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Digital technologies have rapidly transformed our world. Over the last two decades, advances like the cloud, AI, and IoT have supported and accelerated the United Nation’s sustainable development goals. While hailed as a great equaliser, digitalisation also has a darker side – one that’s beginning to garner more and more attention.
Requiring vast arrays of globe-spanning networks, the digital revolution devours materials, space, natural resources and energy. Its gargantuan infrastructures use astounding rates of natural resources. Yet, with little to no direct regulation or oversight, it’s up to the technology industry to get its own greenhouse gases in order.
The sense of IoT
Internet of things (IoT) refers to a network of software applications and physical objects such as appliances, machinery and sensors that work together to automate and streamline processes. Interconnected devices send and receive data and instructions via wired and wireless connections.
IoT devices allow for automation and control through a set of actions dependent upon predefined rules or real-time data. These devices can significantly improve energy efficiency in the following ways.
Sensors: Smart metres monitor electricity usage within data centres. By capturing real-time data, they empower operators to optimise energy consumption. Temperature sensors allow cooling systems to adapt dynamically to environmental conditions, preventing unnecessary energy expenditure.
Smart connectivity: Smart grids intelligently manage energy distribution. By analysing demand and supply patterns, they ensure efficient allocation. When demand spikes, smart grids respond swiftly to minimise wastage. Similarly, smart lighting adjusts brightness based on occupancy and available natural light to cut down on energy waste.
Data analysis: IoT-generated data informs predictive models. By analysing equipment behaviour, it predicts when components might fail so proactive maintenance can be scheduled, reducing downtime. Data-driven insights also include energy usage patterns, allowing load balancing so equipment runs at peak capacity.
Intelligent energy use with AI
AI research centres on traditional goals, including reasoning, knowledge representation, planning, learning, natural language processing, perception and support for robotics. AI algorithms are the backbone of AI, enabling machines to analyse data, perform tasks and make decisions.
AI algorithms form a subset of machine learning, guiding computers to learn and operate independently. AI technology is a widely used tool in digital transformations, such as search engines, user recommendation systems, voice-activated assistants and self-operating equipment.
AI plays a pivotal role in enhancing energy consumption through various mechanisms including the following.
Energy optimisation: AI algorithms analyse historical data to foresee peak demands. These predictive insights enable intelligent resource allocation, thus preventing overloading or underutilising supplies. Likewise, machine learning models fine-tune systems like heating, ventilation and air conditioning to minimise energy waste.
Workload management: AI-driven workload scheduling ensures energy-intensive tasks happen off peak. Peak hour efficiency avoids unnecessary energy spikes by ensuring equipment like servers works at optimal capacity. Dynamic resource allocation regulates power based on real-time demand, meaning machinery is never left idling and draining energy.
Carbon footprint reduction: AI-powered algorithms find the most fuel-efficient way to operate. For example, route optimisation of fleet transport cuts fuel consumption, translating to fewer emissions. In the same vein, AI analyses supply chain data to rationalise operations. Smart supply chains span the whole ecosystem, from reducing emissions to sustainable logistics and eco-friendly materials.
Every cloud has a green lining
Cloud computing eliminates the need for self-managed physical data centres or servers, so users only pay for what they use. Moreover, cloud providers are responsible for keeping the infrastructure well-maintained, secure and up to date.
Cloud computing helps reduce energy waste through a range of approaches including the following.
Server consolidation: Unlike traditional, individually owned and dedicated server setups, cloud computing allows providers to pool resources and consolidate workloads onto fewer servers. Fewer physical servers mean less power consumption and a smaller carbon footprint.
Data centre efficiencies: The sheer scale of cloud computing means data centre owners and operators can invest in greener hardware and infrastructure. Advanced cooling systems, for instance, supplant energy-intensive air conditioning. Similarly, renewable solar, wind and hydroelectric power can replace fossil fuels.
Informed decision-making: Cloud computing isn’t just about hardware; it’s about making smart choices. Predictive insights and resource allocation capitalise on data analysis to ensure dynamic adjustment based on real-time usage. In this way, machines ramp up in anticipation of need and rest during lulls, lessening energy wastage.
The holy trinity of sustainability
Individually, IoT, AI, and cloud computing are effective drivers of sustainable digitalisation. But the true magic happens when they’re combined. Cloud platforms provide computing and storage power, while AI algorithms analyse and provide insights about the data collected from sensors and IoT devices. In this way, the trinity enables informed decision-making, intelligent workflows and efficient operations, effectively reducing energy consumption at scale.
Sustainability flows from the dynamic intersection of technologies. In practice, the convergence of IoT, AI and cloud computing has led to innovative solutions that are reshaping the digital landscape.
One example is a fully managed service that brings together a broad set of tools to enable high-performance, low-cost machine learning for any use case. Operating within eco-conscious digital environments, it prioritises the reduction of emissions.
When deployed in data centres, it takes data from IoT devices like smart metres, temperature sensors and industrial equipment from different locations via cloud-hosted networks to provide real-time streams of crucial information on energy consumption patterns. Its AI algorithms adjust server workloads based on demand to optimise power usage, scaling up and down as needed.
Another is a cloud-based service for building, training and deploying machine learning models. It is at the forefront of reframing model performance metrics to include energetic, computational and environmental costs in addition to traditional measures like accuracy, latency and throughput.
In line with the emerging field of green AI – which challenges the AI community to analyse and mitigate the carbon impact of their models – such solutions expose the costs at scale. Working in partnership with the Allen Institute for AI and the Green Software Foundation, companies responsible for developing these tools are driving toward a portfolio of green AI tactics that promote sustainable development and applications of machine learning.
Getting our own greenhouse gases in order
Dynamic solutions aren’t just about utilising technologies to manage resources efficiently – they, and other innovations like them, are about shaping a greener digital ecosystem. An ecosystem where IoT, AI and cloud computing leverage sensor data, smart connectivity and data analysis to help large-scale IT lower its carbon footprint. An ecosystem where the technology industry autocorrects its sustainable digitalisation problem.
Patrick Strauss is chief Intelligent environments solutions officer at GlobalLogic, a digital engineering firm. He has more than 30 years of commercial, technical and consulting experience in emerging tech, supply chain and digital transformation.
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