Revert "lstm pipeline"

This reverts commit 1af5459d233d042ff693c634a4dd9d5b7c1f1944.

MA_temp_model.h5

@@ -1,3 +0,0 @@
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buildingdata.csv

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energy_model.h5

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energy_model_01.h5

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kmeans_model.pkl

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kmeans_model1.pkl

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kmeans_model2.pkl

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kmeans_model3.pkl

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kmeans_model4.pkl

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mqttclient.ipynb

@@ -2,14 +2,14 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\jerin\\AppData\\Local\\Temp\\ipykernel_3260\\368714138.py:22: DeprecationWarning: Callback API version 1 is deprecated, update to latest version\n",
+      "C:\\Users\\jerin\\AppData\\Local\\Temp\\ipykernel_6272\\1211754903.py:21: DeprecationWarning: Callback API version 1 is deprecated, update to latest version\n",
       "  client = mqtt.Client(mqtt.CallbackAPIVersion.VERSION1)\n"
      ]
     },
@@ -17,13 +17,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'date': '2018-05-02 00:09:00', 'hp_hws_temp': 96.3, 'rtu_003_sa_temp': 64.2, 'rtu_003_oadmpr_pct': 88.4, 'rtu_003_ra_temp': 72.5, 'rtu_003_oa_temp': 61.6, 'rtu_003_ma_temp': 63.5, 'rtu_003_sf_vfd_spd_fbk_tn': 77.8, 'rtu_003_rf_vfd_spd_fbk_tn': 54.3, 'rtu_004_sa_temp': 68.9, 'rtu_004_oadmpr_pct': 54.2, 'rtu_004_ra_temp': 73.3, 'rtu_004_oa_temp': 67.4, 'rtu_004_ma_temp': 67.6, 'rtu_004_sf_vfd_spd_fbk_tn': 75.7, 'rtu_004_rf_vfd_spd_fbk_tn': 78.3, 'air_temp_set_1': 14.9, 'air_temp_set_2': 14.44, 'dew_point_temperature_set_1d': 7.02, 'relative_humidity_set_1': 59.5, 'solar_radiation_set_1': 335.9}\n",
-      "{'date': '2018-05-02 00:10:00', 'hp_hws_temp': 97.0, 'rtu_003_sa_temp': 64.1, 'rtu_003_oadmpr_pct': 88.4, 'rtu_003_ra_temp': 72.4, 'rtu_003_oa_temp': 61.9, 'rtu_003_ma_temp': 63.3, 'rtu_003_sf_vfd_spd_fbk_tn': 77.2, 'rtu_003_rf_vfd_spd_fbk_tn': 52.5, 'rtu_004_sa_temp': 68.8, 'rtu_004_oadmpr_pct': 80.6, 'rtu_004_ra_temp': 73.3, 'rtu_004_oa_temp': 68.2, 'rtu_004_ma_temp': 66.1, 'rtu_004_sf_vfd_spd_fbk_tn': 75.6, 'rtu_004_rf_vfd_spd_fbk_tn': 67.3, 'air_temp_set_1': 14.9, 'air_temp_set_2': 14.44, 'dew_point_temperature_set_1d': 7.02, 'relative_humidity_set_1': 59.5, 'solar_radiation_set_1': 335.9}\n",
-      "{'date': '2018-05-02 00:11:00', 'hp_hws_temp': 97.6, 'rtu_003_sa_temp': 64.1, 'rtu_003_oadmpr_pct': 88.4, 'rtu_003_ra_temp': 72.4, 'rtu_003_oa_temp': 62.0, 'rtu_003_ma_temp': 63.8, 'rtu_003_sf_vfd_spd_fbk_tn': 78.2, 'rtu_003_rf_vfd_spd_fbk_tn': 56.7, 'rtu_004_sa_temp': 67.3, 'rtu_004_oadmpr_pct': 76.6, 'rtu_004_ra_temp': 73.4, 'rtu_004_oa_temp': 68.6, 'rtu_004_ma_temp': 64.3, 'rtu_004_sf_vfd_spd_fbk_tn': 78.6, 'rtu_004_rf_vfd_spd_fbk_tn': 77.5, 'air_temp_set_1': 14.9, 'air_temp_set_2': 14.44, 'dew_point_temperature_set_1d': 7.02, 'relative_humidity_set_1': 59.5, 'solar_radiation_set_1': 335.9}\n",
-      "{'date': '2018-05-02 00:12:00', 'hp_hws_temp': 98.3, 'rtu_003_sa_temp': 64.2, 'rtu_003_oadmpr_pct': 88.4, 'rtu_003_ra_temp': 72.4, 'rtu_003_oa_temp': 62.1, 'rtu_003_ma_temp': 64.1, 'rtu_003_sf_vfd_spd_fbk_tn': 76.4, 'rtu_003_rf_vfd_spd_fbk_tn': 52.9, 'rtu_004_sa_temp': 66.7, 'rtu_004_oadmpr_pct': 51.4, 'rtu_004_ra_temp': 73.4, 'rtu_004_oa_temp': 68.9, 'rtu_004_ma_temp': 65.1, 'rtu_004_sf_vfd_spd_fbk_tn': 79.6, 'rtu_004_rf_vfd_spd_fbk_tn': 82.5, 'air_temp_set_1': 14.9, 'air_temp_set_2': 14.44, 'dew_point_temperature_set_1d': 7.02, 'relative_humidity_set_1': 59.5, 'solar_radiation_set_1': 335.9}\n",
-      "{'date': '2018-05-02 00:13:00', 'hp_hws_temp': 98.9, 'rtu_003_sa_temp': 64.3, 'rtu_003_oadmpr_pct': 88.4, 'rtu_003_ra_temp': 72.3, 'rtu_003_oa_temp': 62.2, 'rtu_003_ma_temp': 63.8, 'rtu_003_sf_vfd_spd_fbk_tn': 78.5, 'rtu_003_rf_vfd_spd_fbk_tn': 57.0, 'rtu_004_sa_temp': 67.8, 'rtu_004_oadmpr_pct': 52.4, 'rtu_004_ra_temp': 73.4, 'rtu_004_oa_temp': 68.9, 'rtu_004_ma_temp': 67.8, 'rtu_004_sf_vfd_spd_fbk_tn': 78.3, 'rtu_004_rf_vfd_spd_fbk_tn': 73.9, 'air_temp_set_1': 14.9, 'air_temp_set_2': 14.44, 'dew_point_temperature_set_1d': 7.02, 'relative_humidity_set_1': 59.5, 'solar_radiation_set_1': 335.9}\n",
-      "{'date': '2018-05-02 00:14:00', 'hp_hws_temp': 99.3, 'rtu_003_sa_temp': 64.4, 'rtu_003_oadmpr_pct': 88.4, 'rtu_003_ra_temp': 72.4, 'rtu_003_oa_temp': 62.5, 'rtu_003_ma_temp': 63.9, 'rtu_003_sf_vfd_spd_fbk_tn': 75.9, 'rtu_003_rf_vfd_spd_fbk_tn': 49.8, 'rtu_004_sa_temp': 68.8, 'rtu_004_oadmpr_pct': 52.4, 'rtu_004_ra_temp': 73.3, 'rtu_004_oa_temp': 68.1, 'rtu_004_ma_temp': 68.3, 'rtu_004_sf_vfd_spd_fbk_tn': 76.5, 'rtu_004_rf_vfd_spd_fbk_tn': 74.7, 'air_temp_set_1': 14.9, 'air_temp_set_2': 14.44, 'dew_point_temperature_set_1d': 7.02, 'relative_humidity_set_1': 59.5, 'solar_radiation_set_1': 335.9}\n",
-      "{'date': '2018-05-02 00:15:00', 'hp_hws_temp': 99.0, 'rtu_003_sa_temp': 63.9, 'rtu_003_oadmpr_pct': 88.4, 'rtu_003_ra_temp': 72.3, 'rtu_003_oa_temp': 62.6, 'rtu_003_ma_temp': 63.6, 'rtu_003_sf_vfd_spd_fbk_tn': 74.3, 'rtu_003_rf_vfd_spd_fbk_tn': 53.5, 'rtu_004_sa_temp': 69.1, 'rtu_004_oadmpr_pct': 79.0, 'rtu_004_ra_temp': 73.3, 'rtu_004_oa_temp': 67.2, 'rtu_004_ma_temp': 66.6, 'rtu_004_sf_vfd_spd_fbk_tn': 75.3, 'rtu_004_rf_vfd_spd_fbk_tn': 69.0, 'air_temp_set_1': 16.0, 'air_temp_set_2': 15.48, 'dew_point_temperature_set_1d': 6.63, 'relative_humidity_set_1': 54.0, 'solar_radiation_set_1': 426.3}\n"
+      "{'sa_temp': 68.6, 'ma_temp': 70.0}\n"
      ]
     },
     {
@@ -33,7 +27,7 @@
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[6], line 26\u001b[0m\n\u001b[0;32m     24\u001b[0m client\u001b[38;5;241m.\u001b[39mconnect(broker_address, broker_port)\n\u001b[0;32m     25\u001b[0m client\u001b[38;5;241m.\u001b[39msubscribe(topic)\n\u001b[1;32m---> 26\u001b[0m \u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloop_forever\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[1;32mIn[2], line 25\u001b[0m\n\u001b[0;32m     23\u001b[0m client\u001b[38;5;241m.\u001b[39mconnect(broker_address, broker_port)\n\u001b[0;32m     24\u001b[0m client\u001b[38;5;241m.\u001b[39msubscribe(topic)\n\u001b[1;32m---> 25\u001b[0m \u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloop_forever\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
       "File \u001b[1;32mc:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\paho\\mqtt\\client.py:2291\u001b[0m, in \u001b[0;36mClient.loop_forever\u001b[1;34m(self, timeout, retry_first_connection)\u001b[0m\n\u001b[0;32m   2289\u001b[0m rc \u001b[38;5;241m=\u001b[39m MQTTErrorCode\u001b[38;5;241m.\u001b[39mMQTT_ERR_SUCCESS\n\u001b[0;32m   2290\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m rc \u001b[38;5;241m==\u001b[39m MQTTErrorCode\u001b[38;5;241m.\u001b[39mMQTT_ERR_SUCCESS:\n\u001b[1;32m-> 2291\u001b[0m     rc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_loop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2292\u001b[0m     \u001b[38;5;66;03m# We don't need to worry about locking here, because we've\u001b[39;00m\n\u001b[0;32m   2293\u001b[0m     \u001b[38;5;66;03m# either called loop_forever() when in single threaded mode, or\u001b[39;00m\n\u001b[0;32m   2294\u001b[0m     \u001b[38;5;66;03m# in multi threaded mode when loop_stop() has been called and\u001b[39;00m\n\u001b[0;32m   2295\u001b[0m     \u001b[38;5;66;03m# so no other threads can access _out_packet or _messages.\u001b[39;00m\n\u001b[0;32m   2296\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_thread_terminate \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m   2297\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_out_packet) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m   2298\u001b[0m             \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_out_messages) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m):\n",
       "File \u001b[1;32mc:\\Users\\jerin\\anaconda3\\envs\\smartbuilding\\Lib\\site-packages\\paho\\mqtt\\client.py:1657\u001b[0m, in \u001b[0;36mClient._loop\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m   1654\u001b[0m     rlist \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sock, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sockpairR]\n\u001b[0;32m   1656\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1657\u001b[0m     socklist \u001b[38;5;241m=\u001b[39m select\u001b[38;5;241m.\u001b[39mselect(rlist, wlist, [], timeout)\n\u001b[0;32m   1658\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m   1659\u001b[0m     \u001b[38;5;66;03m# Socket isn't correct type, in likelihood connection is lost\u001b[39;00m\n\u001b[0;32m   1660\u001b[0m     \u001b[38;5;66;03m# ... or we called disconnect(). In that case the socket will\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1663\u001b[0m     \u001b[38;5;66;03m# rc != MQTT_ERR_SUCCESS and we don't want state to change from\u001b[39;00m\n\u001b[0;32m   1664\u001b[0m     \u001b[38;5;66;03m# mqtt_cs_disconnecting.\u001b[39;00m\n\u001b[0;32m   1665\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m (_ConnectionState\u001b[38;5;241m.\u001b[39mMQTT_CS_DISCONNECTING, _ConnectionState\u001b[38;5;241m.\u001b[39mMQTT_CS_DISCONNECTED):\n",
       "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
@@ -54,9 +48,8 @@
     "def on_message(client, userdata, message):\n",
     "    global df\n",
     "    payload = json.loads(message.payload.decode())\n",
-    "    # sa_temp = payload[\"sa_temp\"]\n",
-    "    # ma_temp = payload[\"ma_temp\"]\n",
-    "    \n",
+    "    sa_temp = payload[\"sa_temp\"]\n",
+    "    ma_temp = payload[\"ma_temp\"]\n",
     "    print(payload)\n",
     "    # df.loc[len(df)] = {\"sa_temp\": sa_temp, \"ma_temp\": ma_temp}\n",
     " \n",

mqttpublisher.ipynb

@@ -2,379 +2,17 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\jerin\\AppData\\Local\\Temp\\ipykernel_4616\\2478473330.py:13: DeprecationWarning: Callback API version 1 is deprecated, update to latest version\n",
+      "C:\\Users\\jerin\\AppData\\Local\\Temp\\ipykernel_18680\\2251714346.py:13: DeprecationWarning: Callback API version 1 is deprecated, update to latest version\n",
       "  client = mqtt.Client(mqtt.CallbackAPIVersion.VERSION1, clientId)\n"
      ]
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
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-      "published!\n",
-      "published!\n",
-      "published!\n",
-      "published!\n",
-      "published!\n",
-      "published!\n",
-      "published!\n",
-      "published!\n"
-     ]
-    },
     {
      "ename": "KeyboardInterrupt",
      "evalue": "",
@@ -382,8 +20,7 @@
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[10], line 94\u001b[0m\n\u001b[0;32m     90\u001b[0m         time\u001b[38;5;241m.\u001b[39msleep(\u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m     93\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m---> 94\u001b[0m     \u001b[43mpublish_sensor_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     95\u001b[0m     \u001b[38;5;66;03m# time.sleep(0.1)\u001b[39;00m\n\u001b[0;32m     96\u001b[0m client\u001b[38;5;241m.\u001b[39mdisconnect()\n",
-      "Cell \u001b[1;32mIn[10], line 90\u001b[0m, in \u001b[0;36mpublish_sensor_data\u001b[1;34m()\u001b[0m\n\u001b[0;32m     55\u001b[0m client\u001b[38;5;241m.\u001b[39mpublish(topic, payload\u001b[38;5;241m=\u001b[39mjson\u001b[38;5;241m.\u001b[39mdumps({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhp_hws_temp\u001b[39m\u001b[38;5;124m'\u001b[39m:hp_hws_temp,\n\u001b[0;32m     56\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrtu_003_sa_temp\u001b[39m\u001b[38;5;124m'\u001b[39m:rtu_003_sa_temp,\n\u001b[0;32m     57\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrtu_003_oadmpr_pct\u001b[39m\u001b[38;5;124m'\u001b[39m: rtu_003_oadmpr_pct,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     87\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrelative_humidity_set_1\u001b[39m\u001b[38;5;124m'\u001b[39m:relative_humidity_set_1,\n\u001b[0;32m     88\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msolar_radiation_set_1\u001b[39m\u001b[38;5;124m'\u001b[39m:solar_radiation_set_1}))\n\u001b[0;32m     89\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpublished!\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m---> 90\u001b[0m time\u001b[38;5;241m.\u001b[39msleep(\u001b[38;5;241m2\u001b[39m)\n",
+      "Cell \u001b[1;32mIn[1], line 28\u001b[0m\n\u001b[0;32m     26\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m     27\u001b[0m     publish_sensor_data()\n\u001b[1;32m---> 28\u001b[0m     time\u001b[38;5;241m.\u001b[39msleep(\u001b[38;5;241m5\u001b[39m)\n\u001b[0;32m     29\u001b[0m client\u001b[38;5;241m.\u001b[39mdisconnect()\n",
       "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
@@ -399,99 +36,24 @@
     "broker_address = \"localhost\"\n",
     "broker_port = 1883\n",
     "\n",
-    "df = pd.read_csv(\"sample_data_smooth_01.csv\")\n",
+    "df = pd.read_csv(\"buildingdata.csv\")\n",
     "\n",
     "client = mqtt.Client(mqtt.CallbackAPIVersion.VERSION1, clientId)\n",
     "client.connect(broker_address, broker_port)\n",
     "topic = \"sensor_data\"\n",
     "\n",
-    "def publish_sensor_data(): \n",
+    "def publish_sensor_data():\n",
+    "    # temp = round(random.uniform(20, 30), 2)  \n",
+    "    # pressure = round(random.uniform(900, 1100), 2)  \n",
     "    for index, row in df.iterrows():\n",
-    "        hp_hws_temp = row['hp_hws_temp']\n",
-    "        rtu_003_sa_temp = row['rtu_003_sa_temp']\n",
-    "        rtu_003_oadmpr_pct = row['rtu_003_oadmpr_pct']\n",
-    "        rtu_003_ra_temp = row['rtu_003_ra_temp']\n",
-    "        rtu_003_oa_temp = row['rtu_003_oa_temp']\n",
-    "        rtu_003_ma_temp = row['rtu_003_ma_temp']\n",
-    "        rtu_003_sf_vfd_spd_fbk_tn = row['rtu_003_sf_vfd_spd_fbk_tn']\n",
-    "        rtu_003_rf_vfd_spd_fbk_tn =row['rtu_003_rf_vfd_spd_fbk_tn']\n",
-    "        rtu_004_sa_temp = row['rtu_004_sa_temp']\n",
-    "        rtu_004_oadmpr_pct = row['rtu_004_oadmpr_pct']\n",
-    "        rtu_004_ra_temp = row['rtu_004_ra_temp']\n",
-    "        rtu_004_oa_temp = row['rtu_004_oa_temp']\n",
-    "        rtu_004_ma_temp = row['rtu_004_ma_temp']\n",
-    "        rtu_004_sf_vfd_spd_fbk_tn = row['rtu_004_sf_vfd_spd_fbk_tn']\n",
-    "        rtu_004_rf_vfd_spd_fbk_tn = row['rtu_004_rf_vfd_spd_fbk_tn']\n",
-    "        rtu_001_sa_temp = row['rtu_001_sa_temp']\n",
-    "        rtu_001_oadmpr_pct = row['rtu_001_oadmpr_pct']\n",
-    "        rtu_001_ra_temp = row['rtu_001_ra_temp']\n",
-    "        rtu_001_oa_temp = row['rtu_001_oa_temp']\n",
-    "        rtu_001_ma_temp = row['rtu_001_ma_temp']\n",
-    "        rtu_001_sf_vfd_spd_fbk_tn = row['rtu_001_sf_vfd_spd_fbk_tn']\n",
-    "        rtu_001_rf_vfd_spd_fbk_tn =row['rtu_001_rf_vfd_spd_fbk_tn']\n",
-    "        rtu_002_sa_temp = row['rtu_002_sa_temp']\n",
-    "        rtu_002_oadmpr_pct = row['rtu_002_oadmpr_pct']\n",
-    "        rtu_002_ra_temp = row['rtu_002_ra_temp']\n",
-    "        rtu_002_oa_temp = row['rtu_002_oa_temp']\n",
-    "        rtu_002_ma_temp = row['rtu_002_ma_temp']\n",
-    "        rtu_002_sf_vfd_spd_fbk_tn = row['rtu_002_sf_vfd_spd_fbk_tn']\n",
-    "        rtu_002_rf_vfd_spd_fbk_tn = row['rtu_002_rf_vfd_spd_fbk_tn']\n",
-    "        rtu_004_sat_sp_tn = row['rtu_004_sat_sp_tn']\n",
-    "        rtu_003_sat_sp_tn = row['rtu_003_sat_sp_tn']\n",
-    "        rtu_001_sat_sp_tn = row['rtu_001_sat_sp_tn']\n",
-    "        rtu_002_sat_sp_tn = row['rtu_002_sat_sp_tn']\n",
-    "        air_temp_set_1 = row['air_temp_set_1']\n",
-    "        air_temp_set_2 = row['air_temp_set_2']\n",
-    "        dew_point_temperature_set_1d = row['dew_point_temperature_set_1d']\n",
-    "        relative_humidity_set_1 = row['relative_humidity_set_1']\n",
-    "        solar_radiation_set_1 = row['solar_radiation_set_1']\n",
-    "        \n",
-    "        \n",
-    "        client.publish(topic, payload=json.dumps({'hp_hws_temp':hp_hws_temp,\n",
-    "        'rtu_003_sa_temp':rtu_003_sa_temp,\n",
-    "        'rtu_003_oadmpr_pct': rtu_003_oadmpr_pct,\n",
-    "        'rtu_003_ra_temp':rtu_003_ra_temp,\n",
-    "        'rtu_003_oa_temp': rtu_003_oa_temp,\n",
-    "        'rtu_003_ma_temp': rtu_003_ma_temp,\n",
-    "        'rtu_003_sf_vfd_spd_fbk_tn': rtu_003_sf_vfd_spd_fbk_tn,\n",
-    "        'rtu_003_rf_vfd_spd_fbk_tn':rtu_003_rf_vfd_spd_fbk_tn,\n",
-    "        'rtu_004_sa_temp':rtu_004_sa_temp,\n",
-    "        'rtu_004_oadmpr_pct':rtu_004_oadmpr_pct,\n",
-    "        'rtu_004_ra_temp':rtu_004_ra_temp,\n",
-    "        'rtu_004_oa_temp':rtu_004_oa_temp,\n",
-    "        'rtu_004_ma_temp':rtu_004_ma_temp,\n",
-    "        'rtu_004_sf_vfd_spd_fbk_tn':rtu_004_sf_vfd_spd_fbk_tn,\n",
-    "        'rtu_004_rf_vfd_spd_fbk_tn':rtu_004_rf_vfd_spd_fbk_tn,\n",
-    "        'rtu_001_sa_temp':rtu_001_sa_temp,\n",
-    "        'rtu_001_oadmpr_pct': rtu_001_oadmpr_pct,\n",
-    "        'rtu_001_ra_temp':rtu_001_ra_temp,\n",
-    "        'rtu_001_oa_temp': rtu_001_oa_temp,\n",
-    "        'rtu_001_ma_temp': rtu_001_ma_temp,\n",
-    "        'rtu_001_sf_vfd_spd_fbk_tn': rtu_001_sf_vfd_spd_fbk_tn,\n",
-    "        'rtu_001_rf_vfd_spd_fbk_tn':rtu_001_rf_vfd_spd_fbk_tn,\n",
-    "        'rtu_002_sa_temp':rtu_002_sa_temp,\n",
-    "        'rtu_002_oadmpr_pct':rtu_002_oadmpr_pct,\n",
-    "        'rtu_002_ra_temp':rtu_002_ra_temp,\n",
-    "        'rtu_002_oa_temp':rtu_002_oa_temp,\n",
-    "        'rtu_002_ma_temp':rtu_002_ma_temp,\n",
-    "        'rtu_002_sf_vfd_spd_fbk_tn':rtu_002_sf_vfd_spd_fbk_tn,\n",
-    "        'rtu_002_rf_vfd_spd_fbk_tn':rtu_002_rf_vfd_spd_fbk_tn,\n",
-    "        'rtu_004_sat_sp_tn':rtu_004_sat_sp_tn,\n",
-    "        'rtu_003_sat_sp_tn' :rtu_003_sat_sp_tn,\n",
-    "        'rtu_001_sat_sp_tn':rtu_001_sat_sp_tn,\n",
-    "        'rtu_002_sat_sp_tn':rtu_002_sat_sp_tn,\n",
-    "        'air_temp_set_1':air_temp_set_1,\n",
-    "        'air_temp_set_2':air_temp_set_2,\n",
-    "        'dew_point_temperature_set_1d':dew_point_temperature_set_1d,\n",
-    "        'relative_humidity_set_1':relative_humidity_set_1,\n",
-    "        'solar_radiation_set_1':solar_radiation_set_1}))\n",
-    "        print(\"published!\")\n",
-    "        time.sleep(2)\n",
+    "        sa_temp = row['rtu_004_sa_temp']\n",
+    "        ma_temp = row['rtu_004_ma_temp']\n",
+    "    client.publish(topic, payload=json.dumps({\"sa_temp\": sa_temp, \"ma_temp\": ma_temp}))\n",
     "\n",
     "\n",
     "while True:\n",
     "    publish_sensor_data()\n",
-    "    # time.sleep(0.1)\n",
+    "    time.sleep(5)\n",
     "client.disconnect()"
    ]
   },

pca_model.pkl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:225896826ab8bfb9ea32f0f3057c1292ca5bc238d2ea87858c25fa8da5960b46
-size 1179

sample_data.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:aa13789e2460e1b575a63d32ca46615d5df237c62de5da2f4da22c1e8edc945c
-size 144290729

sample_data_01.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:8a00542784412e885f2ef707e3dd86b58023e678b35221fefbce5af9ec6cb10d
-size 59769388

sample_data_02.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:3fe51d16cfeffdc7df991e0f27da089da5e9f1260e2ab9c316cd6786999d8d29
-size 119517162

sample_data_smooth_01.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:efbfdef5e85cb88d20e7e50ac2351f60d20f07a31b2a40cf4c925596097c5cc4
-size 299846687

scaler.pkl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:63d98018c0b4da0c42820e9017c91944d1486e85471cd44a9a6ab121e7d2c64a
-size 1101

scaler_1.pkl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:778df19577ac7a34c16dfda1b32f26800c62ff687130e22e58715b8d495dcb37
-size 1437

src/main.py

@@ -1,42 +0,0 @@
-import json
-from rtu.RTUAnomalizer import RTUAnomalizer
-from rtu.RTUPipeline import RTUPipeline
-import paho.mqtt.client as mqtt
-
-
-        
-def main():
-    rtu_data_pipeline = RTUPipeline(scaler_path="rtu/models/scaler_1.pkl")
-    rtu_anomalizer = RTUAnomalizer(
-        prediction_model_path="rtu/models/lstm_4rtu_smooth_02.keras",
-        clustering_model_paths=[
-            "rtu/models/kmeans_model1.pkl",
-            "rtu/models/kmeans_model2.pkl",
-            "rtu/models/kmeans_model3.pkl",
-            "rtu/models/kmeans_model4.pkl",
-        ],
-        num_inputs=rtu_data_pipeline.num_inputs,
-        num_outputs=rtu_data_pipeline.num_outputs
-    )
-    
-    def on_message(client, userdata, message):
-        print(json.loads(message.payload.decode()))
-        df_new, df_trans = rtu_data_pipeline.fit(message)
-        out = rtu_anomalizer.predict(df_new, df_trans, rtu_data_pipeline.scaler)
-        print(out)
-    
-    broker_address = "localhost"
-    broker_port = 1883
-    topic = "sensor_data"
-    client = mqtt.Client(mqtt.CallbackAPIVersion.VERSION1)
-    client.on_message = on_message
-    client.connect(broker_address, broker_port)
-    client.subscribe(topic)
-    client.loop_forever()
-    
-    
-if __name__=='__main__':
-    # 
-    main()
-   
-    

src/rtu/RTUAnomalizer.py

@@ -1,68 +1,14 @@
-import numpy as np
 from tensorflow.keras.models import load_model
-import joblib
+
 
 class RTUAnomalizer:
     model = None
-    kmeans_models = []
-
-    def __init__(self, prediction_model_path = None, clustering_model_paths= None, num_inputs = None, num_outputs = None):
-        
-        self.num_inputs = num_inputs
-        self.num_outputs = num_outputs
-        if not prediction_model_path is None and not clustering_model_paths is None:
-            self.load_models(prediction_model_path, clustering_model_paths)
-
-    def initialize_lists(size=30):
-        initial_values = [0] * size
-        return initial_values.copy(), initial_values.copy(), initial_values.copy()
-
-    def load_models(self, prediction_model_path, clustering_model_paths):
-        self.model = load_model(prediction_model_path)
-        
-        for path in clustering_model_paths:
-            self.kmeans_models.append(joblib.load(path))
-            
-    def predict(self, df_new):
-        return self.model.predict(df_new)
-
-    def calculate_residuals(self,df_trans, pred):
-        actual = df_trans[30,:self.num_outputs+1]
-        resid = actual - pred
-        return actual, resid
-
-    def resize_prediction(self,pred, df_trans):
-        pred.resize((pred.shape[0], pred.shape[1] + len(df_trans[30,self.num_outputs+1:])))
-        pred[:, -len(df_trans[30,self.num_outputs+1:]):] = df_trans[30,self.num_outputs+1:]
-        return pred
-
-    def inverse_transform(scaler, pred, df_trans):
-        pred = scaler.inverse_transform(np.array(pred))
-        actual = scaler.inverse_transform(np.array([df_trans[30,:]]))
-        return actual, pred
 
-    def update_lists(actual_list, pred_list, resid_list, actual, pred, resid):
-        actual_list.pop(0)
-        pred_list.pop(0)
-        resid_list.pop(0)
-        actual_list.append(actual[0,1])
-        pred_list.append(pred[0,1])
-        resid_list.append(resid[0,1])
-        return actual_list, pred_list, resid_list
+    def __init__(self):
+        pass
 
-    def calculate_distances(self,resid):
-        dist = []
-        for i, model in enumerate(self.kmeans_models):
-            dist.append(np.linalg.norm(resid[:,(i*7)+1:(i*7)+8]-model.cluster_centers_[0], ord=2, axis=1))
-            
-        return np.array(dist)
+    def load_model(self, model_path):
+        self.model = load_model(model_path)
 
-    def pipeline(self, df_new, df_trans, scaler):
-        actual_list, pred_list, resid_list = self.initialize_lists()
-        pred = self.predict(df_new)
-        actual, resid = self.calculate_residuals(df_trans, pred)
-        pred = self.resize_prediction(pred, df_trans)
-        actual, pred = self.inverse_transform(scaler, pred, df_trans)
-        actual_list, pred_list, resid_list = self.update_lists(actual_list, pred_list, resid_list, actual, pred, resid)
-        dist = self.calculate_distances(resid)
-        return actual_list, pred_list, resid_list, dist
+    def pipeline(self, data):
+        return

src/rtu/RTUPipeline.py

@@ -1,140 +1,19 @@
-import json
-import joblib
-import pandas as pd
 from sklearn.preprocessing import StandardScaler
 from pickle import load
-import numpy as np
 
 
 class RTUPipeline:
     scaler = None
 
-    def __init__(self, scaler_path=None):
-        self.output_col_names = [
-            "hp_hws_temp",
-            "rtu_003_sa_temp",
-            "rtu_003_oadmpr_pct",
-            "rtu_003_ra_temp",
-            "rtu_003_oa_temp",
-            "rtu_003_ma_temp",
-            "rtu_003_sf_vfd_spd_fbk_tn",
-            "rtu_003_rf_vfd_spd_fbk_tn",
-            "rtu_004_sa_temp",
-            "rtu_004_oadmpr_pct",
-            "rtu_004_ra_temp",
-            "rtu_004_oa_temp",
-            "rtu_004_ma_temp",
-            "rtu_004_sf_vfd_spd_fbk_tn",
-            "rtu_004_rf_vfd_spd_fbk_tn",
-            "rtu_001_sa_temp",
-            "rtu_001_oadmpr_pct",
-            "rtu_001_ra_temp",
-            "rtu_001_oa_temp",
-            "rtu_001_ma_temp",
-            "rtu_001_sf_vfd_spd_fbk_tn",
-            "rtu_001_rf_vfd_spd_fbk_tn",
-            "rtu_002_sa_temp",
-            "rtu_002_oadmpr_pct",
-            "rtu_002_ra_temp",
-            "rtu_002_oa_temp",
-            "rtu_002_ma_temp",
-            "rtu_002_sf_vfd_spd_fbk_tn",
-            "rtu_002_rf_vfd_spd_fbk_tn",
-            # "rtu_004_sat_sp_tn",
-            # "rtu_003_sat_sp_tn",
-            # "rtu_001_sat_sp_tn",
-            # "rtu_002_sat_sp_tn",
-            # "air_temp_set_1",
-            # "air_temp_set_2",
-            # "dew_point_temperature_set_1d",
-            # "relative_humidity_set_1",
-            # "solar_radiation_set_1",
-        ]
-        
-        self.input_col_names  =    ["air_temp_set_1",
-            "air_temp_set_2",
-            "dew_point_temperature_set_1d",
-            "relative_humidity_set_1",
-            "solar_radiation_set_1",
-        ]
-        self.num_inputs = len(self.input_col_names)
-        self.num_outputs = len(self.output_col_names)
-        self.column_names = self.output_col_names+self.input_col_names
-
-        if scaler_path:
-            self.scaler = self.get_scaler(scaler_path)
-        self.df = pd.DataFrame(columns = self.column_names)
+    def __init__(self):
+        self.column_names = []
 
     def get_scaler(self, scaler_path):
-        return scaler_path
-
-    def get_window(self, df):
-        len_df = np.len(df)
-        if len_df > 30:
-            return df[len_df - 31 : len_df].astype("float32")
-        else:
-            return None
+        self.scaler = StandardScaler()
+        self.scaler = load(open(scaler_path, "rb"))
 
-    def transform_window(self, df_window):
-        return self.scaler.transform(df_window)
+    def get_data(self, data):
+        pass
 
-    def prepare_input(self,df_trans):
-        return df_trans[:30, :].reshape((1, 30, len(self.column_names)))
-    
-    def extract_data_from_message(self, message):
-        payload = json.loads(message.payload.decode())
-     
-        len_df = len(self.df)
-        # self.df.loc[len_df] = {'hp_hws_temp':payload['hp_hws_temp'],
-        # 'rtu_003_sa_temp':payload['rtu_003_sa_temp'],
-        # 'rtu_003_oadmpr_pct': payload["rtu_003_oadmpr_pct"],
-        # 'rtu_003_ra_temp':payload["rtu_003_ra_temp"],
-        # 'rtu_003_oa_temp': payload["rtu_003_oa_temp"],
-        # 'rtu_003_ma_temp': payload["rtu_003_ma_temp"],
-        # 'rtu_003_sf_vfd_spd_fbk_tn': payload["rtu_003_sf_vfd_spd_fbk_tn"],
-        # 'rtu_003_rf_vfd_spd_fbk_tn':payload["rtu_003_rf_vfd_spd_fbk_tn"],
-        # 'rtu_004_sa_temp':payload["rtu_004_sa_temp"],
-        # 'rtu_004_oadmpr_pct':payload["rtu_004_oadmpr_pct"],
-        # 'rtu_004_ra_temp':payload["rtu_004_ra_temp"],
-        # 'rtu_004_oa_temp':payload["rtu_004_oa_temp"],
-        # 'rtu_004_ma_temp':payload["rtu_004_ma_temp"],
-        # 'rtu_004_sf_vfd_spd_fbk_tn':payload["rtu_004_sf_vfd_spd_fbk_tn"],
-        # 'rtu_004_rf_vfd_spd_fbk_tn':payload["rtu_004_rf_vfd_spd_fbk_tn"],
-        # 'rtu_001_sa_temp':payload["rtu_001_sa_temp"],
-        # 'rtu_001_oadmpr_pct': payload["rtu_001_oadmpr_pct"],
-        # 'rtu_001_ra_temp':payload["rtu_001_ra_temp"],
-        # 'rtu_001_oa_temp': payload["rtu_001_oa_temp"],
-        # 'rtu_001_ma_temp': payload["rtu_001_ma_temp"],
-        # 'rtu_001_sf_vfd_spd_fbk_tn': payload["rtu_001_sf_vfd_spd_fbk_tn"],
-        # 'rtu_001_rf_vfd_spd_fbk_tn':payload["rtu_001_rf_vfd_spd_fbk_tn"],
-        # 'rtu_002_sa_temp':payload["rtu_002_sa_temp"],
-        # 'rtu_002_oadmpr_pct':payload["rtu_002_oadmpr_pct"],
-        # 'rtu_002_ra_temp':payload["rtu_002_ra_temp"],
-        # 'rtu_002_oa_temp':payload["rtu_002_oa_temp"],
-        # 'rtu_002_ma_temp':payload["rtu_002_ma_temp"],
-        # 'rtu_002_sf_vfd_spd_fbk_tn':payload["rtu_002_sf_vfd_spd_fbk_tn"],
-        # 'rtu_002_rf_vfd_spd_fbk_tn':payload["rtu_002_rf_vfd_spd_fbk_tn"],
-        # 'rtu_004_sat_sp_tn':payload["rtu_004_sat_sp_tn"],
-        # 'rtu_003_sat_sp_tn' :payload["rtu_003_sat_sp_tn"],
-        # 'rtu_001_sat_sp_tn':payload["rtu_001_sat_sp_tn"],
-        # 'rtu_002_sat_sp_tn':payload["rtu_002_sat_sp_tn"],
-        # 'air_temp_set_1':payload["air_temp_set_1"],
-        # 'air_temp_set_2':payload["air_temp_set_2"],
-        # 'dew_point_temperature_set_1d':payload["dew_point_temperature_set_1d"],
-        # 'relative_humidity_set_1':payload["relative_humidity_set_1"],
-        # 'solar_radiation_set_1':payload["solar_radiation_set_1"]}
-        
-        self.df.loc[len_df] = {}
-        for col in self.column_names:
-            self.df.loc[len_df][col] = payload[col]
-        return self.df
-            
-    
-    def fit(self,message):
-        len_df = np.len(df)
-        df = self.extract_data_from_message(message)
-        df_window = self.get_window(df, len_df)
-        if df_window is not None:
-            df_trans = self.transform_window(df_window, self.scaler)
-            df_new = self.prepare_input(df_trans)
-        return df_new,df_trans
+    def pipeline(self, data):
+        pass

src/rtu/models/kmeans_model1.pkl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:aaa404e937bf8fe9d52b38aa1e11f87d544530a2d4789b46d20b5a1665e15846
-size 2064297

src/rtu/models/kmeans_model2.pkl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9cf710504c910255e59d0609617fecf74ee8ad6710f4ba4109d9de5aae0cd879
-size 2064297

src/rtu/models/kmeans_model3.pkl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1fb375913725bf309d514f55b71799daead266db7caad09f02ab9cfdd56bdc0a
-size 2064297

src/rtu/models/kmeans_model4.pkl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:cfbe4b1a5c1aa4b00e8e932c77dd8ed5ce99fbe5efd4960079d14e380206f9f2
-size 2064297

src/rtu/models/scaler_1.pkl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:778df19577ac7a34c16dfda1b32f26800c62ff687130e22e58715b8d495dcb37
-size 1437

streamlit.py

@@ -6,21 +6,9 @@ import plotly.express as px # interactive charts
 import paho.mqtt.client as mqtt
 import json
 import warnings
-from tensorflow.keras.models import load_model
-import joblib
-import plotly.graph_objects as go
 warnings.filterwarnings('ignore')
 
-
-model = load_model("lstm_4rtu_smooth_02.keras")
-scaler = joblib.load('scaler_1.pkl')
-# kmeans = joblib.load('kmeans_model.pkl')
-kmeans1 = joblib.load('kmeans_model1.pkl')
-kmeans2 = joblib.load('kmeans_model2.pkl')
-kmeans3 = joblib.load('kmeans_model3.pkl')
-kmeans4 = joblib.load('kmeans_model4.pkl')
-pca = joblib.load('pca_model.pkl')
-
+# df = pd.read_csv('buildingdata.csv')
 st.set_page_config(
     page_title = 'Real-Time Data Buliding 59',
     page_icon = '✅',
@@ -34,209 +22,26 @@ broker_address = "localhost"
 broker_port = 1883
 topic = "sensor_data"
 
-df = pd.DataFrame(columns=['hp_hws_temp',
- 'rtu_003_sa_temp',
- 'rtu_003_oadmpr_pct',
- 'rtu_003_ra_temp',
- 'rtu_003_oa_temp',
- 'rtu_003_ma_temp',
- 'rtu_003_sf_vfd_spd_fbk_tn',
- 'rtu_003_rf_vfd_spd_fbk_tn',
- 'rtu_004_sa_temp',
- 'rtu_004_oadmpr_pct',
- 'rtu_004_ra_temp',
- 'rtu_004_oa_temp',
- 'rtu_004_ma_temp',
- 'rtu_004_sf_vfd_spd_fbk_tn',
- 'rtu_004_rf_vfd_spd_fbk_tn',
- 'rtu_001_sa_temp',
- 'rtu_001_oadmpr_pct',
- 'rtu_001_ra_temp',
- 'rtu_001_oa_temp',
- 'rtu_001_ma_temp',
- 'rtu_001_sf_vfd_spd_fbk_tn',
- 'rtu_001_rf_vfd_spd_fbk_tn',
- 'rtu_002_sa_temp',
- 'rtu_002_oadmpr_pct',
- 'rtu_002_ra_temp',
- 'rtu_002_oa_temp',
- 'rtu_002_ma_temp',
- 'rtu_002_sf_vfd_spd_fbk_tn',
- 'rtu_002_rf_vfd_spd_fbk_tn',
- 'rtu_004_sat_sp_tn',
- 'rtu_003_sat_sp_tn',
- 'rtu_001_sat_sp_tn',
- 'rtu_002_sat_sp_tn',
- 'air_temp_set_1',
- 'air_temp_set_2',
- 'dew_point_temperature_set_1d',
- 'relative_humidity_set_1',
- 'solar_radiation_set_1'])
-actual_list = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
-pred_list = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
-resid_list = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
-distance = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
-pca_x = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
-pca_y = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
+df = pd.DataFrame({"sa_temp":[60, 56, 66,69,70],"ma_temp":[30,40,50,60,65]})
 
 def on_message(client, userdata, message):
     global df
     payload = json.loads(message.payload.decode())
-    
-    hp_hws_temp = payload['hp_hws_temp']
-    rtu_003_sa_temp = payload['rtu_003_sa_temp']
-    rtu_003_oadmpr_pct = payload['rtu_003_oadmpr_pct']
-    rtu_003_ra_temp = payload['rtu_003_ra_temp']
-    rtu_003_oa_temp = payload['rtu_003_oa_temp']
-    rtu_003_ma_temp = payload['rtu_003_ma_temp']
-    rtu_003_sf_vfd_spd_fbk_tn = payload['rtu_003_sf_vfd_spd_fbk_tn']
-    rtu_003_rf_vfd_spd_fbk_tn =payload['rtu_003_rf_vfd_spd_fbk_tn']
-    rtu_004_sa_temp = payload['rtu_004_sa_temp']
-    rtu_004_oadmpr_pct = payload['rtu_004_oadmpr_pct']
-    rtu_004_ra_temp = payload['rtu_004_ra_temp']
-    rtu_004_oa_temp = payload['rtu_004_oa_temp']
-    rtu_004_ma_temp = payload['rtu_004_ma_temp']
-    rtu_004_sf_vfd_spd_fbk_tn = payload['rtu_004_sf_vfd_spd_fbk_tn']
-    rtu_004_rf_vfd_spd_fbk_tn = payload['rtu_004_rf_vfd_spd_fbk_tn']
-    rtu_001_sa_temp = payload['rtu_001_sa_temp']
-    rtu_001_oadmpr_pct = payload['rtu_001_oadmpr_pct']
-    rtu_001_ra_temp = payload['rtu_001_ra_temp']
-    rtu_001_oa_temp = payload['rtu_001_oa_temp']
-    rtu_001_ma_temp = payload['rtu_001_ma_temp']
-    rtu_001_sf_vfd_spd_fbk_tn = payload['rtu_001_sf_vfd_spd_fbk_tn']
-    rtu_001_rf_vfd_spd_fbk_tn =payload['rtu_001_rf_vfd_spd_fbk_tn']
-    rtu_002_sa_temp = payload['rtu_002_sa_temp']
-    rtu_002_oadmpr_pct = payload['rtu_002_oadmpr_pct']
-    rtu_002_ra_temp = payload['rtu_002_ra_temp']
-    rtu_002_oa_temp = payload['rtu_002_oa_temp']
-    rtu_002_ma_temp = payload['rtu_002_ma_temp']
-    rtu_002_sf_vfd_spd_fbk_tn = payload['rtu_002_sf_vfd_spd_fbk_tn']
-    rtu_002_rf_vfd_spd_fbk_tn = payload['rtu_002_rf_vfd_spd_fbk_tn']
-    rtu_004_sat_sp_tn = payload['rtu_004_sat_sp_tn']
-    rtu_003_sat_sp_tn = payload['rtu_003_sat_sp_tn']
-    rtu_001_sat_sp_tn = payload['rtu_001_sat_sp_tn']
-    rtu_002_sat_sp_tn = payload['rtu_002_sat_sp_tn']
-    air_temp_set_1 = payload['air_temp_set_1']
-    air_temp_set_2 = payload['air_temp_set_2']
-    dew_point_temperature_set_1d = payload['dew_point_temperature_set_1d']
-    relative_humidity_set_1 = payload['relative_humidity_set_1']
-    solar_radiation_set_1 = payload['solar_radiation_set_1']
-    
+    sa_temp = payload["sa_temp"]
+    ma_temp = payload["ma_temp"]
     len_df = len(df)
-    df.loc[len_df] = {'hp_hws_temp':hp_hws_temp,
-    'rtu_003_sa_temp':rtu_003_sa_temp,
-    'rtu_003_oadmpr_pct': rtu_003_oadmpr_pct,
-    'rtu_003_ra_temp':rtu_003_ra_temp,
-    'rtu_003_oa_temp': rtu_003_oa_temp,
-    'rtu_003_ma_temp': rtu_003_ma_temp,
-    'rtu_003_sf_vfd_spd_fbk_tn': rtu_003_sf_vfd_spd_fbk_tn,
-    'rtu_003_rf_vfd_spd_fbk_tn':rtu_003_rf_vfd_spd_fbk_tn,
-    'rtu_004_sa_temp':rtu_004_sa_temp,
-    'rtu_004_oadmpr_pct':rtu_004_oadmpr_pct,
-    'rtu_004_ra_temp':rtu_004_ra_temp,
-    'rtu_004_oa_temp':rtu_004_oa_temp,
-    'rtu_004_ma_temp':rtu_004_ma_temp,
-    'rtu_004_sf_vfd_spd_fbk_tn':rtu_004_sf_vfd_spd_fbk_tn,
-    'rtu_004_rf_vfd_spd_fbk_tn':rtu_004_rf_vfd_spd_fbk_tn,
-    'rtu_001_sa_temp':rtu_001_sa_temp,
-    'rtu_001_oadmpr_pct': rtu_001_oadmpr_pct,
-    'rtu_001_ra_temp':rtu_001_ra_temp,
-    'rtu_001_oa_temp': rtu_001_oa_temp,
-    'rtu_001_ma_temp': rtu_001_ma_temp,
-    'rtu_001_sf_vfd_spd_fbk_tn': rtu_001_sf_vfd_spd_fbk_tn,
-    'rtu_001_rf_vfd_spd_fbk_tn':rtu_001_rf_vfd_spd_fbk_tn,
-    'rtu_002_sa_temp':rtu_002_sa_temp,
-    'rtu_002_oadmpr_pct':rtu_002_oadmpr_pct,
-    'rtu_002_ra_temp':rtu_002_ra_temp,
-    'rtu_002_oa_temp':rtu_002_oa_temp,
-    'rtu_002_ma_temp':rtu_002_ma_temp,
-    'rtu_002_sf_vfd_spd_fbk_tn':rtu_002_sf_vfd_spd_fbk_tn,
-    'rtu_002_rf_vfd_spd_fbk_tn':rtu_002_rf_vfd_spd_fbk_tn,
-    'rtu_004_sat_sp_tn':rtu_004_sat_sp_tn,
-    'rtu_003_sat_sp_tn' :rtu_003_sat_sp_tn,
-    'rtu_001_sat_sp_tn':rtu_001_sat_sp_tn,
-    'rtu_002_sat_sp_tn':rtu_002_sat_sp_tn,
-    'air_temp_set_1':air_temp_set_1,
-    'air_temp_set_2':air_temp_set_2,
-    'dew_point_temperature_set_1d':dew_point_temperature_set_1d,
-    'relative_humidity_set_1':relative_humidity_set_1,
-    'solar_radiation_set_1':solar_radiation_set_1}
-    
-    if len_df>30:
-        df_window = df[len_df-31:len_df]
-        df_window = df_window.astype('float32')
-        df_trans = scaler.transform(df_window)
-        df_new = df_trans[:30,:].reshape((1,30,34))#
-        pred = model.predict(df_new)
-        pred_copy = pred.copy()
-        actual = df_trans[30,:29]#
-        resid = actual - pred
-        #---------
-        pred.resize((pred.shape[0], pred.shape[1] + len(df_trans[30,29:])))#
-        pred[:, -len(df_trans[30,29:]):] = df_trans[30,29:]#
-        pred = scaler.inverse_transform(np.array(pred))
-        actual = scaler.inverse_transform(np.array([df_trans[30,:]]))
-        #---------
-        actual_list.pop(0)
-        pred_list.pop(0)
-        resid_list.pop(0)
-        # distance.pop(0)
-        # pca_x.pop(0)
-        # pca_y.pop(0)
-        actual_list.append(actual[0,1])
-        pred_list.append(pred[0,1])
-        resid_list.append(resid[0,1])
-        # distance.append(np.linalg.norm(pred_copy-kmeans.cluster_centers_[0], ord=2, axis = 1))
-        # dist_color = [1 if num >= 5 else 0 for num in distance]
-        dist = []
-        dist.append(np.linalg.norm(resid[:,1:8]-kmeans1.cluster_centers_[0], ord=2, axis = 1))
-        dist.append(np.linalg.norm(resid[:,8:15]-kmeans2.cluster_centers_[0], ord=2, axis = 1))
-        dist.append(np.linalg.norm(resid[:,15:22]-kmeans3.cluster_centers_[0], ord=2, axis = 1))
-        dist.append(np.linalg.norm(resid[:,22:29]-kmeans4.cluster_centers_[0], ord=2, axis = 1))
-        dist = np.array(dist)
-        # dist_color = [1 if num >= 2 else 0 for num in dist]
-        
-        # pca_cord = pca.transform(resid) 
-        # pca_x.append(pca_cord[0,0])
-        # pca_y.append(pca_cord[0,1])
-        # clust_center = pca.transform(kmeans.cluster_centers_)
-        # theta = np.linspace(0, 2*np.pi, 100)
-        # radius = 2
-        # x_circle = clust_center[0, 0] + radius * np.cos(theta)
-        # y_circle = clust_center[0, 1] + radius * np.sin(theta)
-        
-        ind = np.linspace(1, 30, 30)
-        
+    df.loc[len_df] = {"sa_temp": sa_temp, "ma_temp": ma_temp}
+    if len_df>5:
         with placeholder.container():
-            col1, fig_col1, fig_col2 = st.columns(3)
-            with col1:
-                st.header("RTU Status")
-                for i in range(4):
-                    rtu = ['RTU 1', 'RTU 2', 'RTU 3', 'RTU 4']
-                    tol = [2,2,2,0.1]#[4.5,4,5,5]
-                    status_icon = "🔧" if dist[i,0] > tol[i] else "🔄"
-                    status = "Damper or Fan issue" if dist[i,0] > tol[i] else "Normal"
-                    status_markdown = f"**{rtu[i]} {status_icon}**\n\nSA Temp: {int(actual[0,1])}°C\nRA Temp: {int(actual[0,3])}°C\n\nStatus: {status}"
-                    st.markdown(status_markdown, unsafe_allow_html=True)
+            fig_col1, fig_col2 = st.columns(2)
             with fig_col1:
-                # st.markdown("### Fault")
-                st.header("Fault")
-                fig1 = go.Figure()
-                # fig1.add_trace(go.Scatter(x=ind, y=resid_list, mode='lines', name='Actual',line=dict(color='blue')))
-                # fig1 = px.scatter(x=pca_x, y=pca_y,color=dist_color,color_discrete_map={'1': 'red', '0': 'green'})
-                # fig1.add_trace(go.Scatter(x=x_circle, y=y_circle, mode='lines',line=dict(color='green')))
-                colors = ['red' if value > 0.1 else 'blue' for value in dist[:, 0]]
-                fig1 = fig1.add_trace(go.Bar(x=['RTU 1', 'RTU 2', 'RTU 3', 'RTU 4'],y=dist[:, 0],marker_color=colors,width=0.4))
-                fig1.update_layout(width=500,height=400 )
+                st.markdown("### Supply Air temperature")
+                fig1 = px.line(data_frame=df[len_df-5:len_df], y = 'sa_temp', x = df[len_df-5:len_df].index)
                 st.write(fig1)
             with fig_col2:
-                st.header("Mixed Air temperature")
-                fig2 = go.Figure()
-                fig2.add_trace(go.Scatter(x=ind, y=actual_list, mode='lines', name='Actual',line=dict(color='blue')))
-                fig2.add_trace(go.Scatter(x=ind, y=pred_list, mode='lines', name='Predicted',line=dict(color='red', dash='dot')))
-                fig2.update_layout(yaxis_range=[50, 80],width=500,height=400 )
+                st.markdown("### Mixed Air temperature")
+                fig2 = px.line(data_frame=df[len_df-5:len_df], y = 'ma_temp', x = df[len_df-5:len_df].index)
                 st.write(fig2)
-            
             st.markdown("### Detailed Data View")
             st.dataframe(df[len_df-5:len_df])
             # time.sleep(1)