Adam Sroka: Why the Scottish Energy Sector Needs a Digital Backbone

A Guest Article By Dr. Adam Sroka, CEO & Co-Founder of Hypercube Consulting

Scotland’s renewable sector is booming. In 2024, the nation generated 38.4 TWh of renewable electricity, marking a 13.2% increase over 2023, and reached 17.6 GW of installed capacity – a 14.3% rise.

The industry now supports over 47,000 jobs and contributes £15.5 billion in economic output.

Yet, this success brings new challenges. In the first six months of 2025, Northern Scotland was responsible for the majority of grid curtailments across Great Britain – around 4 TWh of wind power, representing 86% of total curtailed output and 76% of related costs.

That volume of unused clean energy would have been sufficient to meet every Scottish household’s electricity demand for 6 months. At the Seagreen wind farm, 71% of expected output in 2024 was curtailed, resulting in £65 million paid in constraint costs.

Curtailment is fundamentally a grid infrastructure challenge. Transmission upgrades are essential to move power from where it’s generated to where it’s needed. Yet waiting for new pylons and substations alone is not enough. In the meantime, operators must find ways to extract the greatest possible value from every megawatt that does reach the system. That is where data and AI come in.

Why clean power needs a data strategy

Every turbine, solar array, and battery produces torrents of information: SCADA readings, weather forecasts, price signals, state-of-charge data, maintenance logs. These datasets often remain siloed – engineers see operations, traders see markets, compliance teams see regulations – with the bigger picture lost in the gaps.

This is where data strategy and AI come together. By building a digital backbone that unifies these streams, operators can move from reactive to proactive – and even predictive – decision making. That shift unlocks a series of high-impact applications across the sector:

Predictive maintenance

Unplanned downtime is one of the most significant risks facing Scotland’s renewable sector. When major components fail – whether in a turbine, a solar inverter, or a grid-scale battery – the impact extends far beyond the repair bill. The combined effect of logistics, specialist labour, and lost output can escalate into substantial costs, eroding both project economics and system reliability.

With battery energy storage revenues now averaging £92,000 per MW per year in the UK, even short periods of downtime translate directly into lost income. For a 50 MW site, just a couple of days offline can mean tens of thousands of pounds left on the table.

Machine learning changes the equation. By analysing historical SCADA signals, vibration patterns, and other performance data, AI models detect early signs of deterioration long before failure. Maintenance shifts from reactive to predictive, protecting both individual projects and Scotland’s collective renewable output.

Forecasting & optimisation

Weather variability remains one of the hardest challenges for renewable operators: a sudden drop in wind speeds or unexpected cloud cover can wipe out expected output, while stronger-than-forecast gusts may drive excess generation straight into curtailment.

AI-enhanced forecasting is transforming how operators respond. By combining high-resolution weather data, asset performance history, and live market signals, machine learning models deliver more accurate, adaptive forecasts. Instead of static predictions, these systems continually refine themselves, learning from deviations and improving performance over time.

Flexitricity, the UK’s first demand response aggregator, illustrates what this looks like in practice. Its virtual power plant connects over 1.1 GW of distributed assets across Great Britain. By automating its forecasting pipelines with machine learning, the company reduced deployment cycles from weeks to hours, enabling thousands of model experiments each year and unlocking new strategies for trading and optimisation.

Trading & market participation

Flexibility markets are becoming central to Scotland’s renewable economy. These are the mechanisms that pay energy assets to increase, reduce, or shift their output in response to grid balancing needs. Instead of relying solely on gas plants to fill gaps, batteries, co-located renewables, and even industrial consumers can now step in to provide this service – earning new revenues while supporting grid stability.

Trading into these markets is no longer just about having capacity; it’s about making decisions at speed. AI helps by processing forecasts, grid conditions, and price signals in real time, allowing algorithmic engines to adjust bids as circumstances change. Strategies become adaptive and continuously improving with every market cycle.

The lesson, however, is that AI is only as effective as the data behind it. As highlighted in recent sector analysis, poor-quality inputs – gaps in telemetry, incomplete weather data, or inconsistent market feeds – lead to unreliable outputs. Building robust data pipelines and governance frameworks is what makes automated trading credible, compliant, and profitable.

AI governance & compliance

Just as forecasting and trading rely on clean, connected datasets, governance depends on the same backbone. 74% of energy and utility stakeholders report having implemented or are considering AI, but without structure, it introduces as many risks as benefits. Bias, data leakage, and “shadow AI” use outside official channels are now recognised by Gartner among the top enterprise risks for 2025.

Regulators are raising the bar. Ofgem’s Ethical AI guidance, the UK’s Code of Practice for the Cyber Security of AI, and the ICO’s strategy on AI and biometrics all demand greater transparency and accountability. At the same time, the pace of model development – three major releases from Anthropic alone in under a year – shows how fast risk profiles evolve.

This is where compliance automation comes in. By embedding AI use within auditable frameworks, energy organisations gain both speed and assurance: models can be deployed safely, outputs validated, and governance aligned with regulatory requirements.

In the same way predictive maintenance and forecasting protect physical assets, structured compliance safeguards digital operations, ensuring AI is reliable, transparent, and fit for Scotland’s regulated energy sector.

Scotland’s next advantage: Data and collaboration

Scotland already knows how to build world-class renewable projects. The real opportunity now is to treat data with the same importance as physical infrastructure. If we get that right – integrating intelligence into the way we build, trade, and operate – we not only solve technical challenges, we create an ecosystem that’s resilient, profitable, and genuinely collaborative.

About Hypercube

Hypercube began in Glasgow with a simple conviction: in the energy transition, data is the most valuable asset. We are a bootstrapped startup bringing together cloud, data and AI expertise with deep energy domain understanding.

From day one, our focus has been on helping operators turn raw energy data into a competitive edge. That means:

• Strategy: practical AI, data, and technology roadmaps aligned with commercial goals
• Build: energy-focused AI and technical solutions engineered for impact
• Support: from project augmentation to ongoing infrastructure monitoring.

On the Hypercube Energy Podcast, we amplify diverse perspectives on how data and AI are reshaping energy. Join Hypercube’s Beyond Energy Community for invitations to exclusive events, leader roundtables, webinars, on-demand videos, and the Beyond Energy newsletter – your access to unique energy technology insights.