Reproducibility is a cornerstone of scientific research, enabling independent verification and validation of empirical findings. The topic gained prominence in fields such as psychology and medicine, where concerns about non - replicable results sparked ongoing discussions about research practices. In recent years, the fast-growing field of Machine Learning (ML) has become part of this discourse, as it faces similar concerns about transparency and reliability. Some reproducibility issues in ML research are shared with other fields, such as limited access to data and missing methodological details. In addition, ML introduces specific challenges, including inherent nondeterminism and computational constraints. While reproducibility issues are increasingly recognized by the ML community and its major conferences, less is known about how these challenges manifest in applied disciplines. This paper contributes to closing this gap by analyzing the transparency and reproducibility standards of ML applications in building energy systems. The results indicate that nearly all articles are not reproducible due to insufficient disclosure across key dimensions of reproducibility. 72% of the articles do not specify whether the dataset used is public, proprietary, or commercially available. Only two papers share a link to their code - one of which was broken. Two-thirds of the publications were authored exclusively by academic researchers, yet no significant differences in reproducibility were observed compared to publications with industry-affiliated authors. These findings highlight the need for targeted interventions, including reproducibility guidelines, training for researchers, and policies by journals and conferences that promote transparency and reproducibility.

The pervasive application of artificial intelligence and machine learning algorithms is transforming many industries and aspects of the human experience. One very important industry trend is the move to convert existing human dwellings to smart buildings, and to create new smart buildings. Smart buildings aim to mitigate climate change by reducing energy consumption and associated carbon emissions. To accomplish this, they leverage artificial intelligence, big data, and machine learning algorithms to learn and optimize system performance. These fields of research are currently very rapidly evolving and advancing, but there has been very little guidance to help engineers and architects working on smart buildings apply artificial intelligence algorithms and technologies in a systematic and effective manner. In this paper we present B-SMART: the first reference architecture for autonomic smart buildings. B-SMART facilitates the application of artificial intelligence techniques and technologies to smart buildings by decoupling conceptually distinct layers of functionality and organizing them into an autonomic control loop. We also present a case study illustrating how B-SMART can be applied to accelerate the introduction of artificial intelligence into an existing smart building.

Smart building technology is gaining ground across Canada, with wider adoption expected over the next decade. It is still relatively early days for Canada's smart building market. Most projects continue to take the conventional route, employing HVAC, electrical, lighting, security and other such systems in their separate silos. But early adopters have begun to look at buildings not as a jumble of interdependent systems, but as the system as a whole, capable of integrating subsystems to improve energy use, guide automation and optimize the wide array of connected components. Smart building discussions have been ongoing in the construction industry for at least 20 years, according to Sam Boyajian, vice-president of Integrated Building Technology at Modern Niagara.

AI refers to'Artificial Intelligence' which means making machines capable of performing quick tasks like human beings. AI performs automated tasks using intelligence. It is a set of algorithms used by intelligent systems to learn from experience. These are the advanced round of algorithms used by machines to learn from experience. Artificial Intelligence technology is currently at this stage. It is self-learning from experience without the need for external data.

AI refers to'Artificial Intelligence' which means making machines capable of performing quick tasks like human beings. AI performs automated tasks using intelligence. It is a set of algorithms used by intelligent systems to learn from experience. These are the advanced round of algorithms used by machines to learn from experience. Artificial Intelligence technology is currently at this stage. It is self-learning from experience without the need for external data.

AI refers to'Artificial Intelligence' which means making machines capable of performing quick tasks like human beings. AI performs automated tasks using intelligence. It is a set of algorithms used by intelligent systems to learn from experience. These are the advanced round of algorithms used by machines to learn from experience. Artificial Intelligence technology is currently at this stage. It is self-learning from experience without the need for external data.

The field of robotics is coming of age. Robotics and artificial intelligence represent the next cutting edge technology to transform the fields of architecture and design. The past decade's surge towards more computationally defined building systems and highly adaptable open-source design software has left the field ripe for the integration of robotics wither through large-scale building fabrication or through more intelligent/adaptive building systems. Through this surge, architecture has not only been greatly influenced by these emerging technologies, but has also begun influencing other disciplines in unexpected ways. The purpose of this book is to provide systems of classification, categorization and taxonomies of robotics in architecture so that a more systematic and holistic body of work could take place while addressing the multifarious aspects of possible research and production.

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With artificial intelligence integrated into building systems and IoT devices, buildings become more than their brick-and-mortar shells. They begin to operate in new ways, creating personalized experiences for their occupants, and providing energy and cost savings for their owners. In the report'Building intelligence into buildings', the Institute of Business Value (IBV) explores the potential of buildings that think for themselves. Below are some of the key learnings and action points. What if buildings owners could see exactly how their building is being used at any given time?

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