AI-enabled smart meters are reshaping energy efficiency

The electricity sector is in a permanent state of transformation. One feature of the current transformation has been the increasing share of renewable (unplannable) electricity in the energy mix. According to Enerdata, the share of renewables in the energy mix worldwide has increased by 10 per cent since 2010, reaching around 30 per cent by 2023. Terms such as microgeneration, electric vehicles and prosumers (individuals who both produce and consume electricity) define a new reality in which traditional utilities need to update their strategies to meet future challenges.

The widespread diffusion of distributed generation from renewable and non-programmable energy sources and the need for storage are rapidly changing the problems that transmission and distribution system operators face in their activities and are demanding a ‘smarter’ network.

The first step in this direction is developing and installing a flexible smart metering architecture for various energy vectors. To date, the smart meters installed in some countries are practically only dedicated to billing improvements.

Smart meters are, therefore, fundamental to future smart grids. Not least because they help ordinary people become better stewards of their energy use. And this is fuelling an explosion of IoT devices in the electricity sector. Between 2019 and 2023, the penetration of smart electricity meters in Europe will increase from 50 to 60 per cent. The corresponding growth in Canada and the USA was from 65 per cent to around 80 per cent. By 2027, Berg Insight predicts that the penetration rate of smart meters will exceed 76 per cent and 90 per cent, respectively, in Europe and North America.

However, the new metering systems must go much further to help transform the way citizens understand and manage their energy consumption. They can also improve energy and market efficiency and contribute to reducing CO2 emissions and pollutants.

For example, understanding current energy consumption baselines is the first step towards achieving corporate sustainability goals. Many companies still face challenges in identifying and dealing with energy waste, often due to old buildings with outdated equipment or a lack of an equipment upgrade strategy. Smart metering systems can collect data from various sources, such as rooftop HVAC units, central power plants, lighting systems, etc. The result? A comprehensive overview that allows you to establish sustainability baselines, identify energy waste, monitor performance and better understand the economic and environmental costs of equipment ownership.

Moreover, AI can simplify the process by aggregating building data and generating reports based on the client’s chosen ESG reporting standards. This not only ensures compliance with sustainability standards but can also make reporting more efficient and accurate.

Smart meters ‘driven by AI’

AI is key to integrating renewable energy, stabilising energy networks and reducing the financial risks associated with infrastructure instability. It acts as a ‘intelligent agent’ behind smart grids, assessing the environment and taking action to maximise a given goal.

The synergy between AI and smart meters is set to revolutionise how we manage and achieve our energy goals. The real-time insights, predictive capabilities and automated optimisation offered by this combination allow individuals and organisations to take control of their energy consumption. In doing so, we reduce costs and contribute to a more sustainable and environmentally friendly future.

Some of the applications of AI and machine learning in smart grids include:

Agility and resilience: Renewable energy generated by new partners such as co-operatives and prosumers is often intermittent and variable. Sensors and automation can identify vulnerable parts of the grid and respond with automated redirection—storing surplus energy during peak generation times and redirecting it during gaps in the flow.

More accurate forecasting: The utility sector faces wide price variability due to changes in consumption. Predictive analytics models can be used to forecast energy loads and renewable energy generation more reliably. By combining advanced metering infrastructure (AMI) data with AI, forecasts are more accurate than traditional approaches.

More sophisticated power failure alerts: The network of sensors, meters and actuators in a smart grid can provide a short ‘last gasp’ signal transmission, including time and date, to indicate a loss of power due to partial or total outages. In addition, the predictive capabilities of AI and real-time data from smart meters can notify operators of outages before they occur. These systems can even differentiate between individual, street and zone outages.

Optimising production: Sensor networks with AI technology in the generation stages can also be used to optimise energy production. Similarly, solar energy benefits from AI tools that increase productivity by predicting solar radiation.

Improving automated switching: AI tools’ ability to predict imbalances in the network and differentiate between a brief power outage and a total outage will soon allow switching protocols to be automated. This will allow utility companies to reroute power or isolate affected areas before serious damage occurs or the outage expands to other areas. These tools are a line of defence that guarantees the safety of essential equipment to isolate and repair faults.

Greater security: Cybersecurity is a key concern for all business sectors. The growing number and complexity of cyber attack strategies pose a risk to existing and new power grids. AI tools can help reduce this risk by detecting network attack capabilities, malware and intrusion and providing network security protection for power systems. In addition, other technologies, such as blockchain, can provide transparent, tamper-proof and secure systems that enable new business solutions, especially when combined with smart contracts.

Harnessing AI to continuously monitor the performance of buildings and equipment will also be key to identifying energy and emissions savings opportunities. Peaks in energy demand put utility companies under great pressure. Using AI and smart meters in homes and offices can help programme, plan, execute and monitor changes in energy demand to ensure that suppliers can meet them. AI can monitor equipment and develop operational improvement solutions and project recommendations to reinforce the sustainability and economic benefits of reducing energy waste.

Other applications

The use of smart gas and water meters has also grown.

Countries such as Italy, the UK, and France have led the way in the adoption of smart gas meters in Europe. Together, they have an installed base of around 47 million smart gas meters, which represents a market share of 84 percent. Outside Europe, the smart gas meter market is growing particularly strongly in countries such as China and Japan.

The smart water meter market, meanwhile, is at an even earlier stage of adoption but is poised for significant growth as utilities continue to upgrade existing infrastructure and implement smart water solutions to become more sustainable and reduce non-revenue water.