d4974e3241
- flexmeasures-entsoe: ENTSO-E data plugin - flexmeasures-weather: Weather data plugin - USEF Flex Trading Protocol PDF (2.4MB) - Cariflex simulator (publishes to Redis) - Dashboard Grafana updated with correct InfluxDB queries - All tools extracted in /tools/
38 lines
1.4 KiB
Python
38 lines
1.4 KiB
Python
from datetime import datetime
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import pandas as pd
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from pvlib.location import Location
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def compute_irradiance(
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latitude: float, longitude: float, dt: datetime, cloud_coverage: float
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) -> float:
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"""Compute the irradiance received on a location at a specific time.
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This uses pvlib to
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1) compute clear-sky irradiance as Global Horizontal Irradiance (GHI),
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which includes both Direct Normal Irradiance (DNI)
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and Diffuse Horizontal Irradiance (DHI).
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2) adjust the GHI for cloud coverage
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"""
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site = Location(latitude, longitude, tz=dt.tzinfo)
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solpos = site.get_solarposition(pd.DatetimeIndex([dt]))
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ghi_clear = site.get_clearsky(pd.DatetimeIndex([dt]), solar_position=solpos).loc[
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dt
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]["ghi"]
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return ghi_clear_to_ghi(ghi_clear, cloud_coverage)
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def ghi_clear_to_ghi(ghi_clear: float, cloud_coverage: float) -> float:
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"""Compute global horizontal irradiance (GHI) from clear-sky GHI, given a cloud coverage between 0 and 1.
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References
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----------
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Perez, R., Moore, K., Wilcox, S., Renne, D., Zelenka, A., 2007.
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Forecasting solar radiation – preliminary evaluation of an
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approach based upon the national forecast database. Solar Energy
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81, 809–812.
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"""
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if cloud_coverage < 0 or cloud_coverage > 1:
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raise ValueError("cloud_coverage should lie in the interval [0, 1]")
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return (1 - 0.87 * cloud_coverage**1.9) * ghi_clear
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