Saudi Cultural Missions Theses & Dissertations
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Item Restricted Evaluating the Effectiveness of Existing AI Models in Energy Management for Smart Facilities and Buildings(Saudi Digital Library, 2025) Aldawsari, Abdulrahman; Morgan, PeterThis project evaluates the practical effectiveness of existing artificial intelligence (AI) models used in energy management systems for smart buildings and microgrids. While the academic literature is rich in high-performing algorithms, little is known about how these models function under real-world constraints such as data availability, system integration, and operator interpretability. The research focuses on four main AI model types: deep learning models such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU); tree-based models including Random Forest (RF) and Gradient Boosted Trees (GBT); hybrid models combining convolutional neural networks (CNN) and support vector regression (SVR); and reinforcement learning approaches, particularly Proximal Policy Optimisation (PPO). A structured evaluation framework was developed using three pillars: technical performance, operational feasibility, and deployment readiness. Each model was assessed using peer-reviewed results and case studies, with comparative analysis across forecasting accuracy, training demands, interpretability, and integration ease. The findings revealed that deep learning models, particularly LSTM and GRU, excelled in forecasting accuracy but were resource-intensive and opaque to non-specialist users. Tree-based models such as RF offered greater transparency and were easier to deploy but had lower accuracy in complex, time-dependent scenarios. Hybrid models demonstrated the highest accuracy but required significant tuning and maintenance. PPO-based models were effective in dynamic systems like microgrids but presented challenges with explainability and reward design. Federated learning approaches showed promise in decentralised or privacy-sensitive environments, although the results were mixed and highly context-dependent. Key deployment barriers include data quality gaps, limited technical expertise, and poor interoperability with legacy building management systems. Case studies reinforce the view that no model is universally optimal; effectiveness depends on how well a model aligns with the operational environment. For example, interpretable models may be more suitable in public-sector buildings, while advanced reinforcement learning may be better suited to complex, high-investment infrastructure. The study concludes that successful adoption of AI in energy management requires more than technical optimisation. It demands models that are accurate, explainable, and compatible with the real conditions of the buildings they serve. Recommendations include selecting models based on a balance of accuracy and interpretability, planning for model retraining, addressing integration barriers early, and investing in region-specific validation to ensure broader applicability.7 0Item Restricted Impact of Artificial Intelligence Integration in Emergency Department Triage on Waiting Times: A Systematic Review Compared to Conventional Practices in ED Triage.(The University of Sheffield, 2024-09) Alhazmi, Mohammed; Miles, JemieBackground: The global issue of increased patient waiting times in healthcare facilities is a pressing concern, as it can lead to significant patient harm due to delayed access to healthcare. This research proposes the integration of artificial intelligence into emergency department triage systems as a solution to mitigate this issue. Aims: To evaluate the impact of integrating Artificial Intelligence (AI) support tools on waiting times in Emergency Departments through a systematic review of existing literature. Design: A thorough systematic review of the literature was conducted by searching electronic databases and internet search engines, including ScienceDirect, Springer, and PubMed, as well as reference lists. Studies published from January 1, 2019, to May 25, 2024, were included. Articles that did not pertain to AI, interventions that were irrelevant to emergency departments (EDs) or did not provide outcomes related to reducing waiting times either directly or indirectly, or evaluation data were excluded to ensure the quality and relevance of the included studies. Results: The analysis included ten peer-reviewed journals published after January 2019 on integrated Artificial Intelligence (AI) with emergency department triage. Recent findings suggest that integrating artificial Intelligence (AI) models into the emergency department (ED) triage processes can hold significant potential for reducing overcrowding and minimising wait times. Some studies have found that AI reduces waiting times by between 20 seconds and 30 minutes. However, a study found AI to increase waiting times for categories 3 to 5 by 2.75 to 5.33 minutes. Conclusions: This review has highlighted AI's potential to bring innovative solutions to emergency department settings. Implementing these AI-driven solutions has shown promise in enhancing healthcare delivery in the emergency department. However, further research is crucial to refine these models and ensure their practical application, underscoring the importance of continued involvement in the field.105 0