lslog | R Documentation |
Logistic Method for Matching Peak and Load Factor
lslog( x, target_max = 10000, target_lf = 0.7, k = 1, inf_pos = 0.5, iter = 500, def_l = 1 )
x |
A numeric array, representing reference load shape.
All values must be strictly positive containing no |
target_max |
Target peak value of resultant load shape, must be > 0. |
target_lf |
Target load factor of resultant load shape, must be numeric in between 0 and 1 (exclusive). |
k |
Steepness parameter, must be a positive number. See "Details". |
inf_pos |
Inflection point parameter. See "Details". |
iter |
Number of iterations for solving certain parameter. Must be >= 30. See "Details". |
def_l |
Start parameter for solving |
The algorithm first evaluates the load factor of the
reference load shape x
, which is
defined by the ratio of average to peak value. If the target load
factor is greater than reference level, then all base
values are multiplied by a number > 1
. If the target
load factor is less than reference level, then all base values are
multiplied by a number < 1
. The multipliers
increase or decrease with a sigmoid pattern.
The sigmoid function is a transformed version of
f(x)=\frac{L}{1 - exp(-k(x-x_0))}
Parameter k is shape parameter, shaping the
"sigmoidness" of the function. Larger value of k
indicates more steepness in the function and lower value
results in changes in multipliers in more linear fashion.
Location parameter x_0 controls the inflection point
of the function and derived from inf_pos
.
inf_pos = 0.5
indicates the inflection point of
the sigmoid multipliers is halfway.
The L parameter in the sigmoid is numerically solved.
The number of iterations is equal to the iter
argument,
optimized based on the minimum difference between the derived
and target load factor.
The return object contains a data frame df
, having the
following columns:
x_index
: An index given to the original load
shape x
, starting from 1 to length(x)
.
x
: The original array x
, unaltered.
x_rank
: The rank of the data points of the
given array x
, from 1 for the peak to
length(x)
for the lowest value.
x_ordered
: Sorted x
(largest to smallest).
x_pu
: Per unit x
, derived by diving x
by max(x)
.
x_ordered_pu
: Per unit x
, sorted from largest
to smallest.
mult
: Derived multipliers, would be applied to
sorted per unit x
.
y_ordered_pu
: Product of per unit sorted x
and mult
.
y_ordered_pu2
: y_ordered_pu
, sorted again,
in case y_ordered_pu
does not become decreasing.
y_pu
: Resultant load shape in per unit. This is
derived by re-ordering y_ordered_pu2
with respect to their
original rank.
y
: Resultant load shape. This is derived by
multiplying y_pu
by taget_max
/ base_max
A list of class "lslog"
, having following elements:
df
: A data frame. See "Details".
k
: Steepness parameter. See "Details".
inf_pos
: Inflection point parameter.
See "Details".
L
: Numerically solved optimized L parameter.
See "Details".
max_mult
: Maximum of the multipliers.
min_mult
: Minimum of the multipliers.
base_load_factor
: Load factor of the reference load
shape x
.
target_load_factor
: Target load factor.
derived_load_factor
: Load factor of the derived load shape
(object$df$y
).
base_max
: Peak value of the base load shape, x
target_max
: Target peak value of the new load shape.
derived_max
: Peak value of the derived load shape
(object$df$y
)
base_min
: Minimum value of the base load shape, x
derived_min
: Minimum value of the derived load shape
(object$df$y
)
dec_flag
: A logical flag stating
whether the multipliers resulted in strictly decreasing values.
TRUE
indicates the order was not preserved.
Only applicable for target_max
> base_max
.
See "Details".
lf_flag
: A logical flag indicating if the
load factor of the derived shape differs from the target
by more than 1%.
min_pu_flag
: A logical flag indicating existence of
negative values in the derived load shape. TRUE
indicates
the existence of negative values.
Only applicable for target_max
< base_max
.
See "Details".
lslin
,
print.lslog
,
summary.lslog
,
plot.lslog
,
lscore
loads <- ercot[ercot$Year == 2019, ]$COAST plot(loads, type = "l") logistic_loadshape <- lslog(loads, target_lf = 0.50, k = 0.5) summary(logistic_loadshape) #--------------------------------------------------- loads2 <- ercot[ercot$Year == 2020, ]$ERCOT plot(loads2, type = "l") logistic_loadshape2 <- lslog(loads2, target_lf = 0.6, k = 0.5, inf_pos = 0.4) summary(logistic_loadshape2) #--------------------------------------------------- loads3 <- ercot[ercot$Year == 2020, ]$ERCOT plot(loads3, type = "l") logistic_loadshape3 <- lslog(loads3, target_lf = 0.9) summary(logistic_loadshape3)
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