Artificial intelligence is the darling of the tech world, but when it comes to the energy sector, it's much more than just a buzzword. AI has the potential to transform how we produce, distribute, and consume energy. It can optimise grid operations, accelerate the integration of renewable energy, and make our path to net zero smoother and faster. But there's a catch: AI is only as powerful as the data we feed it.
To really unlock AI's potential in the energy transition, we need to focus on capturing large, clean and diverse sets of energy data. Otherwise, we’re just spinning our wheels, letting AI fall short of its transformative promises. So, what are the essential types of energy data we need to supercharge AI? Let’s break it down.
1. Energy Consumption Data
If AI is going to help us optimise energy use, reduce waste, and predict future demand, we need high-quality energy consumption data. This data provides a detailed picture of how much energy is being used, where, when, and by whom.
AI can take this data and analyse it to identify trends, forecast future consumption patterns, and recommend ways to reduce energy use. For example, AI can predict when demand will spike and help grid operators prepare for it, or it can help consumers and businesses cut energy costs by adjusting usage during peak times.
However, this data needs to be real-time and granular. High-level aggregate data isn’t enough. AI needs to understand energy consumption down to the level of individual buildings, devices, and even appliances. If we’re serious about using AI to tackle energy efficiency and demand management, we need to ensure that our consumption data is up to the task.
Up until very recently, this was a pipe dream. But thanks to Ofgem taking the initiative and driving change, we're starting to see a glimmer of hope. The UKs Distribution Network operators (DNOs) have been tasked with publishing high-resolution (or at least high-er resolution) energy consumption data. It's still early days and there are some challenging technical obstacles to overcome but we're really excited about the potential of this dataset.
2. Renewable Generation Data
Renewable energy is the backbone of any serious plan to decarbonise our energy systems. But renewables are unpredictable by nature. The sun doesn’t always shine, and the wind doesn’t always blow. This variability is a challenge for grid operators who need to balance supply and demand in real-time.
That’s where renewable generation data comes in. This data tells us how much energy is being generated by wind, solar, and other renewable sources at any given moment. By feeding this data into AI models, we can predict how much renewable energy will be available in the future and adjust grid operations accordingly.
AI can use renewable generation data to optimise the integration of renewables into the grid, manage storage systems, and even predict the best times to dispatch energy. But to do this effectively, we need high-resolution, real-time data.
As adoption of small-scale renewable energy generation increases, and the majority of roofs are covered with solar PV installations, we need better visibility of historic and future generation. Small scale renewable generation will play an important part in a decentralised, flexible energy system, but if we don't know how much energy is likely to be generated in different scenarios, balancing supply and demand will be incredibly difficult.
3. Low Carbon Technology and Distributed Energy Resources
The future of energy is decentralised. Low-carbon technologies like air and ground-source heat pumps and electric vehicles are proliferating. At the same time, distributed energy resources (DERs), like behind-the-meter batteries and solar PV installations, are becoming more common.
This shift towards decentralisation presents both opportunities and challenges. On one hand, DERs can make our energy systems more resilient and reduce the strain on centralised infrastructure. On the other hand, managing a grid with millions of small, decentralised energy producers is a logistical nightmare.
AI can help us manage this decentralised future, but it needs data on low-carbon technologies and DERs to do so. This data includes information on where DERs are located, how much energy they’re producing, how much they’re consuming, and when they’re connected to the grid.
With this data, AI can optimise the operation of DERs, ensure that they’re used efficiently, and even help with demand response programs. For example, AI could predict when a fleet of EVs will be plugged in for charging and adjust grid operations to accommodate the increased demand. But to make this vision a reality, we need detailed, real-time data on every distributed energy resource out there.
The challenge comes from the lack of visibility of low carbon technologies installed today. With adoption set to increase rapidly over the coming years, the problem is only going to increase. When we spoke to the DNOs, they estimated that they had at best 50-60% visibility of installed low carbon technologies in their region.
The reason for this gap in knowledge is due to it being optional for the consumer or installer to notify the DNO following an install, rather than it being a mandatory step. Only in the case of solar, where it's a requirement to receive grant funding, is this notification regularly completed.
4. Weather and Environmental Data
When it comes to energy consumption and generation, weather and environmental data are absolutely critical. Both depend almost entirely on the weather, and any AI model that tries to optimise the grid without considering weather conditions is doomed to fail.
Weather data is more than just a nice-to-have, it’s a must-have for AI in the energy sector. AI can use this data to forecast renewable energy production, predict demand spikes (think heatwaves and cold snaps), and optimise storage and backup systems.
The key here is accuracy and timeliness. We need real-time weather data that’s hyper-localised to be truly effective. Global forecasts won’t help much when we’re trying to optimise a solar farm in a specific region or manage the output of a wind farm based on local wind conditions.
5. Grid Infrastructure Data
Finally, we come to grid infrastructure data, the backbone of our entire energy system. If we don’t know what’s happening on the grid, we can’t manage it effectively. AI needs detailed information about the physical infrastructure of the grid, including transmission lines, transformers, substations, and even the health of individual components.
Grid infrastructure data allows AI to optimise the operation of the grid, predict and prevent outages, and ensure that energy flows smoothly from producers to consumers. AI can also use this data to help prioritise maintenance and upgrades, reducing the risk of unexpected failures and blackouts.
But again, this data needs to be real-time and granular. AI can’t work with outdated or incomplete information. We need to know the status of every component in the grid at all times if we want to use AI to keep the lights on and the energy flowing.
Where do we go from here?
The promise of AI in the energy sector is huge, but it won’t happen on its own. We need to get serious about collecting, managing, and sharing the right kinds of data. Energy consumption data, renewable generation data, low-carbon technology and DER data, weather and environmental data, and grid infrastructure data are all essential pieces of the puzzle.
The bottom line is this: if we’re serious about accelerating the clean energy transition with AI, we need to invest in the right data infrastructure. That means real-time, high-resolution data from every corner of the energy system, and making sure it gets into the right hands, with as little friction as possible.
