Browsing by Author "Aldawsari, Abdulrahman"

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Evaluating the Effectiveness of Existing AI Models in Energy Management for Smart Facilities and Buildings
(Saudi Digital Library, 2025) Aldawsari, Abdulrahman; Morgan, Peter
This 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.
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