Channeling information from many sources into effective lighting control strategies can make our buildings more energy efficient and comfortable. Algorithms for Advanced Lighting Controls puts all of this data to work, providing a clear picture to optimize indoor lighting and demand response.
The multi-campus project team is investigating how real-time and historical data can be incorporated into advanced algorithms to control lighting systems in buildings. The data comes from both within the building (for example, lighting system power usage, occupancy, and available daylight) and from remote data streams (relaying weather conditions, available power supplies, and real-time pricing from utilities). By amalgamating data on varying occupancy, daylight levels, and electricity rates, the algorithms will adjust electric lighting output continually in individual light fixtures for maximum efficiency.
To make use of many data streams, researchers are using the Simple Measurement and Actuation Profile (sMAP) software environment, developed at UC Berkeley. sMAP is a web service that provides a simple, efficient way to represent a jumble physical data from multiple sources and make it accessible to energy systems. This project is demonstrating how advanced lighting control algorithms can harness this data to vastly improve comfort and energy management in buildings.