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Conference Paper

NREL/CP-5D00-72593

November 2018

Comparing the Value of Improved 

Variable Renewable Energy 

Forecasting Accuracy in South African 

and United States Power Systems

Preprint

E. Chartan,1 J. Wright,2 and G. Landwehr2

1 National Renewable Energy Laboratory

2 CSIR Energy Center, Pretoria, South Africa

Presented at WindAc 2018

Capetown, South Africa

November 5–6, 2018
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Conference Paper

NREL/CP-5D00-72593

November 2018

Comparing the Value of Improved 

Variable Renewable Energy 

Forecasting Accuracy in South African 

and United States Power Systems

Preprint

E. Chartan,1 J. Wright,2 and G. Landwehr2

1 National Renewable Energy Laboratory

2 CSIR Energy Center, Pretoria, South Africa

Suggested Citation 

Chartan, E., J. Wright, and G. Landwehr. 2018. Comparing the Value of Improved 

Variable Renewable Energy Forecasting Accuracy in South African and United States 

Power Systems: Preprint. Golden, CO: National Renewable Energy Laboratory. 

NREL/CP-5D00-72593. https://www.nrel.gov/docs/fy19osti/72593.pdf.
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email: jwright@csir.co.za

1

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Assessing the value of improved variable renewable energy 

forecasting accuracy in the South African power system

J. Wright,1, G. Landwehr,1 and E. Chartan2

1: CSIR Energy Centre, Meiring Naudé Road, Brummeria, Pretoria, South Africa

2: National Renewable Energy Laboratory, 15013 Denver W Pkwy, Golden, CO 80401, USA

Abstract

The value associated with an improved variable renewable energy (VRE) forecast has been quantified in this research. The value of 

improved VRE forecasts can increase with increasing VRE penetration levels as well as the range of this value becoming wider. This 

value also saturates with high levels of improved VRE forecasts as there is relatively lower impact of improving VRE forecasts 

further. This paper discusses how the improvement of variable renewable energy (VRE) forecasting could impact the South African 

power system and a United States power system.

Keywords: variable renewable energy (VRE), forecast, weather systems, uncertainty, production-cost model, day-ahead, realtime, South Africa

1. Introduction

Historically, South Africa has been heavily reliant on 

coal-fired electricity generation (primarily as a result of 

advantageous technology economics and significant 

domestic resource availability). As a result of international 

commitments and local policy directives, the use of coal is 

likely to be limited in the future (The Government of the 

Republic of South Africa, 2011; Department of Environmental 

Affairs (DEA), 2017; United Nations Framework Convention 

on Climate Change (UNFCCC), 2016). Furthermore, this 

decline is in line with the newly released draft integrated 

resource plan (IRP) for 2018 showing the least cost solution 

replacing coal with wind and solar. Combining this with the 

planned decommissioning of existing coal generation 

capacity, an expected increase in future electricity demand 

and the considerable wind (and solar) resource availability

(as well as technology cost reductions already realised and 

expected in future); South Africa is likely to deploy 

considerable amount of variable renewable energy (VRE) in 

the form of solar PV and wind into the future.

Long-term expansion plans in South Africa also 

indicate that VRE contributions of 20-30% of energy 

demand by 2030 and 30-70% by 2050 are being planned 

for (Wright et al., 2017a; Department Of Energy DOE), 

2016). As VRE penetration levels increase, the accuracy and 

confidence in forecasting of VRE becomes more important. 

As a result, one of the most challenging issues for a System 

Operator (SO), is maintaining grid stability and security of

supply as the energy mix diversifies. In the past, VRE was

considered an unpredictable energy source fully dependent

on the varying weather conditions and has thus not 

contributed to system reliability (adequacy or security).

Nowadays, due to increasing penetration of VRE on many 

power systems globally (international Energy Agency (IEA), 

2017), VRE forecasting is becoming critically important as 

SOs better understand VRE resources contribution to system

reliability whilst better scheduling supply-side resources to

meet residual demand and ancillary services requirements

(reserves).

A pre-defined set of weather systems affect a large 

proportion of the already installed variable renewable energy 

(VRE) facilities in South Africa including 1) the coastal low 

pressure cell that sits on the South East coast and results 

from the clockwise motion of frequently approaching cold 

fronts, 2) the ’Berg wind’ which is effectively an entrenched 

high pressure cell that sits on the East coast and brings in 

warm air in an anti-clockwise motion and 3) cloud build up 

on the West coast including mention of the anomalous low 

level jet (LLJ) resulting in strong diurnal cycles in wind speed. 

The current VRE forecast uncertainty associated with these 

localised weather systems at a range of pre-defined 

timescales (real-time and day-ahead) is quantified using a 

meteorological approach. From this, expected resource 

profiles are applied to determine annual energy productions 

(AEP’s) for various forecast uncertainty levels.

Generally speaking, the economic value of VRE forecasts 

in all weather systems can become consider- able but does 

saturate after a certain relative level of improvement. This 

paper combines the expected accuracy of VRE forecasts for 

a range of South African weather systems with a 

production-cost model of the South African power system 

to determine the economic value of VRE forecasts at a 

national scale for a range of VRE penetration levels. It then 

attempts to compare this to a similar utility in the USA.

2. Background

2.1. South African power system overview

South Africa’s installed capacity and electrical 

energy mix is summarised by resource type in Table 1 (for 

2017) (Calitz and Wright, 2017; Wright et al., 2017b). The 

South African electricity mix is currently dominated by coalfired generation capacity complemented by minor nuclear 

as well as domestic and imported hydro generation capacity 
2

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(and pumped storage). A small amount of peaking capacity 

(in the form of open-cycle gas turbines (OCGTs)) also exists 

predominantly run on distillate (diesel fuel) whilst 

renewables thus far play a very small role in the energy mix.

Although the role of VRE resources has historically 

been quite small in South Africa, they have ramped up 

considerably in recent years from a very low base as shown 

in Fig. 1, Fig. 2 and Fig. 3 (Calitz and Wright, 2017). By the 

end of 2017, South Africa had 3.5 GW of solar PV and wind 

both generating a total of 8.3 TWh of electricity contributing 

3.5% to electrical energy demand. An additional 4.0 GW of 

VRE is expected by 2021 which would then contribute 17-

19 TWh (6-7% of energy demand).

Table 1: South Africa existing electricity supply summary 

for 2017 (by technology - adapted).

Figure 1: Installed capacity of variable renewable energy 

(VRE) capacity in South Africa (end 2017).

Figure 2: Energy generated by variable renewable energy 

(VRE) capacity in South Africa (end 2017).

Figure 3: South Africa total energy demand and residual 

demand for 2017 (after solar PV, wind and CSP).

2.2. Error Metrics to be used 

Error metrics are commonly used to express 

differences between two time series namely the predicted 

and measured value (i) as shown in Eq. 2.1:

εεii = xxpppppppp,ii − xxmm mmmm,ii (2.1) [∀ii]

There are a number of common such metrics a few of which 

will be presented in the sub-sections which follow (LANGE 

and FOCKEN, 2005).

2.2.1.Root Mean Square Error (RMSE) 

Although an initial and rudimentary indicator, RMSE

can be used to understand different error sources. It can be 

described as shown in Eq. 2.2 (Willmott, 2005):

RRRRRRRR = �1

NN�εεii

2

NN

ii=1

(2.2) [∀ii]

2.2.2.Mean Absolute Error (MAE): 

Mean Absolute Error (MAE) is the unaltered 

magnitude i.e. absolute value of each difference in a time 

series. It is described as shown in Eq. 3.3 (WILLMOTT, 

2005):

MM = 1

NN�|εεii |

NN

ii=1

(2.3) [∀ii]

MAE can be considered the most natural and unambiguous

measure of average error magnitude. For this reason and 

for the purposes of this study, MAE is considered.

2.3. Impact of weather systems on forecasting 

errors in South Africa 

The expected uncertainty associated with VRE 

forecasts in South Africa is highly dependent on localised 

weather systems. Based on this, and keeping in line with 
3

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MAE values generated from the CSIR semi-operational VRE

forecasting model, the following MAE values per resource 

have been used in the PLEXOS modelling framework 

(guided by Energy and Meteo Systems intra-day and dayahead high level forecasts for existing wind and solar PV 

generators) (LANGE, 2018).

Table 2: Simulated Day-ahead MAE for South African 

relevant test sites

Wind MAE 

Day Ahead

Solar MAE 

Day Ahead

January 13.1% 16.8%

February 13.4% 15.9%

March 13.7% 16.8%

April 13.6% 15.1%

May 12.3% 12.5%

June 11.4% 11.5%

July 13.3% 12.3%

August 13.0% 14.6%

September 13.7% 16.9%

October 14.0% 19.0%

November 13.1% 19.3%

December 12.8% 18.1%

Average 13.1% 15.7%

Similarly, the following values are relevant for the indicative 

United States power systems considered.

Table 3: Aggregate renewable energy forecasting error 

values (wind and solar combined) for forecasting 

improvement and renewable energy penetration cases 

[20]

3. Modelling Approach

3.1. System level valuation of improved VRE 

In the context of this paper, production-cost 

modelling is utilised to quantify the value of VRE forecasts.

Production-cost modelling is an optimisation 

exercise which minimises total system costs by 

incorporating all relevant technical and financial operational 

characteristics of the supply and demand-side resources 

present in the power system.

The approach taken is similar to that in (WANG et 

al., 2016; HODGE et al., 2018). The production-cost model

applies a two-step sequential approach to quantify the value 

of the VRE forecast initially. Historically, SOs made decisions 

with imperfect information on the demand forecast too but 

for the purposes of these investigations, no demand 

uncertainty is considered (it’s expected that the greatest 

variability from day-ahead prices to real-time prices is the 

VRE energy).

The production-cost model co-optimises energy 

and reserves on a day-ahead basis (hourly-resolution) while

solving the unit-commitment and economic dispatch

problem i.e. which units should be online and at what levels 

should they be dispatched once online. Following this, it 

solves the real-time (also hourly-resolution) unitcommitment and economic dispatch problem using the dayahead outcomes in which certain generating units are 

considered committed (typically inflexible plants with large 

minimum up/down times). The real-time run is solved by 

allowing unit-commitment and redispatch of flexible supply 

resources considering the ’actual’ VRE supply on-the-day. 

This is shown in the overview in Fig. 12.

Figure 4: Sequential system dispatch approach with 

imperfect information – interleaved Plexos model (used to 

quantify the value of improved VRE forecasting).

One can now establish the total system production 

costs considering ’actual’ VRE supply. This allows one to 

establish a cost difference too between day-ahead and realtime total system costs which establishes a cost-adder. As 

renewable energy increases, it makes up the largest portion 

of uncertainty.

Although the above establishes the total production

costs that could be expected as a result of certain VRE

penetration levels, it does not necessarily establish the

value of an improved VRE forecast. An improved VRE

forecast would entail a comparison of the total production

cost of the real-time outcomes for a particular VRE

penetration and VRE forecast assumption relative to one in 

which the VRE forecast is improved. The tool used to 

implement the approach to quantify this value of the VRE 

forecast in this paper is PLEXOS (Energy Exemplar, 2017).
4

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3.2. Scenarios 

Selected representative scenarios were chosen to represent

the expected short-term to medium-term penetration levels 

of VRE in South Africa. These are summarised for VRE 

resources in the table below. Relative to the expected 

forecast uncertainty, a 50% forecast improvement for both 

solar PV and wind is considered.

Table 4: Forecasting error improvement for the range of 

selected VRE penetration scenarios.

4. Input assumptions

A representative South African power system is 

considered and has been summarised in Table 1.

The value of improved VRE forecasts is highly 

dependent on the complementary energy mix and thus is 

dependent on the jurisdiction within which the analysis is 

undertaken. Thus, a valuable contribution of this work is 

characterising the value of VRE forecast improvements for 

the South African power system (which has not yet been 

seen in the literature). The intention is to mimic the South 

African power system using estimated generator technical 

characteristics and provide an indication of the system value 

that could be realised as VRE penetration levels increase.

4.1. South African Demand Profile 

The demand profile considered is based on a typical 

South African demand profile and is shown graphically in 

the figure below.

Figure 5: Demand profile considered for South Africa.

The South African power system is relatively stable 

throughout the year but with a distinct winter peak (JuneAugust). The peak demand for the profile considered is 36.0 

GW with a minimum demand of 19.2 GW and annual energy 

of 243 TWh.

Profiles for wind and solar PV are based on the 

national level profiles established in (CSIR; SANEDI; 

FRAUNHOFER IWES; ESKOM, 2016) (based on 27 supply 

areas in South Africa). These are summarised in Fig. 14 and 

Fig. 15 as temporal profiles (showing two representative 

months - minimal seasonality) and in Fig. 16 as duration 

curves.

Figure 6: Supply profile for aggregated wind (January and 

July shown).

Figure 7: Supply profile for aggregated solar PV (January 

and July shown).

Figure 8: Duration curve for VRE profiles considered (solar 

PV and wind).

The associated VRE forecast uncertainty is derived 

from actual recent experience as summarised in section 2.4.

These are utilised in the production-cost modelling 

approach previously described as the basis for VRE forecast 

uncertainty.

4.2. VRE Data sources 

As required for the Plexos modelling framework

inputs, a wind Atlas of South Africa (WASA) mast measured 

three year averaged wind timeseries was used. The WASA 

mast position was selected to provide a wind profile that is 

representative of the best wind resource in South Africa. A 

simple Vestas V112 power curve was used to determine the 

theoretical power production at this position.
5

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From a Solar perspective, a two year averaged 

measured solar PV power production timeseries was used 

for the CSIR installed PV plant at the CSIR Campus in 

Pretoria.

In each case above, forecast values were simulated 

based on the values displayed in table 2 above.

For representative United States ISO’s, wind 

forecasts were taken from the WIND toolkit, which is 

statistically created. State-of-the-art forecast accuracy was 

mimicked by reforecasting the years 2007–2013 using 

industry-standard techniques. A wind power forecasting 

system based on Numerical Weather Prediction was 

developed for Xcel Energy to optimize grid integration. This 

system is solely a forecasting system and focuses on Xcel 

Energy’s specific requirements. It is not publicly available.

5. Findings

5.1. South Africa 

The production cost savings as a result of 

forecasting improvements of 20% and 40% are 

summarised in Fig. 17. These are primarily as a result of 

an improved forecasting of variable solar and wind 

resources which assists in not having to unexpectedly 

activate reserves and run more expensive units relative to 

what was expected by the System Operator. 

The value of VRE forecasts can become 

considerable but does begin to saturate after a certain 

relative level of improvement. The expected penetration 

level of VRE in South Africa in the short- to medium-term 

has been considered only and thus this saturation point has 

not yet been determined but from the figure below this is 

starting to become evident already (only up to 20% VRE 

penetration has been tested). Further work into evaluating 

the value of improved VRE forecasts for higher penetration 

VRE scenarios will be pursued in future.

As can be seen Fig. 9, the value of a 20% improved 

forecast relative to the state-of-the-art South African VRE 

forecast as is 0.02-0.12% of total production costs as VRE 

penetration increases from 3.6-19.0%. Similarly, the value 

of a 40% improved VRE forecast is 0.04- 0.21% as VRE 

penetration increases from 3.6-19.0%. At the existing level 

of VRE penetration in South Africa (3.6%), the absolute 

production cost savings are 1.4 USD-million/yr for a 20% 

improvement and 2.3 USD-million/yr for a 40% 

improvement.

Figure 9: Total production cost savings for a 

representative South African power system as VRE 

penetration increases for an improvement in wind and 

solar PV forecasts by 20% and 40% (relative to the stateof-the-art forecasts available in South Africa)

5.2. Comparison of results from SA and USA 

Following a brief review of the investigations 

undertaken in [20], the outcomes seen for the South African 

power system in this paper are compared to representative 

power systems in the U.S. - the California Independent 

System Operator (CAISO) and the Midcontinent System 

Operator (MISO). The installed capacity and estimated 

energy mixes of these two power systems are shown in the 

tables below. Using these as a basis, wind and solar 

capacity was added with varying penetration ranges of 

12%-56% whilst considering improvements in the forecast 

of 0%, 20% and 40%.

For the relevant range considered (10-20% VRE 

penetration) and a 20% forecast improvement, the 

representative CAISO power system demonstrates total 

operating cost savings of ≈0.2% whilst that of MISO shows 

savings of ≈0.1-0.2%. For a 40% improvement in the 

forecast, the CAISO system shows total operating cost 

savings of 0.25-0.40% whilst that of MISO shows savings 

of 0.3-0.4%. 

These outcomes show slightly higher relative 

production cost savings when compared to the South 

African power system analysed in this paper. More 

specifically, with that of MISO as the energy mix is closer to 

that of South Africa (coal-dominated) whilst the CAISO 

energy mix has more flexible gas-fired capacity.
6

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Table 5: Midcontinent Independent Transmission System 

Operator (MISO) installed capacity 

Technology Installed 

Capacity [MW]

Energy share 

est. [%]

Coal 57 500 48

Nuclear 13 000 16

Gas 59 900 24

Oil 4 150 <0.1

Hydro 3 300 2

Wind 14 700 9

Solar PV 400 <1

Biomass 224 <1

Pumped Storage 2 518 -

Table 6: California Independent System Operator (CAISO)

Technology Installed 

Capacity [MW]

Energy share 

est. [%]

Coal 52 0.2

Nuclear 2 694 9

Gas 42 227 43

Oil 352 <0.1

Hydro 14 002 21

Wind 5 632 6

Solar PV 9 588 11

Biomass 1 314 2

Pumped Storage - -

Geothermal 2 694 6

6. Relevant South African Weather Systems and 

their impact on energy forecast MAE

There are a number of under researched weather 

systems affecting a large proportion of the already installed 

variable renewable energy (VRE) generation facilities in 

South Africa. With further investigation and better 

understanding, this will result in improved VRE forecasting.

As an introduction to these anomalous weather 

systems, it is necessary to discuss the variable resource 

energy potential. It is clear that the variable resource 

potential in South Africa is extremely high. These potentials 

significantly exceed the current and the expectable future 

demand. Wind turbines could be operated with 

extraordinarily high capacity factors above 30% in locations 

all over the country, large areas even offer capacity factors 

around 40%. In other words, more than 80% of South 

Africa's land mass has enough wind resources for low cost 

wind energy [1]. 

Since South Africa is lower than 15 degrees south 

of the equator, it does not experience cyclostrophic winds 

where Coriolis force is tending towards zero but rather 

experiences gradient winds resulting from the balance 

between the pressure gradient force and the Coriolis force. 

In areas where the pressure gradient force exceeds a 

certain amount, there is ageostrophic flow across isobars. 

At low levels, wind has a cross isobaric component causing 

convergence into a low pressure and divergence from a high 

pressure cell [4]. This typically occurs in a clockwise 

direction in the Southern Hemisphere for the low pressure 

‘trough’ and in an anti-clockwise direction for the high 

pressure ridge. This movement of wind results in a 

sinusoidal movement across South Africa which can move 

up and down in an ad-hoc manner at different times of the 

year.

6.1. High pressure cells 

There are three major high pressure cells that are 

well developed over South Africa namely the South Atlantic 

anti-cyclone, the Kalahari high pressure cell and the South 

Indian anticyclone. During summer, the two anti-cyclones 

tend to move southwards with the heat equator and 

effectively block mid-latitude cyclones (MLC’s) from 

penetrating the interior whilst the Kalahari high pressure 

cell remains generally fixed over Northern South Africa with 

a well-developed low existing over the Western part of 

South Africa [5].

In winter however, the sub-tropical high pressure 

cells migrate northwards due to friction between the cooler 

polar easterlies and warmer sub-tropical westerly’s. This 

effectively allows the MLC’s to penetrate the interior [5].

Figure 10: Synoptic patterns over Southern Africa during 

summer and winter seasons [6]

6.2. The coastal low pressure cell 

Off the East Coast of South Africa, a low pressure 

cell is frequently experienced and its depth is dictated by 
7

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the a subsidence inversion at the escarpment (as indicated 

in the figure below) of South Africa and separates warm 

offshore air from cooler surface maritime air [7]. The impact 

of the escarpment and common travelling depressions 

around South Africa pose complex weather and in turn 

energy forecasting problems.

Figure 11: South Africa terrain elevation (SRTM+, NASA 

version 3) [2]

In general there are two major travelling low 

pressure depressions that affect South Africa’s climate 

resulting from the primary global circulations due to the 

movement of energy between areas of a heat surplus to 

areas of a heat deficit:

Mid latitude cyclones: are characterised by 

families of low pressure systems that moves along the 

westerly wind belt and last about six days. These 

depressions move have four stages namely initial, mature, 

occluded and dissipating. They occur throughout the year 

but only really affect South Africa in the winter when the 

sub-tropical highs move north and due to the ‘cold snap’ 

associated with them can result in significant increases in 

power usage for heating [5]. Although relatively well 

known, there are numerous permutations in which the MLC 

can proceed and wind speeds and their directions fluctuate 

depending on whether the cold front or warm front is 

moving past a particular point. As a result this poses an 

energy forecasting challenge and these permutations need 

to be clearly outlined in any VRE forecasting model.

Tropical cyclones: are characterised by ocean 

temperatures above 27°C, high humidity, unstable air and 

very steep low pressure gradients, strong upper air 

divergence and Coriolis force between 5° and 25° N/S of 

the equator. Typically such cyclones form over tropical 

oceans moving in the tropical easterlies and have erratic 

pathways thus increasing the difficulty in energy 

forecasting. Only those moving into the Mozambique 

Channel, and influenced by the warm Mozambique current, 

have an impact on South Africa’s weather with destructive 

winds occurring over the Limpopo, Mpumalanga and KwaZulu Natal provinces. Characteristically however such 

cyclones do break up over land and thus there is a lesser 

impact on these can occur in late summer and early autumn 

between November and April in South Africa [5].

6.3. The ‘Berg’ wind: 

The coastal climate of South Africa experiences an 

annual anomaly in which the highest temperatures of the 

year occur in the winter season and are accompanied by the 

so called ‘Berg’ winds which result from the impact of the 

mid-latitude cyclone [8]. A Berg wind is the local name for 

the East coast localised offshore Föhn‐type wind [6] in 

which moist air resulting from the established coastal low 

and strong Kalahari high pressure is lifted over the 

escarpment and loses its moisture in the high altitudes 

becoming warm and dry as it moves towards the coast. 

These are dry, hot winds which blow out of the interior 

occurring mainly between April and September, most 

frequent along the East coast of the country [8].

Figure 12: South African Berg wind [9]

Figure 13: Synoptic weather map showing Berg wind 

development around the coastal low [9]

Escarpment height 

2000 m to 3482 m
8

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Figure 14: a) Automatic weather station recording of 

surface air temperature at Durban during a Berg wind 

from about 03:15 to 10:00 local time (UTC +2) [7] b) 

Significant daily fluctuation in wind direction [9]

6.4. Cloud build up on the West coast and interior 

During the summer months when the South Atlantic 

and South Indian anti-cyclones tend to move southwards 

with the heat equator and block mid-latitude cyclones 

(MLC’s) from penetrating the interior, the Kalahari high 

pressure cell is weaker but remains generally fixed over 

central Northern South Africa with a well-developed low 

pressure existing over the Western part of South Africa 

moving south and north during this time [5]. The cold 

Benguela current, higher summer temperatures and the 

presence of this low pressure result in increased cloud cover 

over the west coast and interior due to the interaction of 

cold ocean air with the warmer continent as shown in the 

figures below. This leads to large uncertainty in forecast 

since numerical weather models have different opinions and 

accuracies when predicting the cloud formation and 

movement in these areas [9].

Figure 15: West coast and interior cloud cover in summer 

(Worldview) [9]

6.5. Low level jet (LLJ) 

The Wind Atlas of South Africa has a wind met mast 

situated on the Western Coast of South Africa (WM01). 

Based on long term data measurement from this mast, it 

has been documented that there is a very strong diurnal 

cycle in wind speed. Typically this results in a nocturnal low 

level jet (LLJ) stream simply defined as a narrow current of 

fast-moving air. This LLJ is usually caused by an inertial 

oscillation or an imbalance between the stress-divergence, 

pressure gradient force and Coriolis force as night falls. This 

normal explanation of a boundary layer LLJ does not seem 

to hold for the one identified on the West Coast of South 

Africa since it is present mostly during the daytime hours 

and not nocturnally. It is thus speculated that there is a 

“quasi-permanent” LLJ above these areas which gets mixed 

down near to the surface during the day. Furthermore, on 

certain days, the jet is unusually not present [10]. LLJ’s can 

significantly increase the energy generation from wind 

energy generators and thus, if misunderstood, can have a 

considerable influence how well energy is predicted from 

influenced facilities. Once understood, this anomaly can 

then be applied to similar occurrences globally and in turn 

improve short term (day ahead and intraday) energy 

forecasting from such sites.
9

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7. Conclusions

The value associated with an improved VRE 

forecast in a representative South African power system at 

a national scale has been quantified in this research. The 

value of improved a VRE forecast in South Africa increases 

with increasing VRE penetration levels. When comparing to 

representative U.S power systems, the outcomes show 

good correlation and alignment with the coal-dominated 

MISO region than that of the gas-dominated CAISO region. 

Future research is necessary for higher variable renewable 

energy penetration levels for a full comparison to be 

undertaken and this is intended to inform future research in 

this area.

It is clear that there a number of weather scenarios 

that with better understanding can facilitate the presented 

improvements to VRE forecasts.

Acknowledgements

The lead author of this work is the Council for Scientific and 

Industrial Research (CSIR). This work was also authored in 

part by the National Renewable Energy Laboratory, 

operated by Alliance for Sustainable Energy, LLC, for the 

U.S. Department of Energy (DOE) under Contract No. DEAC36-08GO28308. The views expressed in the article do 

not necessarily represent the views of the DOE or the U.S. 

Government. The U.S. Government retains and the 

publisher, by accepting the article for publication, 

acknowledges that the U.S. Government retains a 

nonexclusive, paid-up, irrevocable, worldwide license to 

publish or reproduce the published form of this work, or 

allow others to do so, for U.S. Government purposes.

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