IEEE Power & Energy Magazine - May/June 2022 - 29

Model Inputs
pd−1
pd−2
...
pd−7
Xd
...
D
(a)
u2
...
un
pd,2
...
pd,24
...
D
(b)
figure 5. The multioutput NN architectures for EPF: (a) shallow and (b) deep with two hidden layers. The notation is similar
to that in Figures 1 and 3. Dotted arrows represent feedback connections; if present, the structure is called a recurrent
NN. (Source: The human evolution graphic is a modification of the " Human Evolution Scheme " by José-Manuel Benitos.
Licensed under the terms of the GNU Free Documentation License.)
Model Performance
Let us now compare the predictive accuracy of the two challenging
benchmarks-LEAR and DNNs. Here, we report on
a comprehensive study involving five major DA electricity
markets with test periods spanning two years each. Three
out of the five markets-Germany, France, and Belgium-
are operated by European Power Exchange (EPEX SPOT),
the largest power exchange in Europe. In their case, the test
periods range from 4 January 2016 to 31 December 2017
for Germany and 4 January 2015 to 31 December 2016 for
France and Belgium. The remaining two include Nord Pool,
one of the world's oldest power markets, operating in Scandinavia
and the Baltic states, and the Pennsylvania-New Jersey-Maryland
(PJM) Interconnection, the largest competitive
wholesale electricity market in the Americas. In their
case, the test periods range from 27 December 2016 to 24
December 2018.
Figure 6 plots the performance of a LEAR and DNN
model. The errors are computed with respect to a naive
forecast, which takes last week's price for the same hour.
Both models provide significantly more accurate forecasts
in terms of the mean absolute error (MAE) than the
naive prediction; if plotted, the relative MAE (rMAE) of
the naive method would be one. For instance, for the PJM
market, the MAE values of both models are lower by 40%
(rMAE = ~0.6) than that of the naive approach while, for the
may/june 2022
German EPEX market, by as much as 55% (rMAE = ~0.45).
Moreover, for all five electricity markets, the LEAR model
is outperformed by the DNN. Clearly, there is a benefit to
using the more complex method. However, it comes at a
Relative MAE
PJM
LEAR
DNN
0.7
0.6
Nord
Pool
EPEX
0.5
0.4
Germany
u1
pd,1
Hidden Layer Model Outputs
Model Inputs Hidden Layer 1Hidden Layer 2 Model Outputs
pd−1
pd−2
...
pd−7
Xd
u1,n
u2,m
pd,24
u1,2
...
u2,2
...
pd,2
...
u1,1
u2,1
pd,1
EPEX
France
EPEX
Belgium
figure 6. A radar plot of the relative mean absolute errors
(rMAEs) for the LEAR and DNN models and two-year test
periods from five power markets.
ieee power & energy magazine
29

IEEE Power & Energy Magazine - May/June 2022

Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - May/June 2022

Contents
IEEE Power & Energy Magazine - May/June 2022 - Cover1
IEEE Power & Energy Magazine - May/June 2022 - Cover2
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IEEE Power & Energy Magazine - May/June 2022 - Cover3
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