The energy consumption for a building is driven by numerous factors. Artificial intelligence has proven to be an advantageous option that enables the administrator to control energy consumed by the building.
A well-renowned organization, Nest, provides intelligent thermostats that learn how the buildings regulate the light and temperature conditions. Using this solution, multinational professional services firm, Arup, has delivered energy efficiency at the Victoria & Albert Museum. They developed a system for improving the heating, ventilation and air conditioning controls. This was accomplished using model predictive control (MPC) and machine learning capabilities, with the prime motive as lowering the energy consumption and creating a conducive ambiance in the gallery by controlling the internal environment.
In the European Galleries project, among the first projects to have an MPC, one year worth of data was collected with over 60 variables at the one-minute interval, which formed the base for predictive controls. The system understands the building’s energy consumption based on datapoints which include air humidity, intake of air and temperature to control these conditions. This is in sharp contrast to the systems which only react to the existing conditions.
This approach can be used for other projects and is expected to support the industry’s goal to ensure better buildings that are intelligent and empower to increase energy efficiency.