Electricity demand forecasting plays an important role in short-term load allocation and long-term planning for future generation facilities and transmission augmentation. It is a challenging problem because of the different uncertainties including underlying population growth, changing technology, economic conditions, prevailing weather conditions (and the timing of those conditions), as well as the general randomness inherent in individual usage. It is also subject to some known calendar effects due to the time of day, day of week, time of year, and public holidays. But the most challenging part is that we often want to forecast the peak demand rather than the average demand. Consequently, it is necessary to adopt a probabilistic view of potential peak demand levels in order to evaluate and hedge the financial risk accrued by demand variability and forecasting uncertainty.
I will describe some Australian experiences in addressing these problems via the Monash Electricity Forecasting Model, a semiparametric additive model designed to take all the available information into account, and to provide forecast distributions from a few hours ahead to a few decades ahead. The approach is being used by energy market operators and supply companies to forecast the probability distribution of electricity demand in various regions of Australia.
I will briefly demonstrate an open-source R package to implement the model. The package allows for ensemble forecasting of demand based on simulations of future sample paths of temperatures and other predictor variables.
Finally, I will discuss some recent developments in evaluating peak demand forecasts, and some research competitions that have generated some innovative new methods to tackle energy forecasting problems.
Probabilistic forecasting of peak electricity demand
1. Probabilistic forecasting of
peak electricity demand
Rob J Hyndman
Joint work with Shu Fan
Probabilistic forecasting of peak electricity demand 1
2. Outline
1 The problem
2 The model
3 Forecasts
4 Challenges and extensions
5 Short term forecasts
6 Evaluating probabilistic forecasts
7 MEFM package
8 References and resources
Probabilistic forecasting of peak electricity demand The problem 2
3. The problem
We want to forecast the peak electricity
demand in a half-hour period in twenty years
time.
We have fifteen years of half-hourly electricity
data, temperature data and some economic
and demographic data.
The location is South Australia: home to the
most volatile electricity demand in the world.
Sounds impossible?
Probabilistic forecasting of peak electricity demand The problem 3
4. The problem
We want to forecast the peak electricity
demand in a half-hour period in twenty years
time.
We have fifteen years of half-hourly electricity
data, temperature data and some economic
and demographic data.
The location is South Australia: home to the
most volatile electricity demand in the world.
Sounds impossible?
Probabilistic forecasting of peak electricity demand The problem 3
5. South Australian demand data
Probabilistic forecasting of peak electricity demand The problem 4
6. South Australian demand data
Probabilistic forecasting of peak electricity demand The problem 4
Black Saturday →
10. South Australian demand data
Probabilistic forecasting of peak electricity demand The problem 6
SA State wide demand (summer 2015)
SAStatewidedemand(GW)
1.01.52.02.53.0
Oct Nov Dec Jan Feb Mar
11. South Australian demand data
Probabilistic forecasting of peak electricity demand The problem 6
12. Temperature data (Sth Aust)
Probabilistic forecasting of peak electricity demand The problem 7
13. Temperature data (Sth Aust)
Probabilistic forecasting of peak electricity demand The problem 8
10 20 30 40
1.01.52.02.53.03.5
Time: 12 midnight
Temperature (deg C)
Demand(GW)
Workday
Non−workday
15. Demand densities (Sth Aust)
Probabilistic forecasting of peak electricity demand The problem 10
1.0 1.5 2.0 2.5 3.0 3.5
01234
Density of demand: 12 midnight
South Australian half−hourly demand (GW)
Density
16. Outline
1 The problem
2 The model
3 Forecasts
4 Challenges and extensions
5 Short term forecasts
6 Evaluating probabilistic forecasts
7 MEFM package
8 References and resources
Probabilistic forecasting of peak electricity demand The model 11
17. Predictors
calendar effects
prevailing and recent weather conditions
climate changes
economic and demographic changes
changing technology
Modelling framework
Semi-parametric additive models with
correlated errors.
Each half-hour period modelled separately for
each season.
Variables selected to provide best
out-of-sample predictions using cross-validation
on each summer.
Probabilistic forecasting of peak electricity demand The model 12
18. Predictors
calendar effects
prevailing and recent weather conditions
climate changes
economic and demographic changes
changing technology
Modelling framework
Semi-parametric additive models with
correlated errors.
Each half-hour period modelled separately for
each season.
Variables selected to provide best
out-of-sample predictions using cross-validation
on each summer.
Probabilistic forecasting of peak electricity demand The model 12
19. Monash Electricity Forecasting Model
y∗
t = yt/¯yi
yt denotes per capita demand (minus offset) at
time t (measured in half-hourly intervals);
¯yi is the average demand for quarter i where t
is in quarter i.
y∗
t is the standardized demand for time t.
log(yt) = log(¯yi) + log(y∗
t )
log(¯yi) = f(GSP, price, HDD, CDD) + εi
log(y∗
t ) = f(calendar effects, temperatures) + et
Probabilistic forecasting of peak electricity demand The model 13
22. Annual model
log(yt) = log(¯yi) + log(y∗
t )
log(¯yi) = f(GSP, price, HDD, CDD) + εi
log(y∗
t ) = f(calendar effects, temperatures) + et
log(¯yi) = log(¯yi−1) +
j
cj(zj,i − zj,i−1) + εi
First differences modelled to avoid
non-stationary variables.
Predictors: Per-capita GSP, Price, Summer CDD,
Winter HDD.
Probabilistic forecasting of peak electricity demand The model 15
23. Annual model
log(yt) = log(¯yi) + log(y∗
t )
log(¯yi) = f(GSP, price, HDD, CDD) + εi
log(y∗
t ) = f(calendar effects, temperatures) + et
log(¯yi) = log(¯yi−1) +
j
cj(zj,i − zj,i−1) + εi
First differences modelled to avoid
non-stationary variables.
Predictors: Per-capita GSP, Price, Summer CDD,
Winter HDD.
Probabilistic forecasting of peak electricity demand The model 15
24. Annual model
log(yt) = log(¯yi) + log(y∗
t )
log(¯yi) = f(GSP, price, HDD, CDD) + εi
log(y∗
t ) = f(calendar effects, temperatures) + et
log(¯yi) = log(¯yi−1) +
j
cj(zj,i − zj,i−1) + εi
First differences modelled to avoid
non-stationary variables.
Predictors: Per-capita GSP, Price, Summer CDD,
Winter HDD.
zCDD =
summer
max(0, ¯T − 17.5)
¯T = daily mean
Probabilistic forecasting of peak electricity demand The model 15
25. Annual model
log(yt) = log(¯yi) + log(y∗
t )
log(¯yi) = f(GSP, price, HDD, CDD) + εi
log(y∗
t ) = f(calendar effects, temperatures) + et
log(¯yi) = log(¯yi−1) +
j
cj(zj,i − zj,i−1) + εi
First differences modelled to avoid
non-stationary variables.
Predictors: Per-capita GSP, Price, Summer CDD,
Winter HDD.
zHDD =
winter
max(0, 19.5 − ¯T)
¯T = daily mean
Probabilistic forecasting of peak electricity demand The model 15
26. Annual model
SA summer cooling degree days
Year
scdd
2002 2004 2006 2008 2010 2012 2014
300400500600700
SA winter heating degree days
800850
Probabilistic forecasting of peak electricity demand The model 16
27. Annual model
Variable Coefficient Std. Error t value P value
∆gsp.pc 2.02 5.05 0.38 0.711
∆price −1.67 0.68 −2.46 0.026
∆scdd 1.11 0.25 4.49 0.000
∆whdd 2.07 0.33 0.63 0.537
GSP needed to stay in the model to allow
scenario forecasting.
All other variables led to improved AICC.
Probabilistic forecasting of peak electricity demand The model 17
28. Annual model
Variable Coefficient Std. Error t value P value
∆gsp.pc 2.02 5.05 0.38 0.711
∆price −1.67 0.68 −2.46 0.026
∆scdd 1.11 0.25 4.49 0.000
∆whdd 2.07 0.33 0.63 0.537
GSP needed to stay in the model to allow
scenario forecasting.
All other variables led to improved AICC.
Probabilistic forecasting of peak electricity demand The model 17
29. Annual model
Variable Coefficient Std. Error t value P value
∆gsp.pc 2.02 5.05 0.38 0.711
∆price −1.67 0.68 −2.46 0.026
∆scdd 1.11 0.25 4.49 0.000
∆whdd 2.07 0.33 0.63 0.537
GSP needed to stay in the model to allow
scenario forecasting.
All other variables led to improved AICC.
Probabilistic forecasting of peak electricity demand The model 17
30. Annual model
Probabilistic forecasting of peak electricity demand The model 18
Year
Annualdemand
1.01.21.41.61.82.0
2002 2004 2006 2008 2010 2012 2014
Actual
Fitted
31. Monash Electricity Forecasting Model
log(yt) = log(¯yi) + log(y∗
t )
log(¯yi) = f(GSP, price, HDD, CDD) + εi
log(y∗
t ) = f(calendar effects, temperatures) + et
Calendar effects
“Time of summer” effect (a regression spline)
Day of week factor (7 levels)
Public holiday factor (4 levels)
New Year’s Eve factor (2 levels)
Probabilistic forecasting of peak electricity demand The model 19
32. Monash Electricity Forecasting Model
log(yt) = log(¯yi) + log(y∗
t )
log(¯yi) = f(GSP, price, HDD, CDD) + εi
log(y∗
t ) = f(calendar effects, temperatures) + et
Calendar effects
“Time of summer” effect (a regression spline)
Day of week factor (7 levels)
Public holiday factor (4 levels)
New Year’s Eve factor (2 levels)
Probabilistic forecasting of peak electricity demand The model 19
33. Monash Electricity Forecasting Model
log(yt) = log(¯yi) + log(y∗
t )
log(¯yi) = f(GSP, price, HDD, CDD) + εi
log(y∗
t ) = f(calendar effects, temperatures) + et
Calendar effects
“Time of summer” effect (a regression spline)
Day of week factor (7 levels)
Public holiday factor (4 levels)
New Year’s Eve factor (2 levels)
Probabilistic forecasting of peak electricity demand The model 19
34. Monash Electricity Forecasting Model
log(yt) = log(¯yi) + log(y∗
t )
log(¯yi) = f(GSP, price, HDD, CDD) + εi
log(y∗
t ) = f(calendar effects, temperatures) + et
Calendar effects
“Time of summer” effect (a regression spline)
Day of week factor (7 levels)
Public holiday factor (4 levels)
New Year’s Eve factor (2 levels)
Probabilistic forecasting of peak electricity demand The model 19
35. Fitted results (Summer 3pm)
Probabilistic forecasting of peak electricity demand The model 20
0 50 100 150
−0.40.00.4
Day of summer
Effectondemand
Mon Tue Wed Thu Fri Sat Sun
−0.40.00.4
Day of week
Effectondemand
Normal Day before Holiday Day after
−0.40.00.4
Holiday
Effectondemand
Time: 3:00 pm
36. Monash Electricity Forecasting Model
log(yt) = log(¯yi) + log(y∗
t )
log(¯yi) = f(GSP, price, HDD, CDD) + εi
log(y∗
t ) = f(calendar effects, temperatures) + et
Temperature effects
Ave temp across two sites, plus lags for
previous 3 hours and previous 3 days.
Temp difference between two sites, plus lags
for previous 3 hours and previous 3 days.
Max ave temp in past 24 hours.
Min ave temp in past 24 hours.
Ave temp in past seven days.
Each function estimated using boosted regression splines.
Probabilistic forecasting of peak electricity demand The model 21
37. Monash Electricity Forecasting Model
log(yt) = log(¯yi) + log(y∗
t )
log(¯yi) = f(GSP, price, HDD, CDD) + εi
log(y∗
t ) = f(calendar effects, temperatures) + et
Temperature effects
Ave temp across two sites, plus lags for
previous 3 hours and previous 3 days.
Temp difference between two sites, plus lags
for previous 3 hours and previous 3 days.
Max ave temp in past 24 hours.
Min ave temp in past 24 hours.
Ave temp in past seven days.
Each function estimated using boosted regression splines.
Probabilistic forecasting of peak electricity demand The model 21
38. Monash Electricity Forecasting Model
log(yt) = log(¯yi) + log(y∗
t )
log(¯yi) = f(GSP, price, HDD, CDD) + εi
log(y∗
t ) = f(calendar effects, temperatures) + et
Temperature effects
Ave temp across two sites, plus lags for
previous 3 hours and previous 3 days.
Temp difference between two sites, plus lags
for previous 3 hours and previous 3 days.
Max ave temp in past 24 hours.
Min ave temp in past 24 hours.
Ave temp in past seven days.
Each function estimated using boosted regression splines.
Probabilistic forecasting of peak electricity demand The model 21
39. Monash Electricity Forecasting Model
log(yt) = log(¯yi) + log(y∗
t )
log(¯yi) = f(GSP, price, HDD, CDD) + εi
log(y∗
t ) = f(calendar effects, temperatures) + et
Temperature effects
Ave temp across two sites, plus lags for
previous 3 hours and previous 3 days.
Temp difference between two sites, plus lags
for previous 3 hours and previous 3 days.
Max ave temp in past 24 hours.
Min ave temp in past 24 hours.
Ave temp in past seven days.
Each function estimated using boosted regression splines.
Probabilistic forecasting of peak electricity demand The model 21
40. Monash Electricity Forecasting Model
log(yt) = log(¯yi) + log(y∗
t )
log(¯yi) = f(GSP, price, HDD, CDD) + εi
log(y∗
t ) = f(calendar effects, temperatures) + et
Temperature effects
Ave temp across two sites, plus lags for
previous 3 hours and previous 3 days.
Temp difference between two sites, plus lags
for previous 3 hours and previous 3 days.
Max ave temp in past 24 hours.
Min ave temp in past 24 hours.
Ave temp in past seven days.
Each function estimated using boosted regression splines.
Probabilistic forecasting of peak electricity demand The model 21
41. Monash Electricity Forecasting Model
log(yt) = log(¯yi) + log(y∗
t )
log(¯yi) = f(GSP, price, HDD, CDD) + εi
log(y∗
t ) = f(calendar effects, temperatures) + et
Temperature effects
Ave temp across two sites, plus lags for
previous 3 hours and previous 3 days.
Temp difference between two sites, plus lags
for previous 3 hours and previous 3 days.
Max ave temp in past 24 hours.
Min ave temp in past 24 hours.
Ave temp in past seven days.
Each function estimated using boosted regression splines.
Probabilistic forecasting of peak electricity demand The model 21
42. Monash Electricity Forecasting Model
log(yt) = log(¯yi) + log(y∗
t )
log(¯yi) = f(GSP, price, HDD, CDD) + εi
log(y∗
t ) = f(calendar effects, temperatures) + et
Temperature effects
Ave temp across two sites, plus lags for
previous 3 hours and previous 3 days.
Temp difference between two sites, plus lags
for previous 3 hours and previous 3 days.
Max ave temp in past 24 hours.
Min ave temp in past 24 hours.
Ave temp in past seven days.
Each function estimated using boosted regression splines.
Probabilistic forecasting of peak electricity demand The model 21
43. Monash Electricity Forecasting Model
Temperature effects
6
k=0
fk,p(xt−k) + gk,p(dt−k) + qp(x+
t ) + rp(x−
t ) + sp(¯xt)
+
6
j=1
Fj,p(xt−48j) + Gj,p(dt−48j)
xt is ave temp across two sites at time t;
dt is the temp difference between two sites at
time t;
x+
t is max of xt values in past 24 hours;
x−
t is min of xt values in past 24 hours;
¯xt is ave temp in past seven days.
Probabilistic forecasting of peak electricity demand The model 22
44. Monash Electricity Forecasting Model
Temperature effects
6
k=0
fk,p(xt−k) + gk,p(dt−k) + qp(x+
t ) + rp(x−
t ) + sp(¯xt)
+
6
j=1
Fj,p(xt−48j) + Gj,p(dt−48j)
xt is ave temp across two sites at time t;
dt is the temp difference between two sites at
time t;
x+
t is max of xt values in past 24 hours;
x−
t is min of xt values in past 24 hours;
¯xt is ave temp in past seven days.
Probabilistic forecasting of peak electricity demand The model 22
45. Monash Electricity Forecasting Model
Temperature effects
6
k=0
fk,p(xt−k) + gk,p(dt−k) + qp(x+
t ) + rp(x−
t ) + sp(¯xt)
+
6
j=1
Fj,p(xt−48j) + Gj,p(dt−48j)
xt is ave temp across two sites at time t;
dt is the temp difference between two sites at
time t;
x+
t is max of xt values in past 24 hours;
x−
t is min of xt values in past 24 hours;
¯xt is ave temp in past seven days.
Probabilistic forecasting of peak electricity demand The model 22
46. Monash Electricity Forecasting Model
Temperature effects
6
k=0
fk,p(xt−k) + gk,p(dt−k) + qp(x+
t ) + rp(x−
t ) + sp(¯xt)
+
6
j=1
Fj,p(xt−48j) + Gj,p(dt−48j)
xt is ave temp across two sites at time t;
dt is the temp difference between two sites at
time t;
x+
t is max of xt values in past 24 hours;
x−
t is min of xt values in past 24 hours;
¯xt is ave temp in past seven days.
Probabilistic forecasting of peak electricity demand The model 22
47. Monash Electricity Forecasting Model
Temperature effects
6
k=0
fk,p(xt−k) + gk,p(dt−k) + qp(x+
t ) + rp(x−
t ) + sp(¯xt)
+
6
j=1
Fj,p(xt−48j) + Gj,p(dt−48j)
xt is ave temp across two sites at time t;
dt is the temp difference between two sites at
time t;
x+
t is max of xt values in past 24 hours;
x−
t is min of xt values in past 24 hours;
¯xt is ave temp in past seven days.
Probabilistic forecasting of peak electricity demand The model 22
48. Fitted results (Summer 3pm)
Probabilistic forecasting of peak electricity demand The model 23
10 20 30 40
−0.4−0.20.00.20.4
Temperature
Effectondemand
10 20 30 40
−0.4−0.20.00.20.4
Lag 1 temperature
Effectondemand
10 20 30 40
−0.4−0.20.00.20.4
Lag 2 temperature
Effectondemand
10 20 30 40
−0.4−0.20.00.20.4
Lag 3 temperature
Effectondemand
10 20 30 40
−0.4−0.20.00.20.4
Lag 1 day temperature
Effectondemand
10 15 20 25 30
−0.4−0.20.00.20.4
Last week average temp
Effectondemand
15 25 35
−0.4−0.20.00.20.4
Previous max temp
Effectondemand
10 15 20 25
−0.4−0.20.00.20.4
Previous min temp
Effectondemand
Time: 3:00 pm
49. Half-hourly models
log(y∗
t ) = f(calendar effects, temperatures) + et
Data split into working/non-working days, and
into night/day/evening (6 subsets).
Separate model for each half-hour period within
each subset (96 models).
Same predictors used for all models in a subset.
Predictors chosen by cross-validation on last
two summers.
Each model is fitted to the data twice, first
excluding the last summer and then excluding
the previous summer. Average out-of-sample
MSE calculated from omitted data.
Probabilistic forecasting of peak electricity demand The model 24
50. Half-hourly models
log(y∗
t ) = f(calendar effects, temperatures) + et
Data split into working/non-working days, and
into night/day/evening (6 subsets).
Separate model for each half-hour period within
each subset (96 models).
Same predictors used for all models in a subset.
Predictors chosen by cross-validation on last
two summers.
Each model is fitted to the data twice, first
excluding the last summer and then excluding
the previous summer. Average out-of-sample
MSE calculated from omitted data.
Probabilistic forecasting of peak electricity demand The model 24
51. Half-hourly models
log(y∗
t ) = f(calendar effects, temperatures) + et
Data split into working/non-working days, and
into night/day/evening (6 subsets).
Separate model for each half-hour period within
each subset (96 models).
Same predictors used for all models in a subset.
Predictors chosen by cross-validation on last
two summers.
Each model is fitted to the data twice, first
excluding the last summer and then excluding
the previous summer. Average out-of-sample
MSE calculated from omitted data.
Probabilistic forecasting of peak electricity demand The model 24
52. Half-hourly models
log(y∗
t ) = f(calendar effects, temperatures) + et
Data split into working/non-working days, and
into night/day/evening (6 subsets).
Separate model for each half-hour period within
each subset (96 models).
Same predictors used for all models in a subset.
Predictors chosen by cross-validation on last
two summers.
Each model is fitted to the data twice, first
excluding the last summer and then excluding
the previous summer. Average out-of-sample
MSE calculated from omitted data.
Probabilistic forecasting of peak electricity demand The model 24
53. Half-hourly models
log(y∗
t ) = f(calendar effects, temperatures) + et
Data split into working/non-working days, and
into night/day/evening (6 subsets).
Separate model for each half-hour period within
each subset (96 models).
Same predictors used for all models in a subset.
Predictors chosen by cross-validation on last
two summers.
Each model is fitted to the data twice, first
excluding the last summer and then excluding
the previous summer. Average out-of-sample
MSE calculated from omitted data.
Probabilistic forecasting of peak electricity demand The model 24
55. Half-hourly models
Probabilistic forecasting of peak electricity demand The model 26
60708090
R−squared
Time of day
R−squared(%)
12 midnight 6:00 am 9:00 am 12 noon 3:00 pm 6:00 pm 9:00 pm3:00 am 12 midnight
56. Half-hourly models
Probabilistic forecasting of peak electricity demand The model 26
Demand (January 2015)
Date in January
SAdemand(GW)
012345
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
Actual
Predicted
Temperatures (January 2015)
)
40
temp_23090
temp_23083
60. Outline
1 The problem
2 The model
3 Forecasts
4 Challenges and extensions
5 Short term forecasts
6 Evaluating probabilistic forecasts
7 MEFM package
8 References and resources
Probabilistic forecasting of peak electricity demand Forecasts 27
61. Peak demand forecasting
log(yt) = log(¯yi) + log(y∗
t )
log(¯yi) = f(GSP, price, HDD, CDD) + εi
log(y∗
t ) = f(calendar effects, temperatures) + et
Multiple alternative futures created:
Calendar effects known;
Future temperatures simulated
(taking account of climate change);
Assumed values for GSP, population and price;
Residuals simulated
Probabilistic forecasting of peak electricity demand Forecasts 28
62. Peak demand backcasting
log(yt) = log(¯yi) + log(y∗
t )
log(¯yi) = f(GSP, price, HDD, CDD) + εi
log(y∗
t ) = f(calendar effects, temperatures) + et
Multiple alternative pasts created:
Calendar effects known;
Past temperatures simulated;
Actual values for GSP, population and price;
Residuals simulated
Probabilistic forecasting of peak electricity demand Forecasts 28
68. Seasonal block bootstrapping
Conventional seasonal block bootstrap
Same as block bootstrap but with whole years as the
blocks to preserve seasonality.
But we only have about 10–15 years of data, so there is a
limited number of possible bootstrap samples.
Double seasonal block bootstrap
Suitable when there are two seasonal periods (here we
have years of 151 days and days of 48 half-hours).
Divide each year into blocks of length 48m.
Block 1 consists of the first m days of the year, block 2
consists of the next m days, and so on.
Bootstrap sample consists of a sample of blocks where
each block may come from a different randomly selected
year but must be at the correct time of year.
Probabilistic forecasting of peak electricity demand Forecasts 33
69. Seasonal block bootstrapping
Conventional seasonal block bootstrap
Same as block bootstrap but with whole years as the
blocks to preserve seasonality.
But we only have about 10–15 years of data, so there is a
limited number of possible bootstrap samples.
Double seasonal block bootstrap
Suitable when there are two seasonal periods (here we
have years of 151 days and days of 48 half-hours).
Divide each year into blocks of length 48m.
Block 1 consists of the first m days of the year, block 2
consists of the next m days, and so on.
Bootstrap sample consists of a sample of blocks where
each block may come from a different randomly selected
year but must be at the correct time of year.
Probabilistic forecasting of peak electricity demand Forecasts 33
70. Seasonal block bootstrapping
Conventional seasonal block bootstrap
Same as block bootstrap but with whole years as the
blocks to preserve seasonality.
But we only have about 10–15 years of data, so there is a
limited number of possible bootstrap samples.
Double seasonal block bootstrap
Suitable when there are two seasonal periods (here we
have years of 151 days and days of 48 half-hours).
Divide each year into blocks of length 48m.
Block 1 consists of the first m days of the year, block 2
consists of the next m days, and so on.
Bootstrap sample consists of a sample of blocks where
each block may come from a different randomly selected
year but must be at the correct time of year.
Probabilistic forecasting of peak electricity demand Forecasts 33
71. Seasonal block bootstrapping
Conventional seasonal block bootstrap
Same as block bootstrap but with whole years as the
blocks to preserve seasonality.
But we only have about 10–15 years of data, so there is a
limited number of possible bootstrap samples.
Double seasonal block bootstrap
Suitable when there are two seasonal periods (here we
have years of 151 days and days of 48 half-hours).
Divide each year into blocks of length 48m.
Block 1 consists of the first m days of the year, block 2
consists of the next m days, and so on.
Bootstrap sample consists of a sample of blocks where
each block may come from a different randomly selected
year but must be at the correct time of year.
Probabilistic forecasting of peak electricity demand Forecasts 33
72. Seasonal block bootstrapping
Conventional seasonal block bootstrap
Same as block bootstrap but with whole years as the
blocks to preserve seasonality.
But we only have about 10–15 years of data, so there is a
limited number of possible bootstrap samples.
Double seasonal block bootstrap
Suitable when there are two seasonal periods (here we
have years of 151 days and days of 48 half-hours).
Divide each year into blocks of length 48m.
Block 1 consists of the first m days of the year, block 2
consists of the next m days, and so on.
Bootstrap sample consists of a sample of blocks where
each block may come from a different randomly selected
year but must be at the correct time of year.
Probabilistic forecasting of peak electricity demand Forecasts 33
73. Seasonal block bootstrapping
Conventional seasonal block bootstrap
Same as block bootstrap but with whole years as the
blocks to preserve seasonality.
But we only have about 10–15 years of data, so there is a
limited number of possible bootstrap samples.
Double seasonal block bootstrap
Suitable when there are two seasonal periods (here we
have years of 151 days and days of 48 half-hours).
Divide each year into blocks of length 48m.
Block 1 consists of the first m days of the year, block 2
consists of the next m days, and so on.
Bootstrap sample consists of a sample of blocks where
each block may come from a different randomly selected
year but must be at the correct time of year.
Probabilistic forecasting of peak electricity demand Forecasts 33
74. Seasonal block bootstrapping
Conventional seasonal block bootstrap
Same as block bootstrap but with whole years as the
blocks to preserve seasonality.
But we only have about 10–15 years of data, so there is a
limited number of possible bootstrap samples.
Double seasonal block bootstrap
Suitable when there are two seasonal periods (here we
have years of 151 days and days of 48 half-hours).
Divide each year into blocks of length 48m.
Block 1 consists of the first m days of the year, block 2
consists of the next m days, and so on.
Bootstrap sample consists of a sample of blocks where
each block may come from a different randomly selected
year but must be at the correct time of year.
Probabilistic forecasting of peak electricity demand Forecasts 33
76. Seasonal block bootstrapping
Problems with the double seasonal bootstrap
Boundaries between blocks can introduce large
jumps. However, only at midnight.
Number of values that any given time in year is
still limited to the number of years in the data
set.
Probabilistic forecasting of peak electricity demand Forecasts 35
77. Seasonal block bootstrapping
Problems with the double seasonal bootstrap
Boundaries between blocks can introduce large
jumps. However, only at midnight.
Number of values that any given time in year is
still limited to the number of years in the data
set.
Probabilistic forecasting of peak electricity demand Forecasts 35
78. Seasonal block bootstrapping
Variable length double seasonal block
bootstrap
Blocks allowed to vary in length between m − ∆
and m + ∆ days where 0 ≤ ∆ < m.
Blocks allowed to move up to ∆ days from their
original position.
Has little effect on the overall time series
patterns provided ∆ is relatively small.
Use uniform distribution on (m − ∆, m + ∆) to
select block length, and independent uniform
distribution on (−∆, ∆) to select variation on
starting position for each block.
Probabilistic forecasting of peak electricity demand Forecasts 36
79. Seasonal block bootstrapping
Variable length double seasonal block
bootstrap
Blocks allowed to vary in length between m − ∆
and m + ∆ days where 0 ≤ ∆ < m.
Blocks allowed to move up to ∆ days from their
original position.
Has little effect on the overall time series
patterns provided ∆ is relatively small.
Use uniform distribution on (m − ∆, m + ∆) to
select block length, and independent uniform
distribution on (−∆, ∆) to select variation on
starting position for each block.
Probabilistic forecasting of peak electricity demand Forecasts 36
80. Seasonal block bootstrapping
Variable length double seasonal block
bootstrap
Blocks allowed to vary in length between m − ∆
and m + ∆ days where 0 ≤ ∆ < m.
Blocks allowed to move up to ∆ days from their
original position.
Has little effect on the overall time series
patterns provided ∆ is relatively small.
Use uniform distribution on (m − ∆, m + ∆) to
select block length, and independent uniform
distribution on (−∆, ∆) to select variation on
starting position for each block.
Probabilistic forecasting of peak electricity demand Forecasts 36
81. Seasonal block bootstrapping
Variable length double seasonal block
bootstrap
Blocks allowed to vary in length between m − ∆
and m + ∆ days where 0 ≤ ∆ < m.
Blocks allowed to move up to ∆ days from their
original position.
Has little effect on the overall time series
patterns provided ∆ is relatively small.
Use uniform distribution on (m − ∆, m + ∆) to
select block length, and independent uniform
distribution on (−∆, ∆) to select variation on
starting position for each block.
Probabilistic forecasting of peak electricity demand Forecasts 36
84. Outline
1 The problem
2 The model
3 Forecasts
4 Challenges and extensions
5 Short term forecasts
6 Evaluating probabilistic forecasts
7 MEFM package
8 References and resources
Probabilistic forecasting of peak electricity demand Challenges and extensions 38
85. Challenges
Some judgmental adjustments are done to
account for demand response activity.
We have a separate model for PV generation
based on solar radiation and temperatures. But
limited PV data available, so PV adjustments
are probably inaccurate.
Difficult to account for new technology such as
local storage intended to flatten demand.
Climate change effect is assumed additive at
all temperature levels — probably simplistic.
Probabilistic forecasting of peak electricity demand Challenges and extensions 39
86. Challenges
Some judgmental adjustments are done to
account for demand response activity.
We have a separate model for PV generation
based on solar radiation and temperatures. But
limited PV data available, so PV adjustments
are probably inaccurate.
Difficult to account for new technology such as
local storage intended to flatten demand.
Climate change effect is assumed additive at
all temperature levels — probably simplistic.
Probabilistic forecasting of peak electricity demand Challenges and extensions 39
87. Challenges
Some judgmental adjustments are done to
account for demand response activity.
We have a separate model for PV generation
based on solar radiation and temperatures. But
limited PV data available, so PV adjustments
are probably inaccurate.
Difficult to account for new technology such as
local storage intended to flatten demand.
Climate change effect is assumed additive at
all temperature levels — probably simplistic.
Probabilistic forecasting of peak electricity demand Challenges and extensions 39
88. Challenges
Some judgmental adjustments are done to
account for demand response activity.
We have a separate model for PV generation
based on solar radiation and temperatures. But
limited PV data available, so PV adjustments
are probably inaccurate.
Difficult to account for new technology such as
local storage intended to flatten demand.
Climate change effect is assumed additive at
all temperature levels — probably simplistic.
Probabilistic forecasting of peak electricity demand Challenges and extensions 39
89. Implementation
Probabilistic forecasting of peak electricity demand Challenges and extensions 40
Our model is used for long-term forecasting in:
Victoria’s Vision 2030 energy plan;
all regions of the National Energy Market;
South Western Interconnected System
(WA);
some local distributors.
90. Implementation
Probabilistic forecasting of peak electricity demand Challenges and extensions 40
Our model is used for long-term forecasting in:
Victoria’s Vision 2030 energy plan;
all regions of the National Energy Market;
South Western Interconnected System
(WA);
some local distributors.
It is also used for short-term
forecasting comparisons in:
all regions of the
National Energy Market.
91. Outline
1 The problem
2 The model
3 Forecasts
4 Challenges and extensions
5 Short term forecasts
6 Evaluating probabilistic forecasts
7 MEFM package
8 References and resources
Probabilistic forecasting of peak electricity demand Short term forecasts 41
92. Short-term forecasts
log(yt) = log(¯yi) + log(y∗
t )
log(¯yi) = f(GSP, price, HDD, CDD) + εi
log(y∗
t ) = f(calendar effects, temperatures) + et
Simulating temperatures and residuals is ok for
long-term forecasts because short-term
dynamics wash out after a few weeks.
But short-term forecasts need to take account
of recent temperatures and recent residuals
due to serial correlation.
Short-term temperature forecasts are available.
Probabilistic forecasting of peak electricity demand Short term forecasts 42
93. Short-term forecasts
log(yt) = log(¯yi) + log(y∗
t )
log(¯yi) = f(GSP, price, HDD, CDD) + εi
log(y∗
t ) = f(calendar effects, temperatures) + et
Simulating temperatures and residuals is ok for
long-term forecasts because short-term
dynamics wash out after a few weeks.
But short-term forecasts need to take account
of recent temperatures and recent residuals
due to serial correlation.
Short-term temperature forecasts are available.
Probabilistic forecasting of peak electricity demand Short term forecasts 42
94. Short-term forecasts
log(yt) = log(¯yi) + log(y∗
t )
log(¯yi) = f(GSP, price, HDD, CDD) + εi
log(y∗
t ) = f(calendar effects, temperatures) + et
Simulating temperatures and residuals is ok for
long-term forecasts because short-term
dynamics wash out after a few weeks.
But short-term forecasts need to take account
of recent temperatures and recent residuals
due to serial correlation.
Short-term temperature forecasts are available.
Probabilistic forecasting of peak electricity demand Short term forecasts 42
95. Short-term forecasting model
log(yt) = log(¯yi) + log(y∗
t )
log(¯yi) = f(GSP, price, HDD, CDD) + εi
log(y∗
t ) = f(calendar effects, temperatures,
lagged demand) + et
Lagged demand inputs
Demand in last 3 hours and last 3 days;
Maximum demand in past 24 hours;
Minimum demand in past 24 hours;
Average demand in past 7 days
Each function is estimated using boosted regression splines.
Probabilistic forecasting of peak electricity demand Short term forecasts 43
96. Short-term forecasting model
log(yt) = log(¯yi) + log(y∗
t )
log(¯yi) = f(GSP, price, HDD, CDD) + εi
log(y∗
t ) = f(calendar effects, temperatures,
lagged demand) + et
Lagged demand inputs
Demand in last 3 hours and last 3 days;
Maximum demand in past 24 hours;
Minimum demand in past 24 hours;
Average demand in past 7 days
Each function is estimated using boosted regression splines.
Probabilistic forecasting of peak electricity demand Short term forecasts 43
99. Outline
1 The problem
2 The model
3 Forecasts
4 Challenges and extensions
5 Short term forecasts
6 Evaluating probabilistic forecasts
7 MEFM package
8 References and resources
Probabilistic forecasting of peak electricity demand Evaluating probabilistic forecasts 45
100. Forecast accuracy measures
MAE: Mean absolute error
MSE: Mean squared error
MAPE: Mean absolute percentage error
¯ Good when forecasting a typical future value
(e.g., the mean or median).
Probabilistic forecasting of peak electricity demand Evaluating probabilistic forecasts 46
101. Forecast accuracy measures
MAE: Mean absolute error
MSE: Mean squared error
MAPE: Mean absolute percentage error
¯ Good when forecasting a typical future value
(e.g., the mean or median).
Probabilistic forecasting of peak electricity demand Evaluating probabilistic forecasts 46
102. Forecast accuracy measures
MAE: Mean absolute error
MSE: Mean squared error
MAPE: Mean absolute percentage error
¯ Good when forecasting a typical future value
(e.g., the mean or median).
Probabilistic forecasting of peak electricity demand Evaluating probabilistic forecasts 46
103. Forecast accuracy measures
MAE: Mean absolute error
MSE: Mean squared error
MAPE: Mean absolute percentage error
¯ Good when forecasting a typical future value
(e.g., the mean or median).
Probabilistic forecasting of peak electricity demand Evaluating probabilistic forecasts 46
104. Forecast accuracy measures
MAE: Mean absolute error
MSE: Mean squared error
MAPE: Mean absolute percentage error
¯ Good when forecasting a typical future value
(e.g., the mean or median).
¯ Useless for evaluating forecast percentiles
(probability of exceedance values) and forecast
distributions.
Probabilistic forecasting of peak electricity demand Evaluating probabilistic forecasts 46
117. Forecast scoring
Let Qt(1), . . . , Qt(99) be the PoEs of the forecast
distribution for probabilities 1%,. . . ,99%. Then the
score for observation y is
S(Qt(i), yt) =
1
100
i(Qt(i) − yt) if yt < Qt(i)
1
100
(100 − i)(yt − Qt(i)) if yt ≥ Qt(i)
Scores are averaged over all observed data for
each i to measure the accuracy of the forecasts
for each percentile.
Average score over all percentiles gives the
best distribution forecast.
Takes account of how far PoEs are exceeded.
Probabilistic forecasting of peak electricity demand Evaluating probabilistic forecasts 51
118. Forecast scoring
Let Qt(1), . . . , Qt(99) be the PoEs of the forecast
distribution for probabilities 1%,. . . ,99%. Then the
score for observation y is
S(Qt(i), yt) =
1
100
i(Qt(i) − yt) if yt < Qt(i)
1
100
(100 − i)(yt − Qt(i)) if yt ≥ Qt(i)
Scores are averaged over all observed data for
each i to measure the accuracy of the forecasts
for each percentile.
Average score over all percentiles gives the
best distribution forecast.
Takes account of how far PoEs are exceeded.
Probabilistic forecasting of peak electricity demand Evaluating probabilistic forecasts 51
119. Forecast scoring
Let Qt(1), . . . , Qt(99) be the PoEs of the forecast
distribution for probabilities 1%,. . . ,99%. Then the
score for observation y is
S(Qt(i), yt) =
1
100
i(Qt(i) − yt) if yt < Qt(i)
1
100
(100 − i)(yt − Qt(i)) if yt ≥ Qt(i)
Scores are averaged over all observed data for
each i to measure the accuracy of the forecasts
for each percentile.
Average score over all percentiles gives the
best distribution forecast.
Takes account of how far PoEs are exceeded.
Probabilistic forecasting of peak electricity demand Evaluating probabilistic forecasts 51
120. GEFCom2014
Probabilistic forecasting of demand, price,
wind, and solar.
Rolling forecasts with incremental data update
on a weekly basis.
Forecasts submitted in the form of percentiles
of future distributions.
Evaluation based on quantile scoring.
Prizes for student teams, and for best methods.
Winning methods to be published in the IJF.
Probabilistic forecasting of peak electricity demand Evaluating probabilistic forecasts 52
121. GEFCom2014
Probabilistic forecasting of demand, price,
wind, and solar.
Rolling forecasts with incremental data update
on a weekly basis.
Forecasts submitted in the form of percentiles
of future distributions.
Evaluation based on quantile scoring.
Prizes for student teams, and for best methods.
Winning methods to be published in the IJF.
Probabilistic forecasting of peak electricity demand Evaluating probabilistic forecasts 52
122. GEFCom2014
Probabilistic forecasting of demand, price,
wind, and solar.
Rolling forecasts with incremental data update
on a weekly basis.
Forecasts submitted in the form of percentiles
of future distributions.
Evaluation based on quantile scoring.
Prizes for student teams, and for best methods.
Winning methods to be published in the IJF.
Probabilistic forecasting of peak electricity demand Evaluating probabilistic forecasts 52
123. GEFCom2014
Probabilistic forecasting of demand, price,
wind, and solar.
Rolling forecasts with incremental data update
on a weekly basis.
Forecasts submitted in the form of percentiles
of future distributions.
Evaluation based on quantile scoring.
Prizes for student teams, and for best methods.
Winning methods to be published in the IJF.
Probabilistic forecasting of peak electricity demand Evaluating probabilistic forecasts 52
124. GEFCom2014
Probabilistic forecasting of demand, price,
wind, and solar.
Rolling forecasts with incremental data update
on a weekly basis.
Forecasts submitted in the form of percentiles
of future distributions.
Evaluation based on quantile scoring.
Prizes for student teams, and for best methods.
Winning methods to be published in the IJF.
Probabilistic forecasting of peak electricity demand Evaluating probabilistic forecasts 52
125. GEFCom2014
Probabilistic forecasting of demand, price,
wind, and solar.
Rolling forecasts with incremental data update
on a weekly basis.
Forecasts submitted in the form of percentiles
of future distributions.
Evaluation based on quantile scoring.
Prizes for student teams, and for best methods.
Winning methods to be published in the IJF.
Probabilistic forecasting of peak electricity demand Evaluating probabilistic forecasts 52
127. Outline
1 The problem
2 The model
3 Forecasts
4 Challenges and extensions
5 Short term forecasts
6 Evaluating probabilistic forecasts
7 MEFM package
8 References and resources
Probabilistic forecasting of peak electricity demand MEFM package 54
128. MEFM package for R
Available on github:
install.packages("devtools")
library(devtools)
install_github("robjhyndman/MEFM-package")
Package contents:
seasondays The number of days in a season
sa.econ Historical demographic & economic data for
South Australia
sa Historical data for model estimation
maketemps Create lagged temperature variables
demand_model Estimate the electricity demand models
simulate_ddemand Temperature and demand simulation
simulate_demand Simulate the electricity demand for the next
season
Probabilistic forecasting of peak electricity demand MEFM package 55
129. MEFM package for R
Available on github:
install.packages("devtools")
library(devtools)
install_github("robjhyndman/MEFM-package")
Package contents:
seasondays The number of days in a season
sa.econ Historical demographic & economic data for
South Australia
sa Historical data for model estimation
maketemps Create lagged temperature variables
demand_model Estimate the electricity demand models
simulate_ddemand Temperature and demand simulation
simulate_demand Simulate the electricity demand for the next
season
Probabilistic forecasting of peak electricity demand MEFM package 55
130. MEFM package for R
Usage
library(MEFM)
# Number of days in each "season"
seasondays
# Historical economic data
sa.econ
# Historical temperature and calendar data
head(sa)
tail(sa)
dim(sa)
# create lagged temperature variables
salags <- maketemps(sa,2,48)
dim(salags)
head(salags)
Probabilistic forecasting of peak electricity demand MEFM package 56
131. MEFM package for R
# formula for annual model
formula.a <- as.formula(anndemand ~ gsp + ddays + resiprice)
# formulas for half-hourly model
# These can be different for each half-hour
formula.hh <- list()
for(i in 1:48) {
formula.hh[[i]] <- as.formula(log(ddemand) ~ ns(temp, df=2)
+ day + holiday
+ ns(timeofyear, df=9) + ns(avetemp, df=3)
+ ns(dtemp, df=3) + ns(lastmin, df=3)
+ ns(prevtemp1, df=2) + ns(prevtemp2, df=2)
+ ns(prevtemp3, df=2) + ns(prevtemp4, df=2)
+ ns(day1temp, df=2) + ns(day2temp, df=2)
+ ns(day3temp, df=2) + ns(prevdtemp1, df=3)
+ ns(prevdtemp2, df=3) + ns(prevdtemp3, df=3)
+ ns(day1dtemp, df=3))
}
Probabilistic forecasting of peak electricity demand MEFM package 57
132. MEFM package for R
# Fit all models
sa.model <- demand_model(salags, sa.econ, formula.hh, formula.a)
# Summary of annual model
summary(sa.model$a)
# Summary of half-hourly model at 4pm
summary(sa.model$hh[[33]])
# Simulate future normalized half-hourly data
simdemand <- simulate_ddemand(sa.model, sa, simyears=50)
# economic forecasts, to be given by user
afcast <- data.frame(pop=1694, gsp=22573, resiprice=34.65,
ddays=642)
# Simulate half-hourly data
demand <- simulate_demand(simdemand, afcast)
Probabilistic forecasting of peak electricity demand MEFM package 58
133. MEFM package for R
plot(ts(demand$demand[,sample(1:100, 4)], freq=48, start=0),
xlab="Days", main="Simulated demand futures")
Probabilistic forecasting of peak electricity demand MEFM package 59
134. MEFM package for R
plot(ts(demand$demand[,sample(1:100, 4)], freq=48, start=0),
xlab="Days", main="Simulated demand futures")0.61.01.4
Series52
0.51.52.5
Series49
0.51.52.5
Series88
0.61.21.8
0 50 100 150
Series53
Days
Simulated demand futures
Probabilistic forecasting of peak electricity demand MEFM package 59
135. MEFM package for R
plot(demand$annmax, main="Simulated seasonal maximums",
ylab="GW")
Probabilistic forecasting of peak electricity demand MEFM package 60
136. MEFM package for R
plot(demand$annmax, main="Simulated seasonal maximums",
ylab="GW")
0 20 40 60 80 100
1.52.02.53.0
Simulated seasonal maximums
Index
GW
Probabilistic forecasting of peak electricity demand MEFM package 60
137. MEFM package for R
boxplot(demand$annmax, main="Simulated seasonal maximums",
xlab="GW", horizontal=TRUE)
rug(demand$annmax)
Probabilistic forecasting of peak electricity demand MEFM package 61
138. MEFM package for R
boxplot(demand$annmax, main="Simulated seasonal maximums",
xlab="GW", horizontal=TRUE)
rug(demand$annmax)
1.5 2.0 2.5 3.0
Simulated seasonal maximums
GW
Probabilistic forecasting of peak electricity demand MEFM package 61
139. MEFM package for R
plot(density(demand$annmax, bw="SJ"), xlab="Demand (GW)",
main="Density of seasonal maximum demand")
rug(demand$annmax)
Probabilistic forecasting of peak electricity demand MEFM package 62
140. MEFM package for R
plot(density(demand$annmax, bw="SJ"), xlab="Demand (GW)",
main="Density of seasonal maximum demand")
rug(demand$annmax)
1.5 2.0 2.5 3.0 3.5
0.00.40.81.2
Density of seasonal maximum demand
Demand (GW)
Density
Probabilistic forecasting of peak electricity demand MEFM package 62
141. Outline
1 The problem
2 The model
3 Forecasts
4 Challenges and extensions
5 Short term forecasts
6 Evaluating probabilistic forecasts
7 MEFM package
8 References and resources
Probabilistic forecasting of peak electricity demand References and resources 63
142. References
¯ Hyndman, R.J. & Fan, S. (2010)
“Density forecasting for long-term peak electricity demand”,
IEEE Transactions on Power Systems, 25(2), 1142–1153.
¯ Fan, S. & Hyndman, R.J. (2012) “Short-term load forecasting
based on a semi-parametric additive model”.
IEEE Transactions on Power Systems, 27(1), 134–141.
¯ Ben Taieb, S. & Hyndman, R.J. (2013) “A gradient boosting
approach to the Kaggle load forecasting competition”,
International Journal of Forecasting, 29(4).
¯ Hyndman, R.J., & Fan, S. (2014).
“Monash Electricity Forecasting Model”. Technical paper.
robjhyndman.com/working-papers/mefm/
¯ Fan, S., & Hyndman, R.J. (2014). “MEFM: An R package imple-
menting the Monash Electricity Forecasting Model.”
github.com/robjhyndman/MEFM-package
Probabilistic forecasting of peak electricity demand References and resources 64
143. Some resources
Blogs
robjhyndman.com/hyndsight/
blog.drhongtao.com/
Organizations
International Institute of Forecasters:
forecasters.org
IEEE Working Group on Energy Forecasting:
linkedin.com/groups/
IEEE-Working-Group-on-Energy-4148276
Books
Dickey and Hong (2014) Electric load
forecasting: fundamentals and best practices,
OTexts. www.otexts.org/book/elf
Probabilistic forecasting of peak electricity demand References and resources 65
144. Some resources
Blogs
robjhyndman.com/hyndsight/
blog.drhongtao.com/
Organizations
International Institute of Forecasters:
forecasters.org
IEEE Working Group on Energy Forecasting:
linkedin.com/groups/
IEEE-Working-Group-on-Energy-4148276
Books
Dickey and Hong (2014) Electric load
forecasting: fundamentals and best practices,
OTexts. www.otexts.org/book/elf
Probabilistic forecasting of peak electricity demand References and resources 65
145. Some resources
Blogs
robjhyndman.com/hyndsight/
blog.drhongtao.com/
Organizations
International Institute of Forecasters:
forecasters.org
IEEE Working Group on Energy Forecasting:
linkedin.com/groups/
IEEE-Working-Group-on-Energy-4148276
Books
Dickey and Hong (2014) Electric load
forecasting: fundamentals and best practices,
OTexts. www.otexts.org/book/elf
Probabilistic forecasting of peak electricity demand References and resources 65
146. Some resources
Blogs
robjhyndman.com/hyndsight/
blog.drhongtao.com/
Organizations
International Institute of Forecasters:
forecasters.org
IEEE Working Group on Energy Forecasting:
linkedin.com/groups/
IEEE-Working-Group-on-Energy-4148276
Books
Dickey and Hong (2014) Electric load
forecasting: fundamentals and best practices,
OTexts. www.otexts.org/book/elf
Probabilistic forecasting of peak electricity demand References and resources 65
147. Some resources
Blogs
robjhyndman.com/hyndsight/
blog.drhongtao.com/
Organizations
International Institute of Forecasters:
forecasters.org
IEEE Working Group on Energy Forecasting:
linkedin.com/groups/
IEEE-Working-Group-on-Energy-4148276
Books
Dickey and Hong (2014) Electric load
forecasting: fundamentals and best practices,
OTexts. www.otexts.org/book/elf
Probabilistic forecasting of peak electricity demand References and resources 65
148. Some resources
Blogs
robjhyndman.com/hyndsight/
blog.drhongtao.com/
Organizations
International Institute of Forecasters:
forecasters.org
IEEE Working Group on Energy Forecasting:
linkedin.com/groups/
IEEE-Working-Group-on-Energy-4148276
Books
Dickey and Hong (2014) Electric load
forecasting: fundamentals and best practices,
OTexts. www.otexts.org/book/elf
Probabilistic forecasting of peak electricity demand References and resources 65
149. Some resources
Blogs
robjhyndman.com/hyndsight/
blog.drhongtao.com/
Organizations
International Institute of Forecasters:
forecasters.org
IEEE Working Group on Energy Forecasting:
linkedin.com/groups/
IEEE-Working-Group-on-Energy-4148276
Books
Dickey and Hong (2014) Electric load
forecasting: fundamentals and best practices,
OTexts. www.otexts.org/book/elf
Probabilistic forecasting of peak electricity demand References and resources 65