How to predict a certain time span into the future with recurrent neural networks in Keras

I have the following code for time series predictions with RNNs and I would like to know whether for the testing I predict one day in advance:

# -*- coding: utf-8 -*-
"""
Time Series Prediction with  RNN

"""
import pandas as pd
import numpy as np
from tensorflow import keras


#%%  Configure parameters

epochs = 5
batch_size = 50

steps_backwards = int(1* 4 * 24)
steps_forward = int(1* 4 * 24)

split_fraction_trainingData = 0.70
split_fraction_validatinData = 0.90


#%%  "Reading the data"

dataset = pd.read_csv('C:/User1/Desktop/TestValues.csv', sep=';', header=0, low_memory=False, infer_datetime_format=True, parse_dates={'datetime':[0]}, index_col=['datetime'])

df = dataset
data = df.values
indexWithYLabelsInData = 0
data_X = data[:, 0:2]
data_Y = data[:, indexWithYLabelsInData].reshape(-1, 1)

#%%   Prepare the input data for the RNN

series_reshaped_X =  np.array([data_X[i:i + (steps_backwards+steps_forward)].copy() for i in range(len(data) - (steps_backwards+steps_forward))])
series_reshaped_Y =  np.array([data_Y[i:i + (steps_backwards+steps_forward)].copy() for i in range(len(data) - (steps_backwards+steps_forward))])


timeslot_x_train_end = int(len(series_reshaped_X)* split_fraction_trainingData)
timeslot_x_valid_end = int(len(series_reshaped_X)* split_fraction_validatinData)

X_train = series_reshaped_X[:timeslot_x_train_end, :steps_backwards] 
X_valid = series_reshaped_X[timeslot_x_train_end:timeslot_x_valid_end, :steps_backwards] 
X_test = series_reshaped_X[timeslot_x_valid_end:, :steps_backwards] 


indexWithYLabelsInSeriesReshapedY = 0
lengthOfTheYData = len(data_Y)-steps_backwards -steps_forward
Y = np.empty((lengthOfTheYData, steps_backwards, steps_forward))  
for step_ahead in range(1, steps_forward + 1):     
   Y[..., step_ahead - 1] =   series_reshaped_Y[..., step_ahead:step_ahead + steps_backwards, indexWithYLabelsInSeriesReshapedY]
 
Y_train = Y[:timeslot_x_train_end] 
Y_valid = Y[timeslot_x_train_end:timeslot_x_valid_end] 
Y_test = Y[timeslot_x_valid_end:]


#%%  Build the model and train it

model = keras.models.Sequential([
    keras.layers.SimpleRNN(90, return_sequences=True, input_shape=[None, 2]),
    keras.layers.SimpleRNN(60, return_sequences=True),
    keras.layers.TimeDistributed(keras.layers.Dense(steps_forward))
    #keras.layers.Dense(steps_forward)
])

model.compile(loss="mean_squared_error", optimizer="adam", metrics=['mean_absolute_percentage_error'])
history = model.fit(X_train, Y_train, epochs=epochs, batch_size=batch_size,
                    validation_data=(X_valid, Y_valid))


#%%    #Predict the test data
Y_pred = model.predict(X_test)

prediction_lastValues_list=[]

for i in range (0, len(Y_pred)):
  prediction_lastValues_list.append((Y_pred[i][0][steps_forward-1]))

#%% Create thw dataframe for the whole data

wholeDataFrameWithPrediciton = pd.DataFrame((X_test[:,0]))
wholeDataFrameWithPrediciton.rename(columns = {indexWithYLabelsInData:'actual'}, inplace = True)
wholeDataFrameWithPrediciton.rename(columns = {1:'Feature 1'}, inplace = True)
wholeDataFrameWithPrediciton['predictions'] = prediction_lastValues_list
wholeDataFrameWithPrediciton['difference'] = (wholeDataFrameWithPrediciton['predictions'] - wholeDataFrameWithPrediciton['actual']).abs()
wholeDataFrameWithPrediciton['difference_percentage'] = ((wholeDataFrameWithPrediciton['difference'])/(wholeDataFrameWithPrediciton['actual']))*100

I define eps_forward = int(1* 4 * 24) which is basically one full day (in 15 minutes resolution which makes 1 * 4 *24 = 96 time stamps). I predict the test data by using Y_pred = model.predict(X_test) and I create a list with the predicted values by using for i in range (0, len(Y_pred)): prediction_lastValues_list.append((Y_pred[i][0][steps_forward-1]))

As for me the input and output data of RNNs is quite confusing I am not sure whether for the test dataset I predict one day in advance meaning 96 time steps into the future. Actually what I want is to read historic data and then predict the next 96 time steps based on the historic 96 time steps. Can anyone of you tell me whether I am doing this by using this code or not?

Here I have a link to some test data that I just created randomly. Do not care about the actual values but just on the structure of the prediction: Download Test Data

Am I forecasting 96 steps in advance with the given code?

Reminder: Can anyone tell me something about my question? Or do you need further information? If so, please tell me. I'll highly appreciate your comments and will be quite thankful for your help. I will also award a bounty for a useful answer.

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