http://hdl.handle.net/10453/35196 2026-04-10T01:11:46Z http://hdl.handle.net/10453/193744 Title: RHpT: Bioclimatic framework for passive climate-adaptive bio-façades in urban contexts Authors: Debnath, KB; Pynirtzi, N; Scott, J; Davie, C; Bridgens, B 2026-05-01T00:00:00Z http://hdl.handle.net/10453/193581 Title: Let's Play: Co-designing inclusive school playgrounds with neurodivergent children Authors: Kelly, S; Kerr, J; Rieger, J; Cushing, DF Abstract: School playground design does not always reflect the needs of the children who play there, particularly neurodivergent children. This leads to exclusion and limited opportunities for skill development and peer relationships. This research engages children with neurodivergent conditions, who are rarely asked for their opinions, to conceptualise their vision of their ideal school playground. It demonstrates what is required in play environments to make them inclusive and why neurodivergent children should be included in research. This qualitative research involved two groups of children aged 10–12. Seven ‘predominantly neurodivergent’ (ND) students and six ‘predominantly neurotypical’ (NT) students, across two public, government-run schools in Brisbane, Australia. Play-based workshops used a co-design methodology to create play prototypes, drawings and journals, alongside interactions with a sensory sculpture on the school playground. Guided by the social model of disability and a strengths-based neurodivergent approach, thematic analysis revealed what play features were necessary to support more inclusive play. Participants developed four key design principles from play features that included sensory, social, challenge and nature as priority areas for inclusive playground design. Further, Biophilic, Salutogenic and Prospect-Refuge theories validate what play features are essential to sustain ND children's involvement at play. These elements fostered physical, social, and emotional inclusion, enabling children to play for longer, promoting positive social outcomes. Affordance theory underscores the value of these features in meeting neurodivergent children's needs and enhancing their play experiences. This research highlights opportunities for more inclusive playgrounds and for designers to create environments that support diverse users. 2025-12-01T00:00:00Z http://hdl.handle.net/10453/193474 Title: Dual-stage collaborative optimal allocation of cooling source in ice storage air-conditioning systems considering daily and emergency demand response Authors: Yan, X; Men, Q; Meng, Q; Debnath, KB Abstract: With the increasing pressure on the power grid from peak loads, demand response (DR) has become an important means to alleviate the power supply–demand imbalance and support carbon neutrality goals. In this context, ice storage air-conditioning (IAC) systems, as a key component of DR technology, store ice during off-peak electricity prices and release the ice to provide cooling during peak demand hours. This approach effectively reduces operational costs, balances grid load, and reduces carbon dioxide emissions (CDE). However, existing control methods typically rely on simple electricity price fluctuations and load variations, lacking effective emergency response capabilities for sudden grid load changes. This study presents a dual-stage collaborative optimization framework that synergistically integrates day-ahead scheduling with real-time intra-day control to enhance IAC system operational efficiency. In the day-ahead phase, a multi-objective optimization algorithm is formulated to dynamically allocate cooling loads between chillers and ice-melting processes, achieving simultaneous minimization of energy consumption and CDE. In the intra-day phase, a multi-agent deep reinforcement learning based dynamic control architecture is developed to coordinate efficient regulation under daily demand response (DDR) and emergency demand response (EDR) modes. The results show that under the dual-stage optimization strategy, the system's operating costs were reduced by 15.7 %, and CDE decreased by 15.0 % compared to conventional single-phase strategies. Under EDR conditions, the system participated in load response and obtained compensation benefits, reducing total costs by 81.67 %. The reduction rate remained consistently exceeding 25 %, achieving the expected reduction target, alleviating the burden on the power grid. The indoor temperature increased by no more than 4 °C, ensuring user comfort. This study provides an effective demand-side optimization paradigm for IAC systems DR in smart grid ecosystems. 2025-11-15T00:00:00Z http://hdl.handle.net/10453/193453 Title: EOLD: A reinforcement learning-based energy-optimised load disaggregation framework for demand-side energy management Authors: Wei, Y; Fan, J; Meng, Q; Debnath, KB; Yang, Y; Liu, J; Lei, Y Abstract: Demand-Side energy Management (DSM) is a crucial strategy for balancing electricity supply and demand while enhancing energy efficiency, relying on sufficient data on electricity usage. Non-Intrusive Load Monitoring (NILM) is widely used for DSM strategies, as it effectively identifies the energy consumption of individual devices by measuring total power, significantly enhancing visibility. NILM should prioritise the dynamics of sub-load characteristics under future energy optimisation strategies rather than just historical data. For efficient load disaggregation, it must focus on optimising energy strategies. This study introduces a Reinforcement Learning-based Energy-Optimised Load Disaggregation (EOLD) framework to address this gap. The framework uses load disaggregation for final energy optimisation rather than initial sub-load characteristics. It utilises Reinforcement Learning (RL) to tackle the load disaggregation, with rewards focused on efficient, flexible, or economic energy goals. The Proximal Policy Optimisation (PPO) effectively disaggregates the air-conditioning load of three buildings, demonstrating the capabilities of the EOLD framework in optimising DSM for energy storage systems. The results show the proposed method optimises power curve flattening. It establishes a precise relationship between the main system's design power and the energy storage system's capacity. The framework can also be extended to disaggregate other flexible loads, such as photovoltaics and electric vehicles. 2025-10-15T00:00:00Z