What are out1, out2 etc..

[abcnishant007]

Dear Authors,

Congrats on the amazing work! I have two questions about the data format. Any inputs from you are appreciated. Thanks in advance!

1. About the variables inside the .mat file:
   

   Reinforcement-Learning-for-Real-time-Pricing-and-Scheduling-Control-in-EV-Charging-Stations/code/HSA.py

   Line 33 in a33ee47

   m = loadmat("../data/testingdata.mat")

What are the variables out1, out2 and out3

Reinforcement-Learning-for-Real-time-Pricing-and-Scheduling-Control-in-EV-Charging-Stations/code/HSA.py

Lines 36 to 42 in a33ee47

	for i in range(5):
	out1=np.concatenate((out1,m['out1']),axis=1)
	out2=np.concatenate((m['out2'],m['out2']),axis=1)
	for i in range(5):
	out2=np.concatenate((out2,m['out2']),axis=1)
	out3=np.concatenate((m['out3'],m['out3']),axis=1)
	for i in range(5):

2. The csv file EV_Arrivals.csv does not appear to be used anywhere. Is the same file somehow already loaded in the .MAT file?

[wsyCUHK]

Hi,
The EV_Arrivals is the orignal data, which is preprocessed into the out1, out2, and out3 vectors in the .mat file. Accordingly, the out vectors are the arrivals of different type EVs.

[mfl22]

Could you add some more explanation about data loading:

Reinforcement-Learning-for-Real-time-Pricing-and-Scheduling-Control-in-EV-Charging-Stations/code/HSA.py

Lines 31 to 55 in cbd4c8b

	df=pd.read_excel('../data/Price_CAISO.xlsx')
	from scipy.io import loadmat
	m = loadmat("../data/testingdata.mat")
	import numpy as np
	out1=np.concatenate((m['out1'],m['out1']),axis=1)
	for i in range(5):
	out1=np.concatenate((out1,m['out1']),axis=1)
	out2=np.concatenate((m['out2'],m['out2']),axis=1)
	for i in range(5):
	out2=np.concatenate((out2,m['out2']),axis=1)
	out3=np.concatenate((m['out3'],m['out3']),axis=1)
	for i in range(5):
	out3=np.concatenate((out3,m['out3']),axis=1)
	out1=out1.squeeze().astype('int')
	out2=out2.squeeze().astype('int')
	out3=out3.squeeze().astype('int')
	mixed_price=df['Unnamed: 4'].values
	ISO_eprice=np.zeros((4000,1))
	for i in range(1,1001):
	if mixed_price[9*i-6]>1 and mixed_price[9*i-6]<100:
	ISO_eprice[15*i-14:15*i]=mixed_price[9*i-6]
	elif mixed_price[9*i-6]<100:
	ISO_eprice[15*i-14:15*i]=1
	else:
	ISO_eprice[15*i-14:15*i]=100

Do the timestamps in out1, out2, out3, and ISO_eprice correspond? Also, it is not entirely clear why prices data is loaded in this way. The dimensions of these arrays don't correspond also...

Related, I added an issue about data points used in training: #9 (comment)