The Swiss electricity industry is moving into a new reality. With the massive build-out of photovoltaics, rising intraday volatility and Swissgrid's single-price imbalance mechanism in force since 1 January 2026, forecast quality is becoming a direct economic factor. What used to be a technical side process is increasingly becoming a lever for trading margins, balancing-energy costs and operational stability.
Britain has already gone through this transition. There the «Market-wide Half-Hourly Settlement» (MHHS) reform forces suppliers to predict precisely, for every half hour, how much electricity their customers actually consume. The same principle applies in Switzerland, here on the finer quarter-hour grid. Errors become financially visible immediately.
Over the past eighteen months CentraLogic in Britain — with whom NDG has jointly evaluated transfer to the Swiss market — has built an AI-driven forecasting infrastructure: in production, regulatorily relevant and tested under real market conditions.
The central question is therefore no longer whether AI forecasting models will become relevant in the electricity industry. It is how fast the Swiss industry can build the necessary capabilities.
Why forecasting becomes a Profit-&-Loss lever
Four structural developments are hitting the Swiss energy industry at the same time:
- The solarisation of the grid is changing load profiles and increasing residual-load volatility.
- Hydropower is becoming a strategic flexibility reserve in the European market.
- Coupling with EPEX and neighbouring countries is increasing intraday dynamics.
- Since 2026 Swissgrid charges balancing-energy deviations directly to balancing groups via a single-price mechanism.
This makes forecast accuracy measurable on the P&L.
The challenge does not only affect large corporates. Around 600 distribution network operators in Switzerland (predominantly integrated municipal and communal utilities that also supply energy) face similar problems, often with limited staff resources and heterogeneous data landscapes. The Digital@Utility 2026 study by Kearney et al. shows that around 70 percent of AI initiatives in the energy industry remain stuck at pilot stage and rarely move into productive operation. Many companies have formulated AI strategies, but productive machine-learning systems with clear KPIs remain the exception. The biggest hurdles are not missing ideas, but missing data infrastructure, talent and operational capacity.
What was actually built in Britain
The British platform is not based on a single «super-model» but on several specialised components.
Load forecasting works with separate models per customer segment: households, SMEs, heat pumps, electric mobility or industrial consumers. The reason is pragmatic: a single national model looked good on the averages but systematically mispredicted precisely the critical segments.
Price forecasting in turn deliberately separates day-ahead, intraday and balancing-energy models. Attempts with shared neural networks appeared more efficient but worsened predictions exactly where price errors were most expensive.
Price forecasting today works as a hybrid model: roughly 70 percent machine learning (ML) and 30 percent fundamentals-based sensitivity models for gas, CO₂, generation mix and cross-border flows. The combination of the two model approaches is continuously corrected for systematic biases. The reason is sobering and simple: pure ML models win the average week but often lose during market phases with structural breaks. Yet those very phases are the most expensive.
Quantile regressions were also introduced to produce not just point forecasts but robust confidence bands. Particularly during cold spells or extreme weather, classical assumptions turned out to be too optimistic.
Another key insight: every single forecast must be stored permanently. Only the systematic archiving of predicted and later actual values enables regulatory traceability, true retrospective validation and operational improvement.
The economic order of magnitude of the reform is substantial. A cost-benefit analysis by Ofgem, the British regulator for the gas and electricity markets, puts the cumulative efficiency gains of the MHHS rollout for the British electricity market at GBP 1.6 to 4.5 billion over the period 2021 to 2045. At supplier level, industry estimates suggest that significantly improved load forecasting can lower balancing-energy costs by 8 to 15 percent. For a mid-sized British supplier with around 3 TWh annual volume, that corresponds to annual savings in the single-digit million-pound range.
