Time series Foundation Models based on Physics-Informed Synthetic Histories for Cold-Start Photovoltaic Forecasting
arXiv:2606.07457v1 Announce Type: new Abstract: At commissioning time, Photovoltaic (PV) operators must forecast production before target-site observations are available, limiting the direct use of standard supervised forecasters. This cold-start setting is addressed with a zero-shot pipeline that generates a synthetic production history from plant metadata and meteorological covariates, enabling time-series foundation models (TSFMs) to forecast through inference-time conditioning. Five TSFMs are benchmarked against classical baselines under strict Cold-Start Baseline, Real Feedback, and Self-
The increasing deployment of photovoltaic systems globally emphasizes the immediate need for robust forecasting methods, especially in the initial stages of operation, which this research addresses.
Accurate cold-start forecasting for photovoltaics significantly improves grid stability, energy management, and return on investment for renewable energy projects, reducing reliance on less predictable energy sources.
The development of a zero-shot pipeline using physics-informed synthetic histories and Time Series Foundation Models allows for reliable PV production forecasts even without prior operational data, enabling more efficient and resilient renewable energy integration.
- · Renewable energy operators
- · Grid management companies
- · AI/ML model developers
- · Energy investors
- · Traditional energy forecasting methods
- · Fossil fuel-based energy providers (indirectly)
Improved forecasting capabilities lead to more efficient and reliable renewable energy deployment.
Enhanced grid stability and reduced operational costs for integrating intermittent renewable sources.
Accelerated global transition towards a cleaner, more decentralized energy infrastructure, potentially impacting geopolitical energy dynamics.
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