1403 An Open Source Solar Power Forecasting Tool Using PVLIB-Python

Wednesday, 25 January 2017
William F. Holmgren, Univ. of Arizona, Tucson, AZ; and A. T. Lorenzo and D. G. Groenendyk

We describe an open-source solar photovoltaic power forecasting tool based on the PVLIB-Python library. The tool allows users to easily retrieve standardized weather forecast data relevant to PV power modeling from NOAA/NCEP/NWS models including the GFS, NAM, RAP, HRRR, and the NDFD. PVLIB-Python uses Unidata’s Siphon library to retrieve forecast data hosted on the Unidata THREDDS catalog. A PV power forecast can then be obtained using the weather data as inputs to the comprehensive modeling capabilities of PVLIB-Python. A user can specify PV system parameters, forecast parameters, get the forecast data, and run a PV model with the data in less than 10 lines of Python code. Standardized, open source, reference implementations of PV power forecast methods using publicly available data may help advance the state-of-the-art of solar power forecasting. Additional information on PVLIB-Python and its forecast module can be online at http://pvlib-python.readthedocs.io/
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