This is a presentation on Load and Wind Energy Forecasting The paper was presented at a 2016 conference sponsored by the Swedish Association for Energy Economics (SAEE). There is one error in the slides. The RMSE of the wind energy forecasts for Sweden correspond to the same day, not day-ahead forecasts.

1. The Accuracy of the Load and Wind
Energy Forecasts in Scandinavia and
the Prospects for Improvement
Kevin F. Forbes
Associate Professor of Economics
The Catholic University of America
Forbes@CUA.edu
Ernest M. Zampelli
Professor of Economics
The Catholic University of America
Zampelli@CUA.edu
2016 Conference of the Swedish Association for Energy Economics
Luleå , Sweden
24 August 2016

2. The Organization of this Talk
1)Why is Forecasting Important?
2) The Measurement of Forecast Accuracy
3) How Accurate are the Load Forecasts?
4) How Accurate are the Wind Energy Forecasts? What is the trend in
forecast accuracy?
5)From the point of view of a system operator, how does wind energy
compare with conventional forms of generation?
6)What are the prospects for improving the accuracy of the wind and
load forecasts?

3. 1)Why is Forecasting Important?
• The stability of the power grid is enhanced
when forecasts are more accurate. This is
important because blackouts have very high
societal costs
• Some forms of balancing technologies such as
open-cycle gas turbines can be very expensive
to deploy and also have above average
emissions factors.

4. Errors in the Day-Ahead Load Forecast for New York
City and the Differential between the Real-Time and
Day-Ahead Prices in New York City, 6 August 2009
– 30 June 2013.
Note: Excludes the period of time when operations were affected by Superstorm Sandy i

5. The Dispatch of Regulation Power in
Sweden, 1 Jan 2014 - 20 August 2016-2000-1000
0
10002000
MWh
1
Jan
2015
1
Jan
2016
1
July
2014
1
July
2015
1
July
2016
Upward Regulation Downward Regulation

6. The Dispatch of Regulation Power in
Norway, 1 Jan 2014 - 20 August 2016
-2000-1000
0
10002000
MWh
1
July
2014
1
Jan
2015
1
July
2015
1
Jan
2016
1
July
2016
Upward Regulation Downward Regulation

7. The Dispatch of Regulation Power in
Finland, 1 Jan 2014 - 20 August 2016
-2000-1000
0
10002000MWh
1
July
2014
1
Jan
2015
1
July
2015
1
Jan
2016
1
July
2016
Upward Regulation Downward Regulation

8. The Dispatch of Regulation Power in
Denmark, 1 Jan 2014 - 20 August 2016-2000-1000
0
10002000
MWh
1
July
2014
1
Jan
2015
1
July
2015
1
Jan
2016
1
July
2016
Upward Regulation Downward Regulation

9. The Net Energy Imbalance in Great
Britain, 1 January 2012 – 30 June 2014
This figure depicts the net
deployment of balancing
power.
Positive values represent the
response to market shortage
while negative values represent
the response to an excess
supply.
The root causes of the
deployments include imperfect
forecasts and
the failure of suppliers to
adhere
to their generation and
transmission schedules.

10. System Frequency in Great Britain, 1
December – 31 December 2013
System frequency in Great Britain varies around
the target of 50 Hz with National Grid being
obligated to keep system frequency within one
percent of the 50 Hz target,
i.e. +/- 50 mHz In Great Britain, deviations
within the band +/- 20 mHz are considered
normal. Deviations outside the band +/- 20 mHz
do occur. Specifically, there were 152 cases in
December 2013 in which the operational limits
were violated. This appears to be a higher rate of
violations than previously. For example, there
was only one violation in December 2012.

11. 2)Measuring Forecast Accuracy
• Load Forecasters report the accuracy of their
forecasts by calculating energy weighted root-
mean-squared errors or mean absolute
percent errors.
• In contrast, wind energy forecasters almost
universally weight the errors by capacity.

12. CapacityInstalled
T
ForecastActual
CWRMSE
T
t
tt )(
1
2



ConsumedorProducedEnergyMean
T
ForecastActual
EWRMSE
T
t
tt )(
1
2



CWRMSE vs EWRMSE

13. CWRMSE vs EWRMSE
A little manipulation yields
CWRMSE = CF * EWRMSE
Where CF is the capacity factor.
Thus, EWRMSE will substantially exceed the CWRMSE
when CF is low.
For wind energy, capacity factors are usually less than
0.3

14. THE EWRMSE’S CORRESPONDING TO THE DAY-AHEAD LOAD FORECASTS in
FINLAND, SWEDEN, NORWAY, WESTERN DENMARK, EASTERN DENMARK,
THE NETHERLANDS, FRANCE, GREAT BRITAIN, PJM, and NEW YORK CITY,
JANUARY 2011 – 31 DECEMBER 2013
3) How Accurate are the Load Forecasts?

15. 4) How Accurate are the Wind Energy
Forecasts? What is the Trend in Forecast
Accuracy?
The reported capacity weighted root mean squared errors (CWRMSE) for wind energy are usually less than 10
percent. Adherents of this approach include Lange, et al. (2006, 2007), Cali et al. (2006), Krauss, et al. (2006),
Holttinen, et al. (2006), Kariniotakis, et al. (2006), and even NERC (2010, p. 9).
In a publication entitled, “Wind Power Myths Debunked,” Milligan, et al. (2009) draw on research from
Germany to argue that it is a fiction that wind energy is difficult to forecast. In their words: “In other
research conducted in Germany, typical wind forecast errors for a single wind project are 10% to 15% root
mean-squared error (RMSE) of installed wind capacity (emphasis added) but drop to 5% to 7% for all of
Germany.” (Milligan, et al. 2009, p. 93)
The UK’s Royal Academy of Engineering (2014, p. 33) has noted that wind energy’s capacity weighted forecast
error of about five percent is evidence that that the wind energy forecasts are highly accurate.
A report by the IPCC ( 2012 p, 623) on renewable energy indicates that wind energy is moderately predictable
as evidenced by a capacity weighted RMS forecast error that is less than 10%. Solar energy is reported to be
even more accurate.

16. The Literature on Forecast Accuracy
(Continued)
NREL (2013) implicitly endorses capacity
weighted RMSEs for wind energy but makes
use of energy weighted RMSEs when
discussing the accuracy of load forecasts.
NERC (2009) has been noted that forecast
accuracy has been improving:

17. Evidence in Germany of Declining Day-
Ahead Wind Energy Forecast Errors
Source: Cali, et. al. (2006)

18. Some Dissent
Forbes et. al. (2012) calculate a root-mean-
squared forecast error for wind energy in nine
electricity control areas. The RMSEs are
normalized by the mean level of wind energy
that is produced. The reported energy
weighted root mean squared errors
(EWRMSE) are in excess of 20 %.

19. Energy Weighted RMSEs for Wind Energy Forecasts
Control
Area/Zone Forecast Type Sample Period Observations Granularity
EWRMSE(%)
Great Britain Day-Ahead
1Jan2012
-31Dec3013 30842 Half-Hour 23.54
France Day-Ahead 17,349 Hourly 21.68
MISO (United States) Day-Ahead
1Jan2011 –
31Dec2015
42,768 Hourly 20.76
50Hertz (Germany) Day-Ahead
1Jan2011 –
31Dec2013
104,590 Quarter Hour 27.80
Eastern Denmark Day-Ahead
1Jan2011 –
31Dec2015 43,383 Hourly 27.79
Western Denmark Day-Ahead
1Jan2011 –
31Dec2015 43,293 Hourly 21.92
Sweden Day-Ahead
24Jan2015-
17August2016 13,679 Hourly 12.03
BPA (United States) Hour-Ahead
1Jan2012-
31May2015 31,893 Hourly 25.47

20. A Depiction of the Forecasting
Challenge in the Bonneville Power
Administration
Actual Wind Energy and Hour-Ahead Forecasted
Wind Energy in BPA, 1 Jan 2012 – 31 May 2015
0
10002000300040005000
0 2000 60004000
Hour Ahead Forecasted Wind Energy (MW)

21. Capacity Weighted RMSEs in 50Hertz
in Germany, 2005-2015
50Hertz

22. Energy Weighted RMSEs in 50Hertz in
Germany, 2005-2015
50Hertz

23. 5)From the point of view of a system operator, how does
wind energy compare with conventional forms of
generation? Evidence from Great Britain
• In Great Britain, each generating station informs the system operator of its
intended level of generation one hour prior to real-time. This value is known as
the final physical notification (FPN).
• Generators also submit bids (a proposal to reduce generation) and offers (a
proposal in increase generation) to provide balancing services
• During real-time, the system operator accepts the bids and offers based on
system conditions.
• In short, the revised generation schedule equals the FPN plus the level of
balancing services volume requested by the system operator.
• Failure to follow the revised generation schedule gives rise to an electricity
market imbalance that needs to be resolved by other generators.

24. The Revised Generation Schedules vs
Actual Generation: The Case of Coal in
Great Britain
0
2000400060008000
1000012000
MeteredGeneration(MWh)
0 2000 4000 6000 8000 10000 12000
Scheduled Generation including Balancing Actions (MWh)
EWRMSE = 2.5 %

25. The Revised Generation Schedules vs
Actual Generation: The Case of Combined
Cycle Gas Turbines in Great Britain
0
250050007500
1000012500
MeteredGeneration(MWh)
0 2500 5000 7500 10000 12500
Scheduled Generation including Balancing Actions (MWh)
EWRMSE = 5.6%

26. Actual vs. Scheduled Generation: The
Case of Nuclear Energy in Great
Britain
0
10002000300040005000
MeteredGeneration(MWh)
0 1000 2000 3000 4000 5000
Scheduled Generation (MWh)
EWRMSE = 7.4 %

27. The Revised Generation Schedules vs Actual Generation: The Case
of Wind Energy in Great Britain, 1 Jan 2012 – 31 Dec 2013
0
500
10001500200025003000
MeteredGeneration(MWh)
0 500 1000 1500 2000 2500 3000
Scheduled Generation including Balancing Actions (MWh)
EWRMSE= 18 %

28. Average Energy Imbalances by Fuel in
Great Britain, 1 Jan 2012- 31
December 2013

29. A Closer look at the Wind Energy
Imbalances, 1 Jan 2012 – 30 June 2014
-1500-1000
-500
0
500
1
Apr20121
Jul20121
O
ct2012
1
O
ct2013
1
Jul2013
1
Apr2013
1
Jan
2013
1
Apr2014
1
Jan
2014

30. 6) The Prospects for Improving both
the Load and Wind Energy Forecasts
• Significant improvements in day-ahead forecasts are
possible because there is evidence that the forecasts
do not fully reflect the information contained in the
day-ahead weather forecasts.
• In the case of load, forecast accuracy can sometimes be
enhanced by revising the forecast based on outcomes
in the day-ahead electricity market.
• Significant improvements in very short run forecasts
(e.g. one or two hours ahead) are possible by
exploiting the systematic nature of the existing forecast
errors.

31. Modeling Load
• The load model was estimated using data for Sweden
• The model was estimated using hourly data over the period 1 Jan
2010 – 30 June 2014.
• Explanatory variables include day-ahead forecasted load, forecasted
hourly temperature, forecasted hourly forecasted dewpoint,
forecasted forecasted humidity, forecasted measures of cloud cover
and the forecasted probability of precipation.
• The model also includes a measure of the day-ahead market
outcome and measures of the “shape” over the 24 hours of the
day-ahead load forecast.
• The model was estimated using ARCH/ARMA methods that takes
into account that the residual error terms have “fat tails.”
• The model was evaluated over the period 1 July 2014 – 30
November 2015

32. Modeling Wind Energy
• The wind energy model was estimated using hourly data for
Western Denmark
• The model was estimated using hourly data over the period 1 Jan
2011 – 31 December 2014.
• Explanatory variables include day-ahead hourly forecasted wind
energy, forecasted hourly temperature, forecasted wind speeds
forecasted hourly forecasted dew point, forecasted humidity,
forecasted measures of cloud cover and the forecasted probability
of precipitation.
• The model was estimated using ARCH/ARMA methods that takes
into account that the residual error terms have “fat tails.”
• The model was evaluated over the period 1 January 2015 – 13
March 2016

33. Results for Load
• Not surprising, the coefficient corresponding to forecasted
load is positive and highly statistically significant.
• The following forecasted weather variables are statistically
significant: temperature, windspeed, humidity, and
dewpoint. Two of the cloud cover variables are significant.
• The variables representing hour-of-the-day and “season”
are highly significant.
• Consistent with the day-ahead electricity market being
informationally efficient, the day-ahead price (relative to a
proxy for fuel costs) is positive and highly statistically
significant.
• Most of the ARCH/ARMA terms are highly significant.

34. Results for Wind Energy
• The predicative power of forecasted wind energy is highly
conditional on forecasted weather conditions (e.g.
forecasted temperature)
• The following forecasted weather variables are statistically
significant in their own right: temperature, windspeed,
humidity, and dewpoint. None of the binary variables
representing cloud cover variables are significant.
• Reflecting the possible effects of wind energy curtailments
by the system operator, the share of forecasted load
accounted for by forecasted wind energy is negative and
highly statistically significant.
• The variables representing “season” are highly significant.
• Most of the ARCH/ARMA terms are highly significant.

35. Actual and Day-Ahead Forecasted Load
in Sweden, 1 July 2014 – 24 November
2015
Actual Load in Sweden and an Out-of-Sample
Econometrically Modified Hour-Ahead Load
Forecast, 1 July 2014 – 24 November 2015
EWRMSE = 1.2 % EWRMSE = 3.0 %

36. Actual Wind Energy in Western
Denmark and an Out-of-Sample
Econometrically Modified Hour-
Ahead Wind Energy Forecast, 1
January 2015 – 13 March 2016
Actual and Day-Ahead Forecasted
Wind Energy in Western Denmark,
1 January 2015 – 13 March 2016
EWRMSE = 7.3 %
EWRMSE = 20.9 %

37. Conclusion
• The forthcoming large scale integration of
wind energy will increase the challenge of
maintaining the reliability of the power
system.
• The methods employed in the analysis
presented here may be useful in mitigating
this challenge.