Energy management systems currently focus primarily on optimizing a
best guess scenario based on a feed-in forecast. Renewable energy
feed-in forecasts are subject to uncertainty, resulting in deviations from
the best guess, flawed operating plans and higher opportunity costs.
Some uncertainties can be modeled using probabilistic forecasts. [1]
Main Goals:
• Develop a reproducible workflow for generating probabilistic PV Data
• Automatically create realistic test datasets that include probabilistic PV
feed-in forecasts for use in downstream optimization and analysis
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FFG - Österr. Forschungsförderungs- gesellschaft mbH
