SolarPosition.jl

SolarPosition.jl provides a simple, unified interface to a collection of validated solar position algorithms written in pure, performant julia.

Solar positioning algorithms are commonly used to calculate the solar zenith and azimuth angles, which are essential for various applications where the sun is important, such as:

• Solar energy systems
• Building design
• Climate studies
• Astronomy

Acknowledgement

This package is based on the work done by researchers in the field of solar photovoltaics in the packages solposx and pvlib-python. In particular the positioning and refraction methods have been adapted from solposx, while the SPA algorithm and the deltat calculation are ported from pvlib-python. These packages also provide validation data necessary to ensure correctness of the algorithm implementations.

Example Usage

julia> using SolarPosition, Dates, TimeZones

# define observer location (latitude, longitude, altitude in meters)
julia> obs = Observer(52.35888, 4.88185, 100.0)  # Van Gogh Museum, Amsterdam
Observer(latitude=52.35888°, longitude=4.88185°, altitude=100.0m)

julia> tz = TimeZone("Europe/Brussels")
Europe/Brussels (UTC+1/UTC+2)

# a few hours of timestamps
julia> times = collect(DateTime(2023, 6, 21, 10):Hour(1):DateTime(2023, 6, 21, 15));

# compute solar positions for all timestamps
julia> positions = solar_position(obs, times)
6-element StructArray(::Vector{Float64}, ::Vector{Float64}, ::Vector{Float64}) with eltype SolPos{Float64}:
 SolPos(azimuth=136.1908215897601°, elevation=55.13208390809107°, zenith=34.86791609190893°)
 SolPos(azimuth=160.3753655770986°, elevation=59.974081481305134°, zenith=30.025918518694862°)
 SolPos(azimuth=188.3992597996431°, elevation=60.87918930278924°, zenith=29.120810697210757°)
 SolPos(azimuth=214.62987222053295°, elevation=57.493462259959394°, zenith=32.5065377400406°)
 SolPos(azimuth=235.5258846451899°, elevation=50.992647293443966°, zenith=39.007352706556034°)
 SolPos(azimuth=251.77304757136397°, elevation=42.790197455865076°, zenith=47.209802544134924°)

Sunrise and Sunset Calculations

Calculate sunrise, sunset, and solar noon for a specific date with timezone:

julia> result = transit_sunrise_sunset(obs, ZonedDateTime(2023, 6, 21, tz))
TransitSunriseSunset{ZonedDateTime}(
    transit=2023-06-21T13:42:15+02:00,
    sunrise=2023-06-21T05:18:05+02:00,
    sunset=2023-06-21T22:06:24+02:00
)

Find the next sunrise from a specific time in UTC:

julia> next_sunrise(obs, DateTime(2023, 6, 21, 12, 30))
2023-06-22T03:18:19

Find the next sunset in UTC:

julia> next_sunset(obs, DateTime(2023, 6, 21, 12, 30))
2023-06-21T20:06:24

Solar positioning algorithms

SolarPosition.jl implements most of the solar position algorithms commonly found in literature and industrial applications. If your preferred algorithm is not listed here, please open an issue or submit a pull request.

Algorithm	Reference	Accuracy	Default Refraction
SPA	Reda & Andreas, 2004	±0.0003°	SPA
PSA	Blanco-Muriel et al.	±0.0083°	None
Walraven	Walraven, 1978	±0.0100°	None
Iqbal	Iqbal, 1983	±0.0100°	None
Michalsky	Michalsky, 1988	±0.0100°	MICHALSKY
NOAA	Global Monitoring Laboratory	±0.0167°	HUGHES
USNO	U.S. Naval Observatory	±0.0500°	None

Fast repeated evaluation

For dense time series, the Interpolated wrapper precomputes cubic B-splines of SPA's geocentric solar coordinates and reconstructs positions analytically, roughly 10× faster per query while maintaining SPA's accuracy. Conveniently the same interpolant can be reused for every new observer. It's provided through a package extension for Interpolations.jl.

using Interpolations

alg = Interpolated(SPA(); tspan = (DateTime(2024, 1, 1), DateTime(2025, 1, 1)))
positions = solar_position(obs, times, alg)

See the interpolation guide for accuracy figures and when the construction cost pays off.

Automatic differentiation

All algorithms are generic over the number type, so solar positions are differentiable with ForwardDiff.jl out of the box, with no extension package needed:

using ForwardDiff

grad = ForwardDiff.gradient(
    x -> solar_position(Observer(x[1], x[2]), dt).elevation,
    [45.0, 10.0],
)

The autodiff guide shows gradients through refraction models, panel orientation optimization, and a single axis tracker example.

Refraction correction algorithms

Atmospheric refraction correction algorithms available in SolarPosition.jl.

Algorithm	Reference	Atmospheric Parameters
HUGHES	Hughes, 1985	Pressure, Temperature
ARCHER	Archer et al., 1980	None
BENNETT	Bennett, 1982	Pressure, Temperature
MICHALSKY	Michalsky, 1988	None
SG2	Blanc & Wald, 2012	Pressure, Temperature
SPARefraction	Reda & Andreas, 2004	Pressure, Temperature

Extensions

SolarPosition.jl provides optional extensions that are automatically loaded when you have the corresponding package installed.

Extension	Trigger Package	Features
Makie	Makie.jl	Plotting recipes for solar position visualization
OhMyThreads	OhMyThreads.jl	Parallel computation of solar positions
ModelingToolkit	ModelingToolkit.jl	Symbolic solar position models for simulations
Interpolations	Interpolations.jl	Fast Interpolated algorithm construction
TimeZones	TimeZones.jl	ZonedDateTime input and zoned sunrise/sunset

Numeric precision

The computation runs at the precision of the Observer{T} element type. Float32, Float64, Float128 from Quadmath.jl, and BigFloat are all supported. The precision guide lists the measured accuracy and runtime of every algorithm at every precision we tested.

How to Cite

If you use SolarPosition.jl in your work, please cite using the reference given in CITATION.cff.

Contributing

If you want to make contributions of any kind, please first that a look into our contributing guide directly on GitHub or the contributing page on the website