Review of solar irradiance forecasting methods and a proposition for small-scale insular grids

TLDR: In this article, the authors present an in-depth review of the current methods used to forecast solar irradiance in order to facilitate selection of the appropriate forecast method according to needs.

Abstract: Integration of solar energy into the electricity network is becoming essential because of its continually increasing growth in usage. An efficient use of the fluctuating energy output of photovoltaic (PV) systems requires reliable forecast information. In fact, this integration can offer a better quality of service if the solar irradiance variation can be predicted with great accuracy. This paper presents an in-depth review of the current methods used to forecast solar irradiance in order to facilitate selection of the appropriate forecast method according to needs. The study starts with a presentation of statistical approaches and techniques based on cloud images. Next numerical weather prediction or NWP models are detailed before discussing hybrid models. Finally, we give indications for future solar irradiance forecasting approaches dedicated to the management of small-scale insular grids.

Figures

Figure 2: Relation between horizons, models and activities

Figure 1: Classification of model based on spatial and temporal resolution

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Review of solar irradiance forecasting methods and a
proposition for small-scale insular grids
Hadja Maïmouna Diagne, Mathieu David, Philippe Lauret, John Boland,
Nicolas Schmutz
To cite this version:
Hadja Maïmouna Diagne, Mathieu David, Philippe Lauret, John Boland, Nicolas Schmutz. Review
of solar irradiance forecasting methods and a proposition for small-scale insular grids. Renewable
and Sustainable Energy Reviews, Elsevier, 2013, 27, pp.65 - 76. �10.1016/j.rser.2013.06.042�. �hal-
01090087�

Review of solar irradiance forecasting methods
and a proposition for small-scale insular grids
Ma¨ımouna Diagne
1,2,∗
Mathieu David
2
Philippe Lauret
2
John Boland
3
Nicolas Schmutz
1
1
Reuniwatt company
2
University of La Reunion
3
University of South Australia
∗
maimouna.diagne@reuniwatt.com
Abstract
Integration of solar energy into the electricity network is becoming
essential because of its continually increasing growth in usage. An efficient
use of the fluctuating energy output of photovoltaic (PV) systems requires
reliable forecast information. In fact, this integration can offer a better
quality of service if the solar irradiance variation can be predicted with
great accuracy.
This paper presents an in-depth review of the current methods used to
forecast solar irradiance in order to facilitate selection of the appropriate
forecast method according to needs. The study starts with a presentation
of statistical approaches and techniques based on cloud images. Next nu-
merical weather prediction or NWP models are detailed before discussing
hybrid models. Finally, we give indications for future solar irradiance
forecasting approaches dedicated to the management of small-scale insu-
lar grids.
Keywords: Solar irradiance, forecast models, statistical models, NWP
models, postprocessing methods.
1 Introduction
The contribution of photovoltaic systems (PV system) power production to the
electric power supply is constantly increasing. Utility companies and transmis-
sion system operators have to deal with the fluctuating input from PV system
energy sources. This is a new challenge compared with power production from
conventional power plants that can be adjusted to the expected load profiles.
An efficient use of the fluctuating energy output of PV systems requires reliable
forecast information. Load patterns forecasted for the next 2 days provide the
basis for scheduling of power plants and planning transactions in the electricity
1

market in order to balance the supply and demand of energy and to assure reli-
able grid operation. These forecasts are used by utility companies, transmission
system operators, energy service providers, energy traders, and independent
power producers in their scheduling, dispatching and regulation of power.
In particular, insular territories experience an unstable electricity network
and use expensive means in order to provide the power for the peak demand
periods. Their grids are generally not interconnected with any continent and
all the electricity must be produced inside the territory. The power of grid
connected PV plants increases fast and can interfere with network stability.
An efficient forecasting method will help the grid operators to better manage
the electrical balance between demand and power generation. Kostylev and
Pavlovski [1] identify three forecasting horizons (intra-hour, intra-day and day
ahead) related to the grid operator activities (ramping events, variability related
to operations, unit commitment, transmission scheduling, day ahead markets,
hedging, planning and asset optimization).
Forecasting of global horizontal irradiance (GHI) is the first and most es-
sential step in most PV power prediction systems. GHI forecasting approaches
may be categorized according to the input data used which also determine the
forecast horizon.

Statistical models based on online irradiance measurements are applied for
the very short term timescale from 5 minutes up to 6 hours (see Reikard,
[2]). Examples of direct time series models are autoregressive (AR) and
autoregressive moving average (ARMA) models. Furthermore, artificial
neural networks (ANN) may be applied to derive irradiance forecasts.

For short-term irradiance forecasting, information on the temporal devel-
opment of clouds, which largely determine surface solar irradiance, may
be used as a basis.
– Forecasts based on cloud motion vectors from satellite images (Lorenz
and al, [3]) show good performance for the temporal range from 30
minutes up to 6 hours.
– For the subhour range, cloud information from ground-based sky
images may be used to derive irradiance forecasts with much higher
spatial and temporal resolution compared with the satellite-based
forecasts.

For longer forecast horizons, from about 4−6 hours onward, forecasts
based on numerical weather prediction (NWP) models typically outper-
form the satellite-based forecasts (see Perez and al[4], Heinemann and
al.[5]).

There are also combined approaches that integrate different kinds of input
data to derive an optimized forecast depending on the forecast horizon.
2

Solar irradiance forecasts was assessed in terms of root mean square error
(RMSE) and mean bias error (MBE or bias) which are defined as follows:
RMSE =
q
1
n
·
P
n
i=1
(x
pred,i
− x
obs,i
)
2
MBE =
1
n
·
P
n
i=1
(x
pred,i
− x
obs,i
)
where x
pred,i
and x
obs,i
represent the i
th
valid forecast and observation pair,
respectively and n is the number of evaluated data pairs. This metric are
not formulate in the same way in all the papers we review. David and al [6]
illustrated several formula wrongly called RMSE or MBE.
Many solar irradiance forecasting models have been developed. These models
can be divided into two main groups: statistical models and NWP models.
Statistical models are based upon the analysis of historical data. They include
time series models, satellite data based models, sky images based models, ANN
models, wavelet analysis based models, etc. NWP models are based on historical
data and the reproduction of physical information.
The paper is organized as follow. In Section 2, statistical approaches are
presented. In Section 3, cloud imagery and satellite based models proposed in
the literature are reviewed. In Section 4, the NWP approaches presented in
the literature are reviewed. In Section 5, hybrid models are evaluated. Finally
Section 6 is dedicated to trends for future solar irradiance forecasting in an
insular environment.
2 Statistical models
Forecasting methods based on historical data of solar irradiance are two cat-
egories: statistical and learning methods. Seasonality analysis, Box-Jenkins
or Auto Regressive Integrated Moving Average (ARIMA), Multiple Regres-
sions and Exponential Smoothing are examples of statistical methods, whilst
AI paradigms include fuzzy inference systems, genetic algorithm, neural net-
works, machine learning etc.
2.1 Linear models or time series models
Statistical methods have been used successfully in time series forecasting for
several decades. Using the statistical approach, relations between predictors,
variables used as an input to the statistical model, and the variable to be pre-
dicted, are derived from statistical analysis. Several studies with respect to
direct time series modeling have been performed. In Reikard [2], different time
series models are compared. In Bacher and al.[7], the authors investigate the
use of a simpler AR model to directly predict PV power in comparison with
other models.
3

2.1.1 Persistence model
It is useful to check whether the forecast model provides better results than
any trivial reference model. It is worthwhile to implement and run a complex
forecasting tool only if it is able to clearly outperform trivial models. Probably
the most common reference model in the solar or wind forecasting community for
short term forecasting is the persistence model. The persistence model supposes
that global irradiance at time t is best predicted by its value at time t − 1:
ˆ
X
t+1
= X
t
The persistence model, also known as the na¨ıve predictor, can be used to
benchmark other methods. Persistence forecast accuracy decreases strongly
with forecast duration as cloudiness changes from the current state. Generally,
persistence is an inaccurate method for more than 1 hour ahead forecasting and
should be used only as a baseline forecast for comparison to more advanced
techniques.
In Perez and al.[4], the single site performance of the forecast models is
evaluated by comparing it to persistence.
2.1.2 Preprocessing of input data
When using statistical time series analysis, any type of conditional forecast
model is structured to deal with stationary series, at least weakly stationary.
This means no trend nor seasonality, and the series is homoscedastic (constant
variance). There are several ways to deal with non-stationary series to get them
into an appropriate form.
2.1.2.1 Processes to obtain stationary solar irradiance time series
The solar insolation is the actual amount of solar radiation incident upon a
unit horizontal surface over a specified period of time for a given locality. It
depends strongly on the solar zenith angle. For statistical models, it may be
favorable to treat the influences of the deterministic solar geometry and the non-
deterministic atmospheric extinction separately. For this purpose, two transmis-
sivity measures have been introduced: clearness index (k) and clear-sky index
(k
∗
).
2.1.2.1.1 Clearness index The clearness index k is defined as the ratio
of irradiance at ground level I to extraterrestrial irradiance I
ext
on an horizontal
plane:
k = I/I
ext
(1)
It describes the overall extinction by clouds and atmospheric constituents in
relation to the extraterrestrial irradiance. This approach strongly reduces sea-
sonal and daily patterns by considering the influence of the zenith angle, which
is modeled by I
ext
. The clearness index is widely applied to reduce the deter-
ministic trend in irradiance time series. However, the clearness index accounts
4

Frequently asked questions

Q1. What have the authors contributed in "Review of solar irradiance forecasting methods and a proposition for small-scale insular grids" ?

In this paper, the authors used an ANN to forecast the global horizontal irradiance ( GHI ) of photovoltaic ( PV ) systems. 

Q2. What are the future works in "Review of solar irradiance forecasting methods and a proposition for small-scale insular grids" ?

For small-scale insular grids A summary of the literature on solar irradiance forecasting models illustrated by Figure 1 and Figure 2 gives indications for future work. However, a further consideration in choosing among forecasting models is efficiency. The authors suggest to use the mesoscale model WRF. At higher frequency, the data is more dominated by short-term patterns which can be picked up by persistence or ANN. 

Q3. What are some examples of statistical methods used in time series forecasting?

Seasonality analysis, Box-Jenkins or Auto Regressive Integrated Moving Average (ARIMA), Multiple Regressions and Exponential Smoothing are examples of statistical methods, whilst AI paradigms include fuzzy inference systems, genetic algorithm, neural networks, machine learning etc.Statistical methods have been used successfully in time series forecasting for several decades. 

Q4. What are the main types of models used for irradiance forecasting?

They include time series models, satellite data based models, sky images based models, ANN models, wavelet analysis based models, etc. NWP models are based on historical data and the reproduction of physical information. 

Q5. What are the main sources of information used for the determination of local solar irradiance?

Satellites and ground-based sky images, have been used for the determination and forecasting of local solar irradiance conditions. 

Q6. What are the main features of the NWP models?

They allow the correction of systematic deviations in dependence on different meteorological parameters and for modeling of the irradiance if it is not provided as output parameter of an NWP model. 

Q7. What is the role of MM5 and WRF in the forecasting of solar energy?

In addition, the capability of MM5 and WRF to integrate local measurements, for example, aerosols, may also contribute to improving forecast accuracy. 

Q8. What are the key variables needed to run a forecast?

The key variables needed are the three-dimensional fields of wind, temperature, and humidity and the two-dimensional field of surface pressure. 

Q9. What is the purpose of Kalman filters?

Kalman filters are designed to efficiently extract a signal from noisy data and are therefore expected to show a more robust performance if only limited training data are available, which is the case if the training is performed on the basis of individual stations. 

Q10. What is the way to determine the irradiance of clouds?

Satellites and ground-based sky images with their high temporal and spatial resolution offer the potential to derive the required information on cloud motion. 

Q11. How is the resolution of the driving global model increased?

To achieve the intended high spatial resolution in a mesoscale model with reasonable computing time, the resolution of the driving global model is increased stepwise with internal nesting. 

Q12. What are the main factors that influence the irradiance of clouds at surface level?

Besides the deterministic daily and annual patterns of irradiance, clouds cover as well as cloud optical depth have the strongest influence on solar irradiance at surface level. 

Q13. What is the advantage of using a hybrid model for forecasting sequences of total solar ?

Cao and Cao in [45] and [11] developed a hybrid model for forecasting sequences of total daily solar irradiance, which combines ANN with wavelet analysis. 

Q14. What was the suitable realization of their approach?

Pelland et al. [44] found that the most suitable realization of their approach was a set of Kalman filter equations established separately for each forecast horizon and modeling the bias in dependence on the forecasted irradiance. 

Q15. Why are GFS data used to initialize MM5 or WRF?

GFS data of NOAA are used to initialize MM5 or WRF for operational applications, because, in contrast to ECMWF data, they are available for free. 

Q16. What are the main uses of the forecasts?

These forecasts are used by utility companies, transmission system operators, energy service providers, energy traders, and independent power producers in their scheduling, dispatching and regulation of power. 

Q17. What is the fit to the residual series?

The introduction of a resonating model introduced for the power market by Lucheroni [20] plus the judicious intermittent use of a proxy for curvature allows for a much superior fit to this residual series.