Browsing by Author "Alammar, Ammar"

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Investigating the Impact of Adaptive Façades on Energy Performance Using Simulation and Machine Learning
(Cardiff University, 2023-04-05) Alammar, Ammar; Jabi, Wassim; Lannon, Simon
Buildings consume approximately 40% of the world's primary energy, and half of this energy demand stems from space cooling and heating. To meet the targets of designing high performance buildings, intelligent solutions need to be integrated into the design process of buildings to achieve indoor environmental comfort and minimize energy consumption. In particular, the building façade plays a crucial role, as it acts as a separator element that can control the indoor environment and energy performance. This is even more important in buildings with extensive glazing systems particularly in harsh, hot climates. As stated in the literature, buildings are exposed to dynamic environmental factors that change continuously throughout the day and the year. Nonetheless, regardless of the climatic variations, building skins have been typically designed as static envelopes, which are limited in terms of their responsiveness to indoor or outdoor environmental conditions. In contrast, adaptive façades (AFs) are flexible regarding the adaptability of the system to climatic conditions enabling them to respond to short-term changes in the environment. From an environmental viewpoint, it is essential to reduce the energy consumption of buildings and mitigate their environmental impacts. Numerous innovative building envelope technologies have been developed to improve indoor comfort and reduce the environmental impact of buildings during their life cycle. As stated in the literature, AFs can make a major and practical contribution to achieving the worldwide zero-energy building targets and sustainability of our cities. In practice, assessing the performance of AFs during the early stages of the design is still a challenging task due to their time-varying dynamic behaviour. Most current building performance tools (BPS) were originally developed to assess fixed façades where changes to the geometry of the façade are not taken into consideration during simulation. To that end, adaptive systems require a more complex workflow that can correctly predict their performance. This research is intended to assist architects and façade specialists in two main aspects; firstly, an algorithmic framework was developed to predict the energy performance of AFs in the early design stages. The algorithmic workflow creates a link between plug-ins including the Ladybug and Honeybee tools, and Energy Plus for running the simulation with the built- in tool energy management system (EMS) to program a code to actuate the AF system in an hourly time step. The workflow considers the time-varying dynamic behaviour of AFs based on different environmental parameters. The aim is to accurately evaluate the potential of AFs in the energy performance of an office tower. Secondly, by exploring the complexity and limitation of current tools, a novel method is proposed to assess the energy performance of AFs using machine learning (ML) techniques. Two different ML models, namely, an artificial neural network (ANN) and a Random Forest (RF), were developed to predict the energy performance of AFs in the early design stages in a significantly faster time compared to simulation. The surrogate models were trained, tested, and validated using the generated synthetic database by simulation (hourly cooling loads of AF and hourly solar radiation). During the training phase, a hyperparameters tuning procedure was carried out to select the most suitable surrogate model. By comparing the static shading system with AFs in terms of energy consumption, the results confirmed that the AFs were more effective in terms of cooling load reduction compared to static façades where cooling loads were reduced by 34.6%. The findings also revealed that the control scenario that triggered both incident solar radiation and operative temperature in a closed loop mechanism performed better than other control scenarios. Regarding the surrogate models, this research found that ML techniques can predict the hourly cooling loads of AFs with a high level of accuracy in the range of 85% to 99%. In particular, the RF model showed a 17% improvement in R2 accuracy over the ANN model in predicting the hourly cooling loads of AFs.
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