AI-driven technology for energy efficiency: An overview of developments in residential buildings
Disciplines
Architectural Technology | Architecture | Sustainability
Abstract (300 words maximum)
With the meteoric growth and development of artificial intelligence in a multitude of industries, new options for application arise in the improvement of smart residential homes for better energy efficiency. With the help of AI-driven technology, energy consumption within a home can be effectively optimized, reducing the impact of residential buildings on the environment and lowering homeowners' energy cost. According to the US Department of Energy, typical smart home energy-efficiency technology consists of installed technology created specifically with local climate and site data already in mind (e.g. previously selected materials, cool roofs, and inherent passive solar design). However, these pre-existing technologies are not sufficiently dynamic and responsive, and also require more inherent building qualities and materials planned well before the construction of the residential structure. For this research we will be using a qualitative methodology, allowing us to further understand AI’s application within residential buildings through real-world examples. Essentially, we will utilize a case study based on current examples of AI and energy-efficiency within smart homes. Current trends in these AI applications indicate the potential in building or retrofitting homes with sensors and electric circuits integrated in the house or in appliances, to track energy consumption in the home in real-time. This would allow for prompt energy optimizations that are accurately adjusted by a localized AI model reducing smog, CO2, and hot air emissions. Additionally, these advancements promise lower energy bills and easier integration into AI-driven smart homes, offering significant benefits for residential buildings.
Academic department under which the project should be listed
CACM - Architecture
Primary Investigator (PI) Name
Pegah Zamani
AI-driven technology for energy efficiency: An overview of developments in residential buildings
With the meteoric growth and development of artificial intelligence in a multitude of industries, new options for application arise in the improvement of smart residential homes for better energy efficiency. With the help of AI-driven technology, energy consumption within a home can be effectively optimized, reducing the impact of residential buildings on the environment and lowering homeowners' energy cost. According to the US Department of Energy, typical smart home energy-efficiency technology consists of installed technology created specifically with local climate and site data already in mind (e.g. previously selected materials, cool roofs, and inherent passive solar design). However, these pre-existing technologies are not sufficiently dynamic and responsive, and also require more inherent building qualities and materials planned well before the construction of the residential structure. For this research we will be using a qualitative methodology, allowing us to further understand AI’s application within residential buildings through real-world examples. Essentially, we will utilize a case study based on current examples of AI and energy-efficiency within smart homes. Current trends in these AI applications indicate the potential in building or retrofitting homes with sensors and electric circuits integrated in the house or in appliances, to track energy consumption in the home in real-time. This would allow for prompt energy optimizations that are accurately adjusted by a localized AI model reducing smog, CO2, and hot air emissions. Additionally, these advancements promise lower energy bills and easier integration into AI-driven smart homes, offering significant benefits for residential buildings.