This study develops a robust asset-level revenue forecasting model for wind power assets in the UK electricity market, integrating half-hourly generation data from Elexon BMRS, market pricing from OFGEM, and power price projections from Oxford Economics. The model estimates P10, P50, and P90 revenue levels under multiple economic scenarios, providing insight into revenue the revenue of wind assets in the UK.

A back-analysis of 58 wind farms (2019–2023) validates the model’s accuracy, with an R² of 94% for generation forecasts and 80% for revenue forecasts.

A key application of these revenue forecasts is their integration into the infraMetrics Valuation Model, which estimates the fair market value of unlisted infrastructure equity investments. By providing detailed asset-level revenue projections, this study strengthens valuation accuracy for TICCS Industry class code IC70 (renewables) in the UK, with planned expansion into other markets.

This methodology moves beyond a static approach to revenue growth forecasting by capturing asset-specific dynamics, providing a more adaptive way to assess valuations within infraMetrics. Our initial analysis showed that the updated methodology enhanced revenue forecast accuracy for individual wind farms by 2% to 39% compared to reported figures and past forecasts, with a median adjustment of 22%.