This technology offers a novel model-predictive distributed control method and a flexible and cost effective IoT implementation architecture for energy saving in building HVAC systems. It is scalable and real-time optimally responsive to changes in a large building that has more than 500 zones via a patented token-based HVAC scheduling strategy. The larger the building, the higher the energy saving potential, due to its novel coordinated HVAC scheduling approach. It is autonomously adaptive to the building operational environment via effective system identification techniques, including machine learning techniques, on real-time data attainable from the proposed IoT infrastructure. It is also occupant-centric, i.e., capable of learning and addressing individual human comfort requirements. The technology is applicable to any new or old VAV (or VRV) HVAC system without any need of major retrofitting on existing HVAC controllers and data acquisition systems, due to its highly flexible plug-and-play implementation architecture. It can also be used to convert an old HVAC system into a highly automated and intelligent one, allowing a building owner to check remotely, via commonly used mobile devices, the status of the building HVAC system and initiate supervisory control for better performance, thus, can enhance the effectiveness of existing BMS and building automation. The technology provider is seeking for industry partners to commercialise the technology.
The technology has the following features:
The technology can be applicable to old buildings that need to improve their energy efficiency and building automation so that they can be converted into smart buildings with low retrofitting costs. The larger the building, the higher the energy saving potential. The technology can also be applied to new buidlings to increase the energy saving potential by using advanced system identification and real-time coordinated optimization and control techniques via augmented plug-and-play IoT implementation infrastructure.
This technology will be useful for building retrofit. The worldwide building retrofit market is expected to grow quickly. According to a survey done by the Rockfeller Foundation in 2012, the building retrofit market in 2012 was about $279 billion, with only 10% old buildings were actually retrofitted. In New York City alone, the commercial building retrofit cost is expected to jump from $235 million in 2020 to $2.2 billion in 2024, and to $18.2 billion in 2030. In China, there were 400-600 billion m2 existing buildings in China in 2015, only less than 10% were rated as energy efficient buildings. The current retrofit cost for residential buildings is also hight, at a rate of about 597.9 CNY/m2 to 1365.1 CNY/m2. In Europe, a recent survey shows that the building retrofit market in Europe was $80.3 billion in 2011 and $151.8 billion in 2020, which represents a 7.4% annual compound increase rate. In Singapore, we have about 500K buildings, including residential and commercial buildings. With the recent plan for 80% of buildings to go green by 2030, the building retrofit market is expected to grow fast. In addition, this technology can also improve energy saving for new buildings, serving as a complement to other more costly building energy saving technologies.