An improved weighted average algorithm with Cloud-Based Risk-Conscious stochastic model for building energy optimization

This paper presents a new approach toward building energy optimization through proposing a cloud theory-based stochastic model that considers the risk that arises due to uncertainty in building cooling and heating efficiency. The main aim of this study is minimizing the annual energy consumption (AEC) in a reduced-order office building by addressing the intrinsic variability of the environmental parameters. One of the key contributions of this work is developing an Improved Weighted Average Algorithm (IWAA), introducing a Dynamic Weight Update Mechanism to further balance exploration and exploitation throughout optimization. The proposed method is evaluated under three situations: (1) CEC-2022 benchmark functions, (2) minimized office building energy optimization excluding risk in uncertain parameters, and (3) stochastic building energy optimization model including risk in uncertain cooling and heating efficiencies. The optimization model is also applied to three different weather conditions to highlight its applicability in varying environmental conditions. The results demonstrate that the IWAA is learned much more effectively than the typical algorithms, such as WAA, PSO, and WOA, by providing more stable and consistent results for lower AEC values. Furthermore, injecting uncertainty into the optimization problem with the help of the cloud theory framework is identified as the most significant factor in getting more realistic and credible energy forecasting. The findings illustrate the strength of the proposed IWAA, which achieves better optimization performance with its incorporation of uncertainties and balancing of exploration-exploitation trade-offs. The model is a strong candidate for real-world energy optimization problems, with potential benefits for the design of sustainable energy-efficient buildings.