Among the United Nations’ Sustainable Development Goals is a commitment to provide clean, affordable energy. In 2019, we will use advances in artificial intelligence (AI) to move towards achieving this.
AI creates opportunities to bridge the physical world with the digital, enabling the development of technology that can tackle real-world social, economic and environmental challenges. It will increasingly be used to make distributed energy possible at scale, something that is critical for decarbonising the power grid, expanding the use of (and market for) renewables and increasing energy efficiency.
AI will do this by enhancing the predictability of demand and supply for renewables, improving energy storage and load management, assisting in the integration and reliability of renewables and enabling dynamic pricing and trading, creating market incentives.
AI-capable “virtual power plants” will integrate, aggregate and optimise the use of solar panels, microgrids, energy-storage installations and other facilities. Distributed energy grids will be extended to incorporate new sources such as AI-enabled “solar roads”, which have already been piloted in France and China. These roads can do more than just harness energy from the sun. They can perform “intelligent” functions, such as melting snow by generating warmth, or adjusting traffic lanes based on vehicle flow, reducing congestion and pollution.
Real-time smart grids will also have a substantial impact on energy consumption. Google, for example, has used AI to cut power use in its data centres by 40 per cent with DeepMind’s reinforcement-learning algorithms. It uses multiple separate meters to measure the energy used by its centres’ cooling systems and that used by computing equipment, aiming to reduce power usage efficiency (PUE). A PUE of 2.0 means that, for every watt used by its computing systems, another is used by cooling equipment. The aim is to get as close to a PUE of 1.0 as possible.
Smart city data – such as energy and water consumption and availability, traffic and pedestrian flows and weather conditions – has the potential to reduce the need for costly additional infrastructure and cut pollution and congestion.
In Beijing, IBM’s Green Horizons initiative, for example, is combining machine learning with IoT, harnessing data from air-quality stations, traffic systems, weather satellites, industrial activity and even social media to develop weather and pollution forecasts for up to 10 days ahead. And AI-enabled smart grids will also be critical for the management of fast-growing emerging cities, and are already being piloted in Brazil and the Philippines.
The challenge, however, will be to create sufficient regulation to ensure the security and integrity of software, and to clarify issues such as the ownership and control of intellectual-property rights – essential if we want to unlock investment and innovation. It is not yet clear how regulators will respond to the growth of smart grids in 2019 but the hope is that we will see the development of national “responsible technology” policies that will set clear parameters for technology innovators and ensure that projects align with international frameworks such as the UN’s Sustainable Development Goals.
In 2019 we will see more AI systems, embedded in machines, providing solutions to society’s most pressing environmental challenges – not only how best we can use energy, but also issues such as biodiversity, ocean health, water management, air pollution and, ultimately, the sustainability of life on the planet.
Leanne Kemp is founder and CEO of Everledger
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This article was originally published by WIRED UK
