Pervasive Computing = Pervasive Efficiency
Homes and commercial buildings, and their construction, account for nearly one third of global emissions and, despite substantial progress, average efficiency remains relatively low. The U.S. EPA estimates that 30% of energy delivered to commercial buildings gets wasted1 because of insulation gaps, unmonitored lighting, and appliances drawing power while in standby mode.
By 2050, the global stock of floor space is expected to double.2 Air conditioning is also on the rise: left unchecked, cooling will consume as much power in 2050 as China and India do today.3
New building codes and technologies will be needed. Success will also require minimizing disruption and cost while implementing safeguards for privacy and security.
Digital decarbonization for buildings is largely focused on three areas:
Replacing existing gas appliances and fixtures with electric. Natural gas, which heats 85% of homes in the U.K., will be phased out in favor of electric heat pumps starting in 2025.
Deploying AI, IoT, and cloud and edge services to precisely monitor and manage lights, HVAC, and other energy-consuming fixtures.
Installing microgrids, energy storage, and renewables. Universities have become early incubators for microgrid deployments.
5G Buldings. Lenovo is experimenting with 5G microcells built around Arm-based silicon for functions such as security, energy management, occupant health, and safety. Lenovo is also testing 5G for optimizing factory production.
Refrigerators are the second largest consumers of electricity in homes, accounting for 7-13% of the total. While manufacturers have achieved steady efficiency gains since 1990, current technologies are reaching a plateau. Power-saving alternatives such as better vacuum insulation panels could add 30% to retail prices.
Arm partner Arçelik, a global appliance manufacturer, is adding AI to standard refrigerators – using their existing computing resources – to conserve energy. The company developed a lightweight Reinforcement Learning (RL) algorithm for Arm Cortex-M processors that analyzed in-home behavior – not reams of training data – to balance compressor speed thus reducing power consumption and continually adjusting for the residents' daily patterns.
It found such a system could reduce power by up to 10% without adding to the bill of materials. Deployed across refrigerators in Europe, it could save enough power to shut nine small coal-powered power plants.
Arçelik is also looking into ways to leverage AI to improve industrial and commercial refrigerators. The RL model could also have application in air conditioning – with big potential for emissions reduction.
Waste Not: Arçelik also found RL could keep food fresh around 10% longer by moderating temperature fluctuations. Approximately one third of food gets wasted annually. If food waste were a nation, it would rank third in GHGs (8% of the total) and consume water equivalent to the annual discharge of the Volga.4
Computer vision and AI are increasingly being added to cameras for city planning, supplement elder care and reduce traffic. An estimated 770 million public-facing cameras already operate and the market is growing by 13% per year. 5,6
But smart cameras also have a significant carbon footprint. A 1080p smart camera operating at 30 frames per second can generate 2GB of data per hour7 or 17.5TB per year. Worldwide, that translates to 13.5 zettabytes per year, not including the data generated by deeper AI analysis.
Deploying AI at the edge (see chart) can cut emissions by 42%.8
Edge AI
20 cameras
CO2 avoided (770M cameras)
