lscore: Load Shape Score
In loadshaper: Producing Load Shape with Target Peak and Load Factor

lscore

R Documentation

Load Shape Score

Description

lscore provides a diagnostic score for evaluating the derived load shape in retaining time series properties.

Usage

lscore(ls, type = "acf", output = 2, lag = NULL)

Arguments

`ls`	An object of class `lslin` or `lslog`, created using function `lslin` or `lslog`
`type`	Type of correlation to be evaluate, either `"acf"` or `"pacf"`
`output`	Type of output to be used, either 1 or 2; uses `ls$y` if 1 and `ls$y2` if 2
`lag`	Maximum lag at which to calculate the acf or pacf. Same as `lag.max` in `acf`. If `Null`, then default is used.

Details

The diagnostic measure is calculated as a weighted mean absolute percent error (MAPE) of auto correlation or partial auto correlation values of the derived series with respect to the original. The values are calculated for given lag. Lag = 0 is omitted from calculation for auto correlation as it would be always 1. If o_i and d_i are the correlation values of original and derived load shape at lag i, then weighted MAPE is calculated as

wmape = ∑ _{i=1}^{lag} { w_i * |(o_i - d_i) / o_i|}

where w_i = \frac{|o_i|}{∑ _{i=1}^{lag}|o_i|}

Since wmape is a measure of error, lower value indicates better preservation of time series property.

Value

A list of the followings:

wmape: Weighted MAPE.
lag: Lags at which ACF or PACF values were evaluated and used in calculating wmape.
type: Type of Correlation (ACF or PACF)
cor_x: ACF/PACF values of the original load.
cor_y: ACF/PACF values of the derived load.
weight: Weights at different lags used to calculate wmape.

Examples

loads <- ercot[ercot$Year == 2019, ]$COAST
linear_loadshape <- lslin(loads, target_lf = 0.4)
# --------------
scores_1 <- lscore(linear_loadshape, type = "acf", lag = 20)
print(scores_1)
# --------------
scores_2 <- lscore(linear_loadshape, type = "pacf")
print(scores_2)

loadshaper documentation built on May 17, 2022, 5:07 p.m.