New solar forecasting model performs best

Date:
June 29, 2020
Source:
Uppsala University
Summary:
A new mathematical model for predicting variations in solar
irradiance has been developed. It may help to promote more
efficient use of electricity from solar energy. In tests of
various data models, the model proved capable of making highly
reliable forecasts, and emerged as the best for this purpose in
some respects.



FULL STORY ==========================================================================
A new mathematical model for predicting variations in solar irradiance
has been developed at Uppsala University. It may help to promote more
efficient use of electricity from solar energy. In tests of various data models, the model proved capable of making highly reliable forecasts,
and emerged as the best for this purpose in some respects. The results
have now been published in two articles in the journal Solar Energy.


==========================================================================
As clouds pass overhead, solar power generation from a photovoltaic system fluctuates from one minute to the next. Local producers of their own
solar energy (for a single property, for example) wishing want to adjust
their electricity use according to supply may need to know, in detail,
how the amount of sunlight is changing. Forecasts of solar irradiance
(the amount of solar radiation reaching a given surface, measured in
watts per square metre, W/m2) may be a way of achieving greater control
of solar power production.

Project leader Joakim Munkhammar of the Department of Civil and Industrial Engineering at Uppsala University explains.

"Our 'MCM model', as it's called, serves to predict what will happen in
the next minute, hour or day, based on what usually follows a particular
solar irradiance level. This model has a simple design, is easy to train
and use, and provides surprisingly accurate solar irradiance forecasts."
The model, presented to the scientific community last year, is based on
a "hidden Markov model" -- that is, a statistical model for recognition
and probabilistic forecasting of processes and patterns. The MCM (Markov
chain mixture) distribution model divides solar irradiance into levels
and calculates the probabilities of sunlight in the next and subsequent
time periods being at the various levels. On this basis, it is possible
to forecast when, and between which levels, sunlight will vary, and
to compare the forecasts with actual observations to see how well the
former match reality.

The model has now been tested by both scientists who have worked on it previously and other researchers. This has included test runs to compare
the model with several other models. In one study, in which the model
and five established benchmark models (used for comparison, to evaluate
the relative performance of new models) were tested, the new MCM model
yielded the most reliable forecasts, especially for the near future.

The Uppsala researchers now hope it will be feasible to use their model
to control technical systems.

"We look forward to working with other scientists and companies on
testing the model with real physical systems, such as those for battery
energy storage.

We're going to try and boost the cost-effectiveness of storage systems by adjusting the charge, based on forecasts of local solar power generation," Munkhammar says.


========================================================================== Story Source: Materials provided by Uppsala_University. Note: Content
may be edited for style and length.


========================================================================== Journal References:
1. Kate Doubleday, Vanessa Van Scyoc Hernandez, Bri-Mathias
Hodge. Benchmark
probabilistic solar forecasts: Characteristics and
recommendations. Solar Energy, 2020; 206: 52 DOI:
10.1016/j.solener.2020.05.051
2. Dazhi Yang, Dennis van der Meer, Joakim Munkhammar. Probabilistic
solar
forecasting benchmarks on a standardized dataset at Folsom,
California.

Solar Energy, 2020; 206: 628 DOI: 10.1016/j.solener.2020.05.020 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/06/200629120239.htm

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