Illustrative test cases Discrete Dynamic
Contents
Illustrative test cases Discrete Dynamic#
Temperature forecasting#
This is one of the dynamic data sets provided with the NeurEco installation. The goal of this test case is to predict the temperature at time t, using weather variables from a weather station.
The input and output features of this test case are as follows:
Inputs 
Outputs 

p (mbar) at time t 
T (deg C) at time t 
Tpot(K) at time t 

Tdew(deg C) at time t 

rh(%) at time t 

VPmax(mbar) at time t 

VPact(mbar) at time t 

VPdef(mbar) at time t 

sh(g/kg) at time t 

H2OC(mmol/l) at time t 

rho (g/m^3) at time t 

wv (m/s) at time t 

max wv (m/s) at time t 

wd(deg) at time t 
The test case is provided with the following files:
Training data set containing one trajectory, \(526\) time steps long:
x_first_year.npy: first year of measurements of the input features
y_first_year.npy: first year of measurements of temperature (the output feature)
Testing data set containing one trajectory, \(526\) time steps long:
x_second_year.npy: second year of measurements of the input features
y_second_year.npy: second year of measurements of temperature (the output feature)
Nonlinear oscillator#
This is one of the dynamic data sets provided with the NeurEco installation. This test case describes a Duffing oscillator governed by the following equation:
where:
The choice of the equation parameters were made arbitrarily to give it a strong non linearity.
This test case is provided with the following files:
Training data set containing two trajectories:
train_exc_1.csv: the training inputs file  part 1, \(750\) time steps long
train_out_1.csv: the training targets file  part 1
train_exc_2.csv: the training inputs file  part 2, \(750\) time steps long
train_out_2.csv: the training targets file  part 2
Validation data set containing one trajectory:
valid_exc_1.csv: the validation inputs file, \(1501\) time steps long
valid_out_1.csv: the validation targets file
Testing data set containing one trajectory:
test_exc_1.csv: the testing inputs file, \(1501\) time steps long
test_out_1.csv: the testing targets file
Electric Motor Temperature#
This is one of the dynamic data sets provided with the NeurEco installation. The goal is to predict the temperature of the permanent magnet inside an electrical synchronous motor at time t, using its excitations. The motor is excited by handdesigned driving cycles denoting a reference motor speed and a reference torque. Currents in d/qcoordinates (columns “id” and iq”) and voltages in d/qcoordinates (columns “ud” and “uq”) are a result of a standard control strategy trying to follow the reference speed and torque. Columns “motor_speed” and “torque” are the resulting quantities achieved by that strategy, derived from set currents and voltages.
The data set and its detailed description can be found here: Kaggle: Electric Motor Temperature.
Note: This test case uses 1 time step in every 100 time steps found on the website (1% of the data)
Seven input features:
u_q: Voltage qcomponent measurement in dqcoordinates (in V). coolant: Coolant temperature (in °C). u_d: Voltage dcomponent measurement in dqcoordinates (in V). motor_speed: Motor speed (in rpm). i_d: Current dcomponent measurement in dqcoordinates. i_q: Current qcomponent measurement in dqcoordinates. ambient: Ambient temperature (in °C)
One output feature: pm: Permanent magnet temperature (in °C) measured with thermocouples and transmitted wirelessly via a thermography unit.
This test case is provided with the following files:
Training data set containing 20 trajectories:
train_exc_n.csv: the \(n^{th}\) training inputs file
train_out_n.csv: the \(n^{th}\) training targets file
Validation data set containing 20 trajectories:
valid_exc_n.csv: the \(n^{th}\) validation inputs file
valid_out_n.csv: the \(n^{th}\) validation targets file
Testing data set containing 21 trajectories:
test_exc_n.csv: the \(n^{th}\) testing inputs file
test_out_n.csv: the \(n^{th}\) testing targets file