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jerin commited on
Commit
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1 Parent(s): 6b33198

add training file and readme.txt

Browse files
Files changed (5) hide show
  1. RTU_LSTM_training_notebook/lstm_training.ipynb +340 -0
  2. mqtt_client.py +1 -1
  3. mqttpublisher.ipynb +0 -2750
  4. mqttpublisher.py +33 -0
  5. readme.txt +51 -0
RTU_LSTM_training_notebook/lstm_training.ipynb ADDED
@@ -0,0 +1,340 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "cells": [
3
+ {
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+ "cell_type": "code",
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+ "execution_count": null,
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+ "metadata": {},
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+ "outputs": [],
8
+ "source": [
9
+ "\"\"\"\n",
10
+ "Import necessary libraries and modules.\n",
11
+ "\"\"\"\n",
12
+ "import pandas as pd \n",
13
+ "from datetime import date\n",
14
+ "import matplotlib.pyplot as plt\n",
15
+ "import numpy as np\n",
16
+ "import pandas as pd\n",
17
+ "from keras.models import Sequential\n",
18
+ "from keras.layers import LSTM, Dense\n",
19
+ "from sklearn.model_selection import train_test_split\n",
20
+ "from sklearn.preprocessing import MinMaxScaler,StandardScaler\n",
21
+ "from keras.callbacks import ModelCheckpoint\n",
22
+ "from sklearn.cluster import KMeans\n",
23
+ "from sklearn.decomposition import PCA\n",
24
+ "from tensorflow.keras.models import load_model\n"
25
+ ]
26
+ },
27
+ {
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+ "cell_type": "code",
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+ "execution_count": null,
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+ "metadata": {},
31
+ "outputs": [],
32
+ "source": [
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+ "\"\"\"\n",
34
+ "Load the dataset\n",
35
+ "\"\"\"\n",
36
+ "\n",
37
+ "merged = pd.read_csv('long_merge.csv')\n",
38
+ "\n",
39
+ "zone = \"47\"\n",
40
+ "\n",
41
+ "if zone in [\"36\", \"37\", \"38\", \"39\", \"40\", \"41\", \"42\", \"64\", \"65\", \"66\", \"67\", \"68\", \"69\", \"70\"]:\n",
42
+ " rtu = \"rtu_001\"\n",
43
+ " wing = \"hvac_N\"\n",
44
+ "elif zone in [\"18\", \"25\", \"26\", \"45\", \"48\", \"55\", \"56\", \"61\"]:\n",
45
+ " rtu = \"rtu_003\"\n",
46
+ " wing = \"hvac_S\"\n",
47
+ "elif zone in [\"16\", \"17\", \"21\", \"22\", \"23\", \"24\", \"46\", \"47\", \"51\", \"52\", \"53\", \"54\"]:\n",
48
+ " rtu = \"rtu_004\"\n",
49
+ " wing = \"hvac_S\"\n",
50
+ "else:\n",
51
+ " rtu = \"rtu_002\"\n",
52
+ " wing = \"hvac_N\"\n",
53
+ "sorted = merged[[\"date\"]+[col for col in merged.columns if zone in col or rtu in col or wing in col]+[\"hp_hws_temp\", \"aru_001_cwr_temp\" , \"aru_001_cws_fr_gpm\" ,\"aru_001_cws_temp\",\"aru_001_hwr_temp\" ,\"aru_001_hws_fr_gpm\" ,\"aru_001_hws_temp\"]]\n",
54
+ "sorted"
55
+ ]
56
+ },
57
+ {
58
+ "cell_type": "code",
59
+ "execution_count": null,
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+ "metadata": {},
61
+ "outputs": [],
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+ "source": [
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+ "\"\"\"\n",
64
+ "Load the dataset\n",
65
+ "\"\"\"\n",
66
+ "\n",
67
+ "rtu = [\"rtu_003\",\"rtu_004\",\"rtu_001\",\"rtu_002\"]\n",
68
+ "env = [\"air_temp_set_1\",\"air_temp_set_2\",\"dew_point_temperature_set_1d\",\"relative_humidity_set_1\",\"solar_radiation_set_1\"]\n",
69
+ "energy_data = merged[[\"date\",\"hp_hws_temp\"]+[col for col in merged.columns if \n",
70
+ " any(sub in col for sub in rtu)]+env]\n",
71
+ "df_filtered = energy_data[[col for col in energy_data.columns if 'Unnamed' not in col]]\n",
72
+ "df_filtered = df_filtered[[col for col in df_filtered.columns if 'co2' not in col]]\n",
73
+ "df_filtered = df_filtered[[col for col in df_filtered.columns if 'templogger' not in col]]\n",
74
+ "df_filtered['date'] = pd.to_datetime(df_filtered['date'], format = \"%Y-%m-%d %H:%M:%S\")\n",
75
+ "df_filtered = df_filtered[ (df_filtered.date.dt.date >date(2018, 5, 1)) & (df_filtered.date.dt.date< date(2020, 5, 1))] #(2018, 5, 1)\n",
76
+ "if df_filtered.isna().any().any():\n",
77
+ " print(\"There are NA values\",df_filtered.isna().sum().tolist())"
78
+ ]
79
+ },
80
+ {
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+ "cell_type": "code",
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+ "execution_count": null,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "\"\"\"\n",
87
+ "Extract the relevant parameters\n",
88
+ "\"\"\"\n",
89
+ "\n",
90
+ "\n",
91
+ "df_filtered = df_filtered.loc[:,['date','hp_hws_temp',\n",
92
+ " 'rtu_003_sa_temp',\n",
93
+ " 'rtu_003_oadmpr_pct',\n",
94
+ " 'rtu_003_ra_temp',\n",
95
+ " 'rtu_003_oa_temp',\n",
96
+ " 'rtu_003_ma_temp',\n",
97
+ " 'rtu_003_sf_vfd_spd_fbk_tn',\n",
98
+ " 'rtu_003_rf_vfd_spd_fbk_tn',\n",
99
+ " 'rtu_004_sa_temp',\n",
100
+ " 'rtu_004_oadmpr_pct',\n",
101
+ " 'rtu_004_ra_temp',\n",
102
+ " 'rtu_004_oa_temp',\n",
103
+ " 'rtu_004_ma_temp',\n",
104
+ " 'rtu_004_sf_vfd_spd_fbk_tn',\n",
105
+ " 'rtu_004_rf_vfd_spd_fbk_tn',\n",
106
+ " 'rtu_003_sat_sp_tn',\n",
107
+ " 'rtu_004_sat_sp_tn',\n",
108
+ " \n",
109
+ "# 'rtu_001_sa_temp',\n",
110
+ "# 'rtu_001_oadmpr_pct',\n",
111
+ "# 'rtu_001_ra_temp',\n",
112
+ "# 'rtu_001_oa_temp',\n",
113
+ "# 'rtu_001_ma_temp',\n",
114
+ "# 'rtu_001_sf_vfd_spd_fbk_tn',\n",
115
+ "# 'rtu_001_rf_vfd_spd_fbk_tn',\n",
116
+ "# 'rtu_002_sa_temp',\n",
117
+ "# 'rtu_002_oadmpr_pct',\n",
118
+ "# 'rtu_002_ra_temp',\n",
119
+ "# 'rtu_002_oa_temp',\n",
120
+ "# 'rtu_002_ma_temp',\n",
121
+ "# 'rtu_002_sf_vfd_spd_fbk_tn',\n",
122
+ "# 'rtu_002_rf_vfd_spd_fbk_tn',\n",
123
+ "# 'rtu_001_sat_sp_tn',\n",
124
+ "# 'rtu_002_sat_sp_tn',\n",
125
+ "\n",
126
+ " 'air_temp_set_1',\n",
127
+ " 'air_temp_set_2',\n",
128
+ " 'dew_point_temperature_set_1d',\n",
129
+ " 'relative_humidity_set_1',\n",
130
+ " 'solar_radiation_set_1']]"
131
+ ]
132
+ },
133
+ {
134
+ "cell_type": "code",
135
+ "execution_count": null,
136
+ "metadata": {},
137
+ "outputs": [],
138
+ "source": [
139
+ "\"\"\"\n",
140
+ "splitting into training and testing sets.\n",
141
+ "\"\"\"\n",
142
+ "\n",
143
+ "df_filtered = df_filtered.dropna()\n",
144
+ "\n",
145
+ "testdataset_df = df_filtered[(df_filtered.date.dt.date >date(2019, 7, 21))]\n",
146
+ "\n",
147
+ "traindataset_df = df_filtered[(df_filtered.date.dt.date <date(2019, 7, 21))]\n",
148
+ "\n",
149
+ "testdataset = testdataset_df.drop(columns=[\"date\"]).rolling(window=30,min_periods=1).mean().values\n",
150
+ "\n",
151
+ "traindataset = traindataset_df.drop(columns=[\"date\"]).rolling(window=30,min_periods=1).mean().values\n",
152
+ "\n",
153
+ "columns_with_na_train = traindataset_df.columns[traindataset_df.isna().any()].tolist()\n",
154
+ "columns_with_na_test = testdataset_df.columns[testdataset_df.isna().any()].tolist()\n",
155
+ "print(columns_with_na_train)\n",
156
+ "print(columns_with_na_test)"
157
+ ]
158
+ },
159
+ {
160
+ "cell_type": "code",
161
+ "execution_count": null,
162
+ "metadata": {},
163
+ "outputs": [],
164
+ "source": [
165
+ "\n",
166
+ "\"\"\"\n",
167
+ "Preprocess the data by scaling\n",
168
+ "\"\"\"\n",
169
+ "\n",
170
+ "traindataset = traindataset.astype('float32')\n",
171
+ "testdataset = testdataset.astype('float32')\n",
172
+ "\n",
173
+ "scaler = StandardScaler()\n",
174
+ "traindataset = scaler.fit_transform(traindataset)\n",
175
+ "testdataset = scaler.transform(testdataset)"
176
+ ]
177
+ },
178
+ {
179
+ "cell_type": "code",
180
+ "execution_count": null,
181
+ "metadata": {},
182
+ "outputs": [],
183
+ "source": [
184
+ "\"\"\"\n",
185
+ "Training the model\n",
186
+ "\"\"\"\n",
187
+ "\n",
188
+ "\n",
189
+ "train,test = traindataset,testdataset\n",
190
+ "\n",
191
+ "def create_dataset(dataset,time_step):\n",
192
+ " x = [[] for _ in range(22)] \n",
193
+ " Y = []\n",
194
+ " for i in range(len(dataset) - time_step - 1):\n",
195
+ " for j in range(22):\n",
196
+ " x[j].append(dataset[i:(i + time_step), j])\n",
197
+ " Y.append([dataset[i + time_step, 0],dataset[i + time_step, 1],dataset[i + time_step, 2],dataset[i + time_step, 3],\n",
198
+ " dataset[i + time_step, 4],dataset[i + time_step, 5],\n",
199
+ " dataset[i + time_step, 6],dataset[i + time_step, 7],\n",
200
+ " dataset[i + time_step, 8],dataset[i + time_step, 9],dataset[i + time_step, 10],\n",
201
+ " dataset[i + time_step, 11],dataset[i + time_step, 12],\n",
202
+ " dataset[i + time_step, 13],dataset[i + time_step, 14]])\n",
203
+ " x= [np.array(feature_list) for feature_list in x]\n",
204
+ " Y = np.reshape(Y,(len(Y),15))\n",
205
+ " return np.stack(x,axis=2),Y\n",
206
+ "\n",
207
+ "time_step = 30\n",
208
+ "X_train, y_train = create_dataset(train, time_step)\n",
209
+ "X_test, y_test = create_dataset(test, time_step)\n",
210
+ "\n",
211
+ "\n",
212
+ "model = Sequential()\n",
213
+ "model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], X_train.shape[2])))\n",
214
+ "model.add(LSTM(units=50, return_sequences=True))\n",
215
+ "model.add(LSTM(units=30))\n",
216
+ "model.add(Dense(units=15))\n",
217
+ "\n",
218
+ "model.compile(optimizer='adam', loss='mean_squared_error')\n",
219
+ "\n",
220
+ "checkpoint_path = \"lstm_2rtu_smooth_04.keras\" # \"lstm_2rtu_smooth_03.keras\"--> 3,4 rtu\n",
221
+ "checkpoint_callback = ModelCheckpoint(filepath=checkpoint_path, monitor='val_loss', verbose=1, save_best_only=True, mode='min')\n",
222
+ "model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=5, batch_size=64, verbose=1, callbacks=[checkpoint_callback])"
223
+ ]
224
+ },
225
+ {
226
+ "cell_type": "code",
227
+ "execution_count": null,
228
+ "metadata": {},
229
+ "outputs": [],
230
+ "source": [
231
+ "\"\"\"\n",
232
+ "load the model for prediction\n",
233
+ "\"\"\"\n",
234
+ "\n",
235
+ "checkpoint_path = \"lstm_2rtu_smooth_03.keras\"\n",
236
+ "model = load_model(checkpoint_path)\n",
237
+ "test_predict1 = model.predict(X_test)\n",
238
+ "train_predict1 = model.predict(X_train)"
239
+ ]
240
+ },
241
+ {
242
+ "cell_type": "code",
243
+ "execution_count": null,
244
+ "metadata": {},
245
+ "outputs": [],
246
+ "source": [
247
+ "%matplotlib qt\n",
248
+ "var = 6\n",
249
+ "plt.plot(testdataset_df['date'][31:], y_test[:,var], label='Original Testing Data')\n",
250
+ "plt.plot(testdataset_df['date'][31:] ,test_predict1[:,var], label='Predicted Data')\n",
251
+ "\n",
252
+ "# anomalies = np.where(abs(test_predict1[:,var] - y_test[:,var]) > 0.38)\n",
253
+ "# plt.scatter(anomalies,test_predict1[anomalies,var], color='black',marker =\"o\",s=100 )\n",
254
+ "\n",
255
+ "\n",
256
+ "plt.title('Testing Data - Predicted vs Actual')\n",
257
+ "plt.xlabel('Time')\n",
258
+ "plt.ylabel('Value')\n",
259
+ "plt.legend()\n",
260
+ "plt.show()"
261
+ ]
262
+ },
263
+ {
264
+ "cell_type": "code",
265
+ "execution_count": null,
266
+ "metadata": {},
267
+ "outputs": [],
268
+ "source": [
269
+ "\"\"\"\n",
270
+ " Perform clustering and dimensionality reduction using KMeans and PCA\n",
271
+ "\"\"\"\n",
272
+ "\n",
273
+ "np.random.seed(0)\n",
274
+ "X1 = train_predict1[:,1:8] - y_train[:,1:8]\n",
275
+ "X2 = train_predict1[:,8:15] - y_train[:,8:15]\n",
276
+ "\n",
277
+ "\n",
278
+ "k = 1\n",
279
+ "\n",
280
+ "pca1 = PCA(n_components=2)\n",
281
+ "X1 = pca1.fit_transform(X1)\n",
282
+ "pca2 = PCA(n_components=2)\n",
283
+ "X2 = pca2.fit_transform(X2)\n",
284
+ "\n",
285
+ "kmeans1 = KMeans(n_clusters=k, random_state=10) \n",
286
+ "kmeans1.fit(X1)\n",
287
+ "kmeans2 = KMeans(n_clusters=k, random_state=10) \n",
288
+ "kmeans2.fit(X2)\n",
289
+ "\n",
290
+ "\n",
291
+ "labels = kmeans1.labels_\n",
292
+ "plt.scatter(X1[:, 0], X1[:, 1],c=labels, cmap='rainbow')\n",
293
+ "plt.scatter(kmeans1.cluster_centers_[:, 0], kmeans1.cluster_centers_[:, 1], marker='x', c='red', s=200, linewidths=2)\n",
294
+ "plt.title('KMeans Clustering')\n",
295
+ "plt.xlabel('Feature 1')\n",
296
+ "plt.ylabel('Feature 2')\n",
297
+ "plt.show()"
298
+ ]
299
+ },
300
+ {
301
+ "cell_type": "code",
302
+ "execution_count": null,
303
+ "metadata": {},
304
+ "outputs": [],
305
+ "source": [
306
+ "\"\"\"\n",
307
+ " Plot the faults after windowing\n",
308
+ "\"\"\"\n",
309
+ "\n",
310
+ "%matplotlib qt\n",
311
+ "\n",
312
+ "distance1 = np.linalg.norm((pca1.transform(test_predict1[:,1:8]-y_test[:,1:8]))-kmeans1.cluster_centers_[0], ord=2, axis = 1)\n",
313
+ "distance2 = np.linalg.norm((pca2.transform(test_predict1[:,8:15]-y_test[:,8:15]))-kmeans2.cluster_centers_[0], ord=2, axis = 1)\n",
314
+ "\n",
315
+ "plt.plot(testdataset_df['date'][31:] ,y_test[:,7],label='Return air fan speed')\n",
316
+ "plt.plot(testdataset_df['date'][31:] ,abs(distance1)>1,linewidth=2.5,label='Faults')\n",
317
+ "plt.plot(testdataset_df['date'][31:] ,pd.Series((distance1)>3.5).rolling(window=60,min_periods=1).mean()==1,linewidth=2.5,label='Faults')\n",
318
+ "\n",
319
+ "plt.title('RTU-2 Faults')\n",
320
+ "plt.xlabel('Time')\n",
321
+ "plt.ylabel('Value')\n",
322
+ "plt.legend()\n",
323
+ "plt.show()\n"
324
+ ]
325
+ }
326
+ ],
327
+ "metadata": {
328
+ "kernelspec": {
329
+ "display_name": "smartbuilding",
330
+ "language": "python",
331
+ "name": "python3"
332
+ },
333
+ "language_info": {
334
+ "name": "python",
335
+ "version": "3.11.8"
336
+ }
337
+ },
338
+ "nbformat": 4,
339
+ "nbformat_minor": 2
340
+ }
mqtt_client.py CHANGED
@@ -20,7 +20,7 @@ def on_message(client, userdata, msg):
20
  data_list.append(data)
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22
 
23
- client = mqtt.Client()
24
  client.on_connect = on_connect
25
  client.on_message = on_message
26
 
 
20
  data_list.append(data)
21
 
22
 
23
+ client = mqtt.Client(mqtt.CallbackAPIVersion.VERSION1)
24
  client.on_connect = on_connect
25
  client.on_message = on_message
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mqttpublisher.ipynb DELETED
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- {
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- "cells": [
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- {
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- "cell_type": "code",
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- "execution_count": 5,
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "import paho.mqtt.client as mqtt\n",
10
- "import time\n",
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- "import random\n",
12
- "import pandas as pd\n",
13
- "import json\n",
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- "\n",
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- "clientId = \"smartbuilding\"\n",
16
- "broker_address = \"localhost\"\n",
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- "broker_port = 1883\n",
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- "\n",
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- "df = pd.read_csv(\"data/demo_data.csv\")[30000:]\n"
20
- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 6,
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- "metadata": {},
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- "outputs": [
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- {
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- "data": {
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- "text/plain": [
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- "0 2020-03-31 00:00:00\n",
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- "1 2020-03-31 00:00:00\n",
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- "2 2020-03-31 00:01:00\n",
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- "3 2020-03-31 00:01:00\n",
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- "4 2020-03-31 00:02:00\n",
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- " ... \n",
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- "845141 2020-12-30 23:58:00\n",
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- "845142 2020-12-30 23:59:00\n",
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- "845143 2020-12-30 23:59:00\n",
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- "845144 2020-12-31 00:00:00\n",
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- "845145 2020-12-31 00:00:00\n",
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- "Name: date, Length: 845146, dtype: object"
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- ]
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- },
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- "execution_count": 6,
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- "metadata": {},
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- "output_type": "execute_result"
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- }
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- ],
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- "source": [
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- "df['date']"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 7,
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- "metadata": {},
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- "outputs": [
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- {
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- "name": "stderr",
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- "output_type": "stream",
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- "text": [
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- "C:\\Users\\arbal\\AppData\\Local\\Temp\\ipykernel_13892\\342713793.py:1: DeprecationWarning: Callback API version 1 is deprecated, update to latest version\n",
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- " client = mqtt.Client(mqtt.CallbackAPIVersion.VERSION1, clientId)\n"
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- ]
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- },
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- {
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- "name": "stdout",
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- "output_type": "stream",
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- "text": [
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- "published!\n",
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- "published!\n",
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- "published!\n",
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- "published!\n",
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- "published!\n",
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- "published!\n",
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- "published!\n",
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- "published!\n",
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- "published!\n",
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2636
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2640
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2641
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2642
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2643
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2644
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2645
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2646
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2647
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2648
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2649
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2650
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2651
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2652
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2653
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2654
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2655
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2656
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2657
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2658
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2659
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2660
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2661
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2662
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2663
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2664
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2665
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2666
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2667
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2668
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2669
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2670
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2671
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2672
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2673
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2674
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2675
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2676
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2677
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2678
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2679
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2680
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2681
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2682
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2683
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2684
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2685
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2686
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2687
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2688
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2689
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2690
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2691
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2692
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2693
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2694
- ]
2695
- }
2696
- ],
2697
- "source": [
2698
- "\n",
2699
- "client = mqtt.Client(mqtt.CallbackAPIVersion.VERSION1, clientId)\n",
2700
- "client.connect(broker_address, broker_port)\n",
2701
- "topic = \"sensor_data\"\n",
2702
- "\n",
2703
- "def publish_sensor_data(): \n",
2704
- " for i in range(len(df)):\n",
2705
- "\n",
2706
- " data = {}\n",
2707
- " for col in df.columns:\n",
2708
- " data[col] = df[col][i]\n",
2709
- "\n",
2710
- " client.publish(topic, payload=json.dumps(data))\n",
2711
- " print(\"published!\")\n",
2712
- " time.sleep(0.2)\n",
2713
- "\n",
2714
- "\n",
2715
- "while True:\n",
2716
- " publish_sensor_data()\n",
2717
- " # time.sleep(0.1)\n",
2718
- "client.disconnect()"
2719
- ]
2720
- },
2721
- {
2722
- "cell_type": "code",
2723
- "execution_count": null,
2724
- "metadata": {},
2725
- "outputs": [],
2726
- "source": []
2727
- }
2728
- ],
2729
- "metadata": {
2730
- "kernelspec": {
2731
- "display_name": "smartbuilding",
2732
- "language": "python",
2733
- "name": "python3"
2734
- },
2735
- "language_info": {
2736
- "codemirror_mode": {
2737
- "name": "ipython",
2738
- "version": 3
2739
- },
2740
- "file_extension": ".py",
2741
- "mimetype": "text/x-python",
2742
- "name": "python",
2743
- "nbconvert_exporter": "python",
2744
- "pygments_lexer": "ipython3",
2745
- "version": "3.11.8"
2746
- }
2747
- },
2748
- "nbformat": 4,
2749
- "nbformat_minor": 2
2750
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
mqttpublisher.py ADDED
@@ -0,0 +1,33 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import paho.mqtt.client as mqtt
2
+ import time
3
+ import random
4
+ import pandas as pd
5
+ import json
6
+
7
+ clientId = "smartbuilding"
8
+ broker_address = "localhost"
9
+ broker_port = 1883
10
+
11
+ df = pd.read_csv("data/demo_data2.csv")
12
+
13
+
14
+ client = mqtt.Client(mqtt.CallbackAPIVersion.VERSION1, clientId)
15
+ client.connect(broker_address, broker_port)
16
+ topic = "sensor_data"
17
+
18
+ def publish_sensor_data():
19
+ for i in range(len(df)):
20
+
21
+ data = {}
22
+ for col in df.columns:
23
+ data[col] = df[col][i]
24
+
25
+ client.publish(topic, payload=json.dumps(data))
26
+ print("published!")
27
+ time.sleep(0.2)
28
+
29
+
30
+ while True:
31
+ publish_sensor_data()
32
+ # time.sleep(0.1)
33
+ client.disconnect()
readme.txt ADDED
@@ -0,0 +1,51 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---------------------------------------------------------------------------------
2
+ HOW TO RUN THE ENTIRE PIPELINE? -
3
+ ------------------------------- -
4
+ Datastreaming --> Datapipeline --> Dashboard -
5
+ _______________________________________________________________________________ -
6
+ 1. Activate the samrtbuilding environment -
7
+ 2. RUN the below files from smart-buildings directory -----> cd smart-buildings -
8
+ 3. RUN the mqttpublisher.py file -----> python mqttpublisher.py -
9
+ 4. RUN the dashboard.py file -----> streamlit run dashboard.py -
10
+ ---------------------------------------------------------------------------------
11
+
12
+
13
+ RTUPipeline:
14
+
15
+ This file handles the data preparation part. It first extracts the relevant parameters from the incoming data stream and creates the 30-minute data buffer for the prediction. Then apply the scaler on that buffer.
16
+
17
+ input:
18
+ scaler_1: Standard scaler RTU 1,2
19
+ scaler_2: Standard scaler RTU 2,3
20
+ Incoming datastream as a dataframe
21
+ output:
22
+ df_new1: prev 30 samples of input parameters - reshaped for RTU-1,2 prediction model
23
+ df_trans1: prev 31 samples of rtu parameters - residual is calculated w.r.t 31st sample and predicted value
24
+ df_new2: prev 30 samples of input parameters - reshaped for RTU-3,4 prediction model
25
+ df_trans2: prev 31 samples of rtu parameters - residual is calculated w.r.t 31st sample and predicted value
26
+
27
+
28
+
29
+
30
+ RTUAnomalizer1/2:
31
+
32
+ There are two files RTUAnomalizer1 and RTUAnomalizer2, both do the same function. One for RTU 1,2 and the other for RTU 3,4. This file handles the prediction and clustering part of the RTU anomalies/faults
33
+
34
+ input:
35
+ model: The prediction model.
36
+ kmeans_models: List of clustering models.
37
+ pca_models: List of PCA models.
38
+ df_new: prev 30 samples of input parameters - reshaped for prediction model - Received from RTUPipeline
39
+ df_trans: prev 31 samples of input parameters - residual is calculated w.r.t 31st sample - Received from RTUPipeline
40
+
41
+ output:
42
+ actual_list: List of actual values.
43
+ pred_list: List of predicted values.
44
+ resid_list: List of residual values.
45
+ resid_pca_list: List of residual values after PCA transformation.
46
+ distance_list: List of distances between residuals and cluster centers.
47
+ resid_list for RTU 1:onverted to 0 and 1 based on the threshold
48
+ resid_list for RTU 2: converted to 0 and 1 based on the threshold
49
+ fault_1: Flag indicating the presence of fault in RTU 1.
50
+ fault_2: Flag indicating the presence of fault in RTU 2.
51
+