applications. Future research can be conducted to exploit the performance of image classification methods over more building asset categories and building and building asset data attribute types.