HVAC_NN_Load calc.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

import tensorflow as tf
import keras
from keras import models
from keras import layers
from keras.utils import np_utils

print('Dota Loading ...')

# Load of buildings energy data from file
dataset = pd.read_csv('/Users/theodosismoumiadis/Documents/ENB2012_data.csv')
# A command that returns all data rows in random order
# dataset = dataset.sample(frac=1)

# Separation of training data into X and Y sets
X_train = dataset.ix[0:667,1:9].values.astype('float32')
Y1_train = dataset.loc[0:667,'Y1'].values.astype('float32')
Y2_train = dataset.loc[0:667,'Y2'].values.astype('float32')

# Separation of test data into X and Y sets
X_test = dataset.ix[668:767,1:9].values.astype('float32')
Y1_test = dataset.loc[668:767,'Y1'].values.astype('float32')
Y2_test = dataset.loc[668:767,'Y2'].values.astype('float32')

print('Data Loaded')

print(' ')
print('Data Normalization ... ')

# Training and test input data Normalization
mean = X_train.mean(axis=0)
X_train -= mean
std = X_train.std(axis=0)
X_train /= std

X_test -= mean
X_test /= std

print('Data Normalized')

print(' ')
print('Neural network training ... ')

# NN model definition

# Train of the DL network using tf.keras
model = tf.keras.models.Sequential()
# The Input Layer:
model.add(tf.keras.layers.Dense(64, activation=tf.nn.relu, input_shape=(8,)))
# The Hidden Layers
model.add(tf.keras.layers.Dense(64,activation=tf.nn.relu))

# The Output Layer
model.add(tf.keras.layers.Dense(1))

model.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])

model.fit(X_train, Y1_train, epochs=300, batch_size=10, verbose=0)

test_mse_score, test_mae_score = model.evaluate(X_test, Y1_test)