How AI is helping companies manage the energy transition

Three major developments facing companies today

Energy transition, driven by international agreements and national laws, is well underway. In 2015, the Paris Climate Agreement set the main goal: to limit global warming to 1.5 degrees Celsius (compared to the average temperature of the pre-industrial period from 1850 to 1900).

While this is a challenge for the whole of society, companies have a central part to play. Yet the associated trend towards sustainability also offers companies an opportunity to improve their image and increase their own market opportunities.

In some cases, companies need to completely reorganise their energy usage in order to achieve this. But how? In an article for LinkedIn, Ellen Smeele, associate at SET Ventures, identifies three key developments.

• Many companies are now increasing on-site power generation and installing solar panels (PV), electric vehicle chargers, heat pumps or batteries for their own consumption. This, according to Smeele, shifts the energy load profiles of companies and invalidates the basis on which traditional energy suppliers have calculated their offers.
• Energy suppliers are also suffering economically due to the many ongoing international crises. As a result, they are shifting their market risk to their clients more than ever before and withdrawing from their traditional role as energy risk managers. As a result, trade and industry customers are often left to their own devices and forced to rethink their energy procurement strategies.
• Looking at the combined impact of supply and demand for renewables, we’re seeing higher volatility with wider spreads. Smeele’s concerns are echoed by UK market research institute Cornwall Insight, which specialises in helping companies tackle the net zero transition. According to the company’s Benchmark Power Curve, which monitors energy investment and operational decisions across a 30-year period, power prices could become increasingly volatile from 2026. There’s likely to be significant seasonal variation, with the difference between prices in the summer and winter standing at £95/MWh in 2026 and rising to nearly £120/MWh by 2030. To manage this volatility, the UK must develop long-term strategies to manage its overreliance on imported energy and cope with changes in energy production and unstable economic, geopolitical and ecological systems.
   

Managing the energy transition: how can AI help?

The energy transition is forcing both suppliers and companies to reorganise their procurement strategies and energy usage. This is where digitalisation and artificial intelligence can provide valuable services. Here are a few examples.
 

Optimisation of energy systems

Smart grids can dynamically control power distribution. Machine learning algorithms are used to predict energy demand and adjust performance accordingly, helping to avoid waste. In addition, AI can detect anomalies and inefficiencies in systems or flag up preventive maintenance.
 

Rationalisation of industrial processes

In manufacturing and logistics, AI has huge potential for minimising energy consumption. Intelligent robotics automates tasks, while machine vision takes care of quality control and detects errors at an early stage. This eliminates unnecessary manual work and reduces material waste and emissions.
 

Intelligent building management

AI-powered building management systems track occupancy, weather, usage patterns and more to efficiently regulate lighting, heating and cooling in commercial buildings. Motion sensors and predictive functions save electricity when rooms are not in use. By combining and fine-tuning these technologies, energy consumption can be optimised in a lot of commercial buildings.
 

Improved demand response

Energy suppliers can use AI to analyse data and predict peaks in energy demand. This means that targeted demand response programs can be implemented in order to flatten demand curves.
 

Improvements in logistics and transport

AI enables supply chain and logistics service providers to optimise routes and loading capacities. It helps transportation companies reduce fuel waste by analysing traffic patterns and deploying vehicles where they are needed.
 

Energy audits and recommendations

AI applications automate energy audits for buildings and suggest bespoke improvements. They can take into account insulation requirements, occupancy patterns and local weather conditions.
 

In conclusion: managing the energy transition with AI

AI has enormous potential for optimising energy consumption and conserving resources in several industries. Intelligent systems can streamline processes, improve sustainability and reduce consumption on a large scale. But for AI to deliver on its promise, precise development and rigorous management are crucial.

Companies need to build robust models based on high-quality data, set well-defined AI boundaries, implement strict security and privacy policies, ensure transparency and actively minimise their carbon footprint.