DAILY_DEMAND_TR

Training dataset with values of temperature and working day to predict electrical demand

data

Analysis functions to quantify inputs importance in neural network models.
Functions are available for calculating and plotting the inputs importance and obtaining
the activation function of each neuron layer and its derivatives. The importance of a given
input is defined as the distribution of the derivatives of the output with respect to that
input in each training data point <doi:10.18637/jss.v102.i07>.

Jaime Pizarroso Gonzalo

NeuralSens

Sensitivity Analysis of Neural Networks

José Portela González

Antonio Muñoz San Roque

DAILY_DEMAND_TR function

A data frame with 1980 rows and 4 variables:<dl>
 <dt>DATE</dt>
<dd>date of the measure</dd> <dt>DEM</dt>
<dd>electrical demand</dd> <dt>WD</dt>
<dd>Working Day: index which express how much work is made that day</dd> <dt>TEMP</dt>
<dd>weather temperature</dd>
</dl>

Format

Author

Data frame with 4 variables — DAILY_DEMAND_TR

A data frame with 1980 rows and 4 variables:<dl>
 <dt>DATE</dt>
<dd>date of the measure</dd>

 <dt>DEM</dt>
<dd>electrical demand</dd>

 <dt>WD</dt>
<dd>Working Day: index which express how much work is made that day</dd>

 <dt>TEMP</dt>
<dd>weather temperature</dd>


</dl>

DAILY_DEMAND_TR: Data frame with 4 variables

Description

Arguments

Format

Author

References