Determining an optimal sensor system for smart buildings with uncertain energy supply and demand

The increased smartness of buildings and new technology are expected to mitigate the impact of climate change through renewable energy usage, efficient energy management, and greenhouse gas emission reduction. Using internet of things, sensors, and data analysis, energy management can substantially optimize the use of energy and balance energy supply and demand. However, a distributed sensor system is complex and needs to be located well to make the system efficient. The present study proposes a continuous approximation approach to determine the number of sensors required and the location to set them up in a building as well as the smartness level of each sensor while minimizing the total network cost. We also conducted a numerical analysis to illustrate the model and obtain management insights. Interestingly, in our example, we found that the basement floor of a building should have more sensors installed than other floors.

Automated summary

The increased smartness of buildings and technological advancements aim to reduce the impact of climate change by implementing renewable energy, efficient energy management, and greenhouse gas emission reduction. By using the internet of things, sensors, and data analysis, energy management can optimize energy usage and maintain a balance between energy supply and demand. However, it is crucial to accurately determine the optimal number and placement of sensors in a building for an efficient distributed sensor system.