Photovoltaic Power Forecasting with AI: A Cost–Benefit Framework Across Multiple Time Horizons

The rapid global expansion of photovoltaic capacity, now exceeding 1 TW, has transformed solar power forecasting from an engineering problem into a financially critical investment decision. Yet virtually all published forecasting studies optimise statistical accuracy metrics without translating improvements into monetised operational value. This paper introduces a unified cost–benefit framework that maps forecast errors across three operationally distinct time horizons onto imbalance costs, arbitrage revenues, and AI deployment costs. The economic conclusions are grounded in Romanian Balancing Market conditions (mean up-regulation price λ+ ≈ 85 €/MWh, mean down-regulation price λ− ≈ 42 €/MWh; 15 min settlement interval), a five-year dataset (2018–2022) from a 10 MW utility-scale PV installation in Romania, and an annual AI system cost of 36,000 €/MW decomposed into data infrastructure, cloud GPU compute, and model-monitoring personnel. A Temporal Fusion Transformer ensemble, benchmarked against CNN-LSTM, Informer, and smart-persistence baselines, achieves a 0.38 Skill Score at the day-ahead horizon and a 0.28 Value Score, translating to a net economic benefit of €142,000 per installed MW per annum after full AI system cost deduction. While the framework is designed to be reusable across markets, all reported economic values are specific to the stated Romanian market parameters and should be recalibrated for other regulatory jurisdictions.