The building sector accounts for a significant portion of the global energy consumption and carbon dioxide (CO₂) emissions, making it a critical area for improving energy efficiency. In Africa, the rapid energy demand and costs have further emphasized the urgency of developing effective solutions for reducing building energy use. This paper presents a comprehensive review of data-driven and physics-based modeling approaches for forecasting and optimizing energy performance in non-domestic buildings. The review highlights the evolution of statistical models, classical machine learning methods, deep learning, and hybrid approaches across various application scenarios. Emphasis is placed on the role of data pre-processing techniques, including data fusion and transfer learning, as strategies to address data limitations and improve model generalization. Furthermore, the study evaluates the strengths and limitations of different modeling methods in terms of accuracy, scalability, and applicability in real-world contexts. By integrating insights from recent literature, this paper identifies key research gaps such as the need for standard datasets, physics-informed hybrid modeling, and policy-oriented frameworks. The findings aim to guide building managers, policymakers, and researchers toward adopting robust data-driven solutions that enhance energy resilience, reduce operational costs, and support environmental sustainability in the built environment. The review also justifies the importance of these models for practical applications like energy benchmarking, retrofit planning, and CO₂ reduction, providing a clear link between research and industry implementation.

Non-orthogonal multiple access (NOMA) represents the latest addition to the array of multiple access techniques, enabling simultaneous servicing of multiple users within a singular resource block in terms of time, frequency, and code. A typical NOMA configuration comprises a base station along with proximate and distant users. The proximity users experience more favorable channel conditions in contrast to distant users, resulting in a compromised performance for the latter due to the less favorable channel conditions. When cooperative communication is integrated with NOMA, the overall system performance, including spectral efficiency and capacity, is further elevated. This study introduces a cooperative NOMA setup in the downlink, involving three users, and employs dynamic power allocation (DPA). Within this framework, User 2 acts as a relay, functioning under the decode-and-forward protocol, forwarding signals to both User 1 and User 3. This arrangement aims to bolster the performance of the user positioned farthest from the base station, who is adversely affected by weaker channel conditions. Theoretical and simulation outcomes reveal enhancements within the system’s performance.