config.toml

[data]
building_train = [1, 2, 5]
building_valid = [1]
building_test = [1]
appliances = ["fridge", "dish_washer", "washing_machine"]
period = "1min"  # Time period of the sequence
power_scale = 2000.0  # Power value by which we divide the load, normalizing it
max_power = 10000.0  # Maximum load power, in watts

[data.dates]  # Date range for each building
1 = ["2013-04-12", "2014-12-15"]
2 = ["2013-05-22", "2013-10-03 6:16"]
5 =["2014-04-29", "2014-09-01"]

[data.threshold]
method = "mp"  # Thresholding method to use:
# 'vs': Variance-Sensitive. Applies k-means and uses the std of each
# cluster to move the threshold accordingly.
# 'mp': Middle-Point. Applies k-means and sets the threshold in the
# middle of the two cluster centroids.
# 'at': Activation-Time. Takes the thresholds and activation times defined by J. Kelly.
#'custom': Use custom thresholding, by providing the (!) parameters.

list = ["mp", "vs", "at"]  # (!) Only used when threshold_method is 'custom'.
# Thresholds per appliance, in watts.
# If not provided, thresholds are computed using k-means.

std = true  # (!) Only used when threshold_method is 'custom'.
# If the threshold is computed by k-means, use the standard deviation of each cluster
# to move the threshold.

min_off = "None"  # (!) Only used when threshold_method is 'custom'.
# Number of records that an appliance must be below the threshold to be considered
# turned OFF. If not provided, thresholds are computed using k-means.

min_on = "None"  # (!) Only used when threshold_method is 'custom'.
# Number of records that an appliance must be above the threshold to be considered
# turned ON. If not provided, thresholds are computed using k-means.]

[train]
name = "ConvModel"
train_size = 0.8  # Train size, relative to 1
valid_size = 0.1  # Validation size, relative to 1
batch_size = 32
epochs = 300
patience = 300
num_models = 5 # Number of models to train. Their scores are then averaged
return_means = true  # Return as last argument the computed means of each status

[train.model]
input_len = 510  # Input sequence of the model
output_len = 480  # Output sequence of the model
border = 16  # Border added to input sequences, so that input = output + 2 * border
init_features = 32
learning_rate = 1.0e-4
dropout = 0.1
classification_w = 0.0

[plot]
f1_lim = [0.4, 1.0]
nde_lim = [0.3, 0.8]
figsize = [4, 3]
num_plots = 10

[plot.appliances]
dish_washer= "Dishwasher"
fridge= "Fridge"
washing_machine= "Washing machine"

[plot.thresh_color]
at = 'b'
mp = 'g'
vs = 'r'