chore: Remove unused pickle files and refactor VAV module

dashboard.py

@@ -56,12 +56,12 @@ rtu_anomalizers.append(
 
 vav_pipelines = []
 vav_anomalizers = []
-for i in range(1, 2):
+for i in range(1, 5):
     vav_pipelines.append(
         VAVPipeline(rtu_id=i, scaler_path=f"src/vav/models/scaler_vav_{i}.pkl")
     )
 
-for i in range(1, 2):
+for i in range(1, 5):
     vav_anomalizers.append(
         VAVAnomalizer(
             rtu_id=i,
@@ -75,26 +75,26 @@ for i in range(1, 2):
 
 
 all_data = pd.read_csv("data/bootstrap_data.csv")
-df_faults = pd.DataFrame(columns = ["_______Time_______","__________Issue__________"])
-current_stat = [False,False,False,False]
-# energy_pipeline_north = EnergyPredictionPipeline(
-#     scaler_path="src/energy_prediction/models/scalerNorth.pkl",
-#     wing="north",
-#     bootstrap_data=all_data,
-# )
-# energy_pipeline_south = EnergyPredictionPipeline(
-#     scaler_path="src/energy_prediction/models/scalerSouth.pkl",
-#     wing="south",
-#     bootstrap_data=all_data,
-# )
-
-# energy_prediction_model_north = EnergyPredictionModel(
-#     model_path=r"src/energy_prediction/models/lstm_energy_north_01.keras"
-# )
-
-# energy_prediction_model_south = EnergyPredictionModel(
-#     model_path=r"src/energy_prediction/models/lstm_energy_south_01.keras"
-# )
+df_faults = pd.DataFrame(columns=["_______Time_______", "__________Issue__________"])
+current_stat = [False, False, False, False]
+energy_pipeline_north = EnergyPredictionPipeline(
+    scaler_path="src/energy_prediction/models/scalerNorth.pkl",
+    wing="north",
+    bootstrap_data=all_data,
+)
+energy_pipeline_south = EnergyPredictionPipeline(
+    scaler_path="src/energy_prediction/models/scalerSouth.pkl",
+    wing="south",
+    bootstrap_data=all_data,
+)
+
+energy_prediction_model_north = EnergyPredictionModel(
+    model_path=r"src/energy_prediction/models/lstm_energy_north_01.keras"
+)
+
+energy_prediction_model_south = EnergyPredictionModel(
+    model_path=r"src/energy_prediction/models/lstm_energy_south_01.keras"
+)
 
 
 # Set the layout of the page to 'wide'
@@ -161,13 +161,13 @@ for i in range(4):
 
 
 # Temperatures streaming and updates
-def update_status_boxes(df,fault):
+def update_status_boxes(df, fault):
     for i in range(4):
         sa_temp = df[f"rtu_00{i+1}_sa_temp"].iloc[-1]
         ra_temp = df[f"rtu_00{i+1}_ra_temp"].iloc[-1]
         rtu_placeholders[i]["sa_temp"].markdown(f"**SA temp:**  {sa_temp} °F")
         rtu_placeholders[i]["ra_temp"].markdown(f"**RA temp:**  {ra_temp} °F")
-        if fault[i]== 1:
+        if fault[i] == 1:
             rtu_placeholders[i]["box"].markdown(
                 f"""
             <div style='background-color:#ff4d4d;padding:3px;border-radius:5px;margin-bottom:10px'>
@@ -176,8 +176,6 @@ def update_status_boxes(df,fault):
             """,
                 unsafe_allow_html=True,
             )
-       
-
 
 
 # Zones
@@ -304,19 +302,24 @@ with row1_col3:
     </div>""",
         unsafe_allow_html=True,
     )
-    
+
     fault_placeholder["dataframe"].dataframe(df_faults)
-    
-def fault_table_update(fault,df_faults,current_stat,df_time):
-    if fault[i]== 1 and current_stat[i] == False:
-        df_faults.loc[len(df_faults)] = [df_time,f'RTU_0{i+1}_fan/damper_fault - Start']
+
+
+def fault_table_update(fault, df_faults, current_stat, df_time):
+    if fault[i] == 1 and current_stat[i] == False:
+        df_faults.loc[len(df_faults)] = [
+            df_time,
+            f"RTU_0{i+1}_fan/damper_fault - Start",
+        ]
         current_stat[i] = True
-        
-    if fault[i]== 0 and current_stat[i] == True:
-        df_faults.loc[len(df_faults)] = [df_time,f'RTU_0{i+1}_fan/damper_fault - End']
+
+    if fault[i] == 0 and current_stat[i] == True:
+        df_faults.loc[len(df_faults)] = [df_time, f"RTU_0{i+1}_fan/damper_fault - End"]
         current_stat[i] = False
     fault_placeholder["dataframe"].dataframe(df_faults)
 
+
 # Details
 with st.container():
     st.markdown(
@@ -361,7 +364,7 @@ with st.container():
 distances = []
 
 
-def create_residual_plot(resid_pca_list, distance, rtu_id,lim=8):
+def create_residual_plot(resid_pca_list, distance, rtu_id, lim=8):
     if rtu_id % 2 == 1:
         ax1 = 0
         ax2 = 1
@@ -401,7 +404,6 @@ resid_placeholder = st.empty()
 resid_vav_placeholder = st.empty()
 
 
-
 while True:
 
     if mqtt_client.data_list:
@@ -422,12 +424,12 @@ while True:
             )
 
         # Loop to update
-        
 
         dist = None
         resid_pca_list_rtu = None
         resid_pca_list_rtu_2 = None
         resid_pca_list_vav_1 = None
+        resid_pca_list_vav_2 = None
         rtu_1_distance = None
         rtu_2_distance = None
         fault_1 = None
@@ -436,7 +438,6 @@ while True:
         rtu_4_distance = None
         fault_3 = None
         fault_4 = None
-        
 
         df_new1, df_trans1, df_new2, df_trans2 = rtu_data_pipeline.fit(
             pd.DataFrame(mqtt_client.data_list)
@@ -448,6 +449,27 @@ while True:
         vav_anomalizers[0].num_inputs = vav_pipelines[0].num_inputs
         vav_anomalizers[0].num_outputs = vav_pipelines[0].num_outputs
 
+        vav_2_df_new, vav_2_df_trans = vav_pipelines[1].fit(
+            pd.DataFrame(mqtt_client.data_list)
+        )
+        vav_anomalizers[1].num_inputs = vav_pipelines[1].num_inputs
+        vav_anomalizers[1].num_outputs = vav_pipelines[1].num_outputs
+
+        vav_3_df_new, vav_3_df_trans = vav_pipelines[2].fit(
+            pd.DataFrame(mqtt_client.data_list)
+        )
+        vav_anomalizers[2].num_inputs = vav_pipelines[2].num_inputs
+        vav_anomalizers[2].num_outputs = vav_pipelines[2].num_outputs
+
+        vav_4_df_new, vav_4_df_trans = vav_pipelines[3].fit(
+            pd.DataFrame(mqtt_client.data_list)
+        )
+        vav_anomalizers[3].num_inputs = vav_pipelines[3].num_inputs
+        vav_anomalizers[3].num_outputs = vav_pipelines[3].num_outputs
+
+        energy_df_north = energy_pipeline_north.fit(all_data)
+        energy_df_south = energy_pipeline_south.fit(all_data)
+
         if (
             not df_new1 is None
             and not df_trans1 is None
@@ -463,7 +485,7 @@ while True:
                 rtu_1_distance,
                 rtu_2_distance,
                 fault_1,
-                fault_2
+                fault_2,
             ) = rtu_anomalizers[0].pipeline(
                 df_new1, df_trans1, rtu_data_pipeline.scaler1
             )
@@ -476,7 +498,7 @@ while True:
                 rtu_3_distance,
                 rtu_4_distance,
                 fault_3,
-                fault_4
+                fault_4,
             ) = rtu_anomalizers[1].pipeline(
                 df_new1, df_trans1, rtu_data_pipeline.scaler1
             )
@@ -491,58 +513,154 @@ while True:
                 vav_1_df_new, vav_1_df_trans, vav_pipelines[0].scaler
             )
 
+        if not vav_2_df_new is None:
+            (
+                actual_list_vav_2,
+                pred_list_vav_2,
+                resid_list_vav_2,
+                resid_pca_list_vav_2,
+                dist_vav_2,
+            ) = vav_anomalizers[1].pipeline(
+                vav_2_df_new, vav_2_df_trans, vav_pipelines[1].scaler
+            )
+
+        if not vav_3_df_new is None:
+            (
+                actual_list_vav_3,
+                pred_list_vav_3,
+                resid_list_vav_3,
+                resid_pca_list_vav_3,
+                dist_vav_3,
+            ) = vav_anomalizers[2].pipeline(
+                vav_3_df_new, vav_3_df_trans, vav_pipelines[2].scaler
+            )
+
+        if not vav_4_df_new is None:
+            (
+                actual_list_vav_4,
+                pred_list_vav_4,
+                resid_list_vav_4,
+                resid_pca_list_vav_4,
+                dist_vav_4,
+            ) = vav_anomalizers[3].pipeline(
+                vav_4_df_new, vav_4_df_trans, vav_pipelines[3].scaler
+            )
+
         if resid_pca_list_rtu is not None:
             resid_pca_list_rtu = np.array(resid_pca_list_rtu)
             resid_pca_list_rtu_2 = np.array(resid_pca_list_rtu_2)
 
-        if resid_pca_list_rtu is not None:
+        if resid_pca_list_rtu is not None:  # Plot RTU residuals
             with resid_placeholder.container():
                 resid_rtu1_placeholder, resid_rtu2_placeholder = st.columns(2)
                 with resid_rtu1_placeholder:
                     st.subheader("RTU 1 Residuals")
-                    fig = create_residual_plot(resid_pca_list_rtu, rtu_1_distance, rtu_id=1)
+                    fig = create_residual_plot(
+                        resid_pca_list_rtu, rtu_1_distance, rtu_id=1
+                    )
                     st.plotly_chart(fig)
 
                 with resid_rtu2_placeholder:
                     st.subheader("RTU 2 Residuals")
-                    fig = create_residual_plot(resid_pca_list_rtu, rtu_2_distance, rtu_id=2)
+                    fig = create_residual_plot(
+                        resid_pca_list_rtu, rtu_2_distance, rtu_id=2
+                    )
                     st.plotly_chart(fig)
 
                 resid_rtu3_placeholder, resid_rtu4_placeholder = st.columns(2)
                 with resid_rtu3_placeholder:
                     st.subheader("RTU 3 Residuals")
-                    fig = create_residual_plot(resid_pca_list_rtu, rtu_3_distance, rtu_id=3)
+                    fig = create_residual_plot(
+                        resid_pca_list_rtu, rtu_3_distance, rtu_id=3
+                    )
                     st.plotly_chart(fig)
 
                 with resid_rtu4_placeholder:
                     st.subheader("RTU 4 Residuals")
-                    fig = create_residual_plot(resid_pca_list_rtu, rtu_4_distance, rtu_id=4)
+                    fig = create_residual_plot(
+                        resid_pca_list_rtu, rtu_4_distance, rtu_id=4
+                    )
                     st.plotly_chart(fig)
 
-        if resid_pca_list_vav_1 is not None:
-            # print(resid_pca_list_vav_1)
+        if resid_pca_list_vav_1 is not None:  # Plot VAV residuals
+
             with resid_vav_placeholder.container():
-                st.subheader("VAV 1 Residuals")
-                fig = create_residual_plot(
-                    np.array(resid_pca_list_vav_1),rtu_4_distance, rtu_id=1, lim=15
+                resid_rtu_1_vav_placeholder, resid_rtu_2_vav_placeholder = st.columns(2)
+
+                with resid_rtu_1_vav_placeholder:
+                    st.subheader("VAV 1 Residuals")
+                    fig = create_residual_plot(
+                        np.array(resid_pca_list_vav_1), rtu_4_distance, rtu_id=1, lim=15
+                    )
+                    st.plotly_chart(fig)
+
+                with resid_rtu_2_vav_placeholder:
+                    st.subheader("VAV 2 Residuals")
+                    fig = create_residual_plot(
+                        np.array(resid_pca_list_vav_2), rtu_4_distance, rtu_id=1, lim=15
+                    )
+                    st.plotly_chart(fig)
+
+                resid_rtu_3_vav_placeholder, resid_rtu_4_vav_placeholder = st.columns(2)
+                with resid_rtu_3_vav_placeholder:
+                    st.subheader("VAV 3 Residuals")
+                    fig = create_residual_plot(
+                        np.array(resid_pca_list_vav_3), rtu_4_distance, rtu_id=1, lim=15
+                    )
+                    st.plotly_chart(fig)
+
+                with resid_rtu_4_vav_placeholder:
+                    st.subheader("VAV 4 Residuals")
+                    fig = create_residual_plot(
+                        np.array(resid_pca_list_vav_4), rtu_4_distance, rtu_id=1, lim=15
+                    )
+                    st.plotly_chart(fig)
+
+        current_time = pd.to_datetime(df_time)
+
+        if energy_df_north is not None:
+
+            energy_prediction_north = energy_prediction_model_north.pipeline(
+                energy_df_north, energy_pipeline_north.scaler
+            ).flatten()
+
+            x_time = pd.date_range(
+                current_time, periods=len(energy_prediction_north), freq="1h"
+            )
+
+            with north_wing_energy_container:
+
+                fig = px.line(
+                    x=x_time,
+                    y=energy_prediction_north,
+                    labels={"x": "Time", "y": "Energy (kWh)"},
+                    height=200,
                 )
+
                 st.plotly_chart(fig)
 
-        # with north_wing_energy_container:
-        #     df_energy = generate_energy_data()  # ---- REPLACE WITH ACTUAL DATA ----
-        #     fig, ax = plt.subplots(figsize=(5, 1.5))
-        #     ax.plot(df_energy["Time"], df_energy["Energy"])
-        #     ax.set_xlabel("Time")
-        #     ax.set_ylabel("Energy (kWh)")
-        #     st.pyplot(fig)
-
-        # with south_wing_energy_container:
-        #     df_energy = generate_energy_data()  # ---- REPLACE WITH ACTUAL DATA ----
-        #     fig, ax = plt.subplots(figsize=(5, 1.5))
-        #     ax.plot(df_energy["Time"], df_energy["Energy"])
-        #     ax.set_xlabel("Time")
-        #     ax.set_ylabel("Energy (kWh)")
-        #     st.pyplot(fig)
-        update_status_boxes(df,[fault_1,fault_2,fault_3,fault_4])
-        fault_table_update([fault_1,fault_2,fault_3,fault_4],df_faults,current_stat,df_time)
+        if energy_df_south is not None:
+            energy_prediction_south = energy_prediction_model_south.pipeline(
+                energy_df_south, energy_pipeline_south.scaler
+            ).flatten()
+
+            x_time = pd.date_range(
+                current_time, periods=len(energy_prediction_south), freq="1h"
+            )
+
+            with south_wing_energy_container:
+
+                fig = px.line(
+                    x=x_time,
+                    y=energy_prediction_south,
+                    labels={"x": "Time", "y": "Energy (kWh)"},
+                    height=200,
+                )
+
+                st.plotly_chart(fig)
+
+        update_status_boxes(df, [fault_1, fault_2, fault_3, fault_4])
+        fault_table_update(
+            [fault_1, fault_2, fault_3, fault_4], df_faults, current_stat, df_time
+        )
         mqtt_client.data_list.clear()

physLSTM/kmeans_vav_2.pkl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:eac01ceecdae11713ee21462a8bd3dc7ea32e740c3daa42795b266a05e7c424a
-size 1567961

pipeline.ipynb

@@ -1,132 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "def initialize_lists(size=30):\n",
-    "    initial_values = [0] * size\n",
-    "    return initial_values.copy(), initial_values.copy(), initial_values.copy()\n",
-    "\n",
-    "def get_window(df, len_df):\n",
-    "    if len_df > 30:\n",
-    "        return df[len_df-31:len_df].astype('float32')\n",
-    "    else:\n",
-    "        return None\n",
-    "\n",
-    "def transform_window(df_window, scaler):\n",
-    "    return scaler.transform(df_window)\n",
-    "\n",
-    "def prepare_input(df_trans):\n",
-    "    return df_trans[:30,:].reshape((1,30,30))\n",
-    "\n",
-    "def predict(model, df_new):\n",
-    "    return model.predict(df_new)\n",
-    "\n",
-    "def calculate_residuals(df_trans, pred):\n",
-    "    actual = df_trans[30,:25]\n",
-    "    resid = actual - pred\n",
-    "    return actual, resid\n",
-    "\n",
-    "def resize_prediction(pred, df_trans):\n",
-    "    pred.resize((pred.shape[0], pred.shape[1] + len(df_trans[30,25:])))\n",
-    "    pred[:, -len(df_trans[30,25:]):] = df_trans[30,25:]\n",
-    "    return pred\n",
-    "\n",
-    "def inverse_transform(scaler, pred, df_trans):\n",
-    "    pred = scaler.inverse_transform(np.array(pred))\n",
-    "    actual = scaler.inverse_transform(np.array([df_trans[30,:]]))\n",
-    "    return actual, pred\n",
-    "\n",
-    "def update_lists(actual_list, pred_list, resid_list, actual, pred, resid):\n",
-    "    actual_list.pop(0)\n",
-    "    pred_list.pop(0)\n",
-    "    resid_list.pop(0)\n",
-    "    actual_list.append(actual[0,1])\n",
-    "    pred_list.append(pred[0,1])\n",
-    "    resid_list.append(resid[0,1])\n",
-    "    return actual_list, pred_list, resid_list\n",
-    "\n",
-    "def calculate_distances(resid, kmeans1, kmeans2, kmeans3, kmeans4):\n",
-    "    dist = []\n",
-    "    dist.append(np.linalg.norm(resid[:,1:8]-kmeans1.cluster_centers_[0], ord=2, axis=1))\n",
-    "    dist.append(np.linalg.norm(resid[:,8:15]-kmeans2.cluster_centers_[0], ord=2, axis=1))\n",
-    "    dist.append(np.linalg.norm(resid[:,15:22]-kmeans3.cluster_centers_[0], ord=2, axis=1))\n",
-    "    dist.append(np.linalg.norm(resid[:,22:29]-kmeans4.cluster_centers_[0], ord=2, axis=1))\n",
-    "    return np.array(dist)\n",
-    "\n",
-    "def pipeline(df, scaler, model, kmeans1, kmeans2, kmeans3, kmeans4):\n",
-    "    actual_list, pred_list, resid_list = initialize_lists()\n",
-    "    len_df = np.len(df)\n",
-    "    df_window = get_window(df, len_df)\n",
-    "    if df_window is not None:\n",
-    "        df_trans = transform_window(df_window, scaler)\n",
-    "        df_new = prepare_input(df_trans)\n",
-    "        pred = predict(model, df_new)\n",
-    "        actual, resid = calculate_residuals(df_trans, pred)\n",
-    "        pred = resize_prediction(pred, df_trans)\n",
-    "        actual, pred = inverse_transform(scaler, pred, df_trans)\n",
-    "        actual_list, pred_list, resid_list = update_lists(actual_list, pred_list, resid_list, actual, pred, resid)\n",
-    "        dist = calculate_distances(resid, kmeans1, kmeans2, kmeans3, kmeans4)\n",
-    "        return actual_list, pred_list, resid_list, dist\n",
-    "    else:\n",
-    "        return actual_list, pred_list, resid_list, None\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['a', 'b', 'c', 'd']"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inp = ['a', 'b']\n",
-    "out = ['c','d']\n",
-    "\n",
-    "inp+out"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "smartbuilding",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

src/energy_prediction/EnergyPredictionPipeline.py

@@ -50,12 +50,12 @@ class EnergyPredictionPipeline:
 
         return df
 
-    def prepare_input(self, df1):
+    def prepare_input(self, df1: pd.DataFrame):
 
         df = df1.copy()
         df["date"] = pd.to_datetime(df["date"])
         df.set_index("date", inplace=True)
-        df = df.resample("60T").mean()
+        df = df.resample("60min").mean()
         df = self.date_encoder(df)
         df.reset_index(inplace=True, drop=True)
         df = df.astype("float32")
@@ -69,7 +69,7 @@ class EnergyPredictionPipeline:
 
         return self.df
 
-    def get_window(self, df):
+    def get_window(self, df: pd.DataFrame):
 
         time = df["date"].iloc[-1]
         time = datetime.strptime(time, "%Y-%m-%d %H:%M:%S")
@@ -79,8 +79,8 @@ class EnergyPredictionPipeline:
         else:
             return None
 
-    def fit(self, message):
-        df_new = self.extract_data_from_message(message)
+    def fit(self, df: pd.DataFrame):
+        df_new = self.extract_data_from_message(df)
         df_window = self.get_window(df_new)
         if df_window is not None:
             df = self.prepare_input(df_window)

src/vav/VAVPipeline.py

@@ -39,8 +39,34 @@ class VAVPipeline:
         if rtu_id == 1:
             self.zones = [69, 68, 67, 66, 65, 64, 42, 41, 40, 39, 38, 37, 36]
         if rtu_id == 2:
-            self.zones = [72, 71, 63, 62, 60, 59, 58,57, 50, 49, 44, 43, 35, 34, 33, 32, 31, 30, 29, 28]
-
+            self.zones = [
+                72,
+                71,
+                63,
+                62,
+                60,
+                59,
+                58,
+                57,
+                50,
+                49,
+                44,
+                43,
+                35,
+                34,
+                33,
+                32,
+                31,
+                30,
+                29,
+                28,
+            ]
+
+        if rtu_id == 3:
+            self.zones = [61, 56, 55, 48, 45, 26, 25, 18]
+
+        if rtu_id == 4:
+            self.zones = [16, 17, 21, 23, 24, 46, 47, 51, 52, 53, 54]
         self.output_col_names = []
         self.input_col_names = [
             f"rtu_00{rtu_id}_fltrd_sa_flow_tn",

src/vav/models/kmeans_vav_2.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:874aa63843989880be0b133e3de125c60ec4290146e152685a0ba09faf101f71
+oid sha256:ee2cdc974b315bbd708a0ca2faa084feedd284794e4a53412013dc29cd62f903
 size 1567961

src/vav/models/kmeans_vav_3.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:086d45b9d2c98baaea5b0588cd6d228d84eb141b707fe845b11316b3ddc58774
-size 1568153
+oid sha256:ffd45990b3c8a63580195cc71b1c33b39fff6003e855430fe0ddde4b42225c9a
+size 1567961

src/vav/models/kmeans_vav_4.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:086d45b9d2c98baaea5b0588cd6d228d84eb141b707fe845b11316b3ddc58774
-size 1568153
+oid sha256:51cc457df6f45e89c345e8ba79461dbe6ce4f4d975c0b42d18d400582d1be3fc
+size 1567961

src/vav/models/pca_vav_2.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:bf25a096656f207f218cc87347f126c2cae81a0bbe45fea6a8c144922dc6eeab
+oid sha256:e33e866c070ca03333683658d69a00ded03ac7bda2704f8b95d59f82c1478441
 size 1371

physLSTM/pca_vav_2.pkl → src/vav/models/pca_vav_3.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:00484ce0c76fc9df1f8f119325a12ec7be5baf8879d4ac192448b2d7ba397c7e
-size 1323
+oid sha256:5c1e89a6b52ba885943048e6c32b2c70263b6ca3709618b623f0cafa65955bad
+size 1195

physLSTM/scaler_vav_1.pkl → src/vav/models/pca_vav_4.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:293ee3c9082e7104dfc96425cecad2a44e5914bbd1f43c25a0fd8c36507b103a
-size 1925
+oid sha256:f6926a53d803f3d2621cbe29f06dbc1f75c7a4ee74738b7ef31550bfd4874657
+size 1275

src/vav/models/scaler_vav_2.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:12522c4935877d8dedeb1fe60b577a93c64d864ff6292960cf5fcd85ffc342e1
-size 1925
+oid sha256:562a4d725a8a1c502745ec6f801487fea985b1d4e984030537ad60757c25d348
+size 1973

src/vav/models/scaler_vav_3.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:293ee3c9082e7104dfc96425cecad2a44e5914bbd1f43c25a0fd8c36507b103a
-size 1925
+oid sha256:558ca56b2cb9fd886ab81d19517430abdc573db9607a375f5d86de69130187b7
+size 1493

src/vav/models/scaler_vav_4.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:293ee3c9082e7104dfc96425cecad2a44e5914bbd1f43c25a0fd8c36507b103a
-size 1925
+oid sha256:23f2e858e596e90833da43752d7111a8ea85c01a0c32ace1af86cf3a0d4c4997
+size 1781

streamlit.py

@@ -1,260 +0,0 @@
-import streamlit as st # web development
-import numpy as np # np mean, np random 
-import pandas as pd # read csv, df manipulation
-import time # to simulate a real time data, time loop 
-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(r"src\rtu\models\lstm_4rtu_smooth_02.keras")
-scaler = joblib.load(r'src\rtu\models\scaler_1.pkl')
-# kmeans = joblib.load('kmeans_model.pkl')
-kmeans1 = joblib.load(r'src\rtu\models\kmeans_model1.pkl')
-kmeans2 = joblib.load(r'src\rtu\models\kmeans_model2.pkl')
-kmeans3 = joblib.load(r'src\rtu\models\kmeans_model3.pkl')
-kmeans4 = joblib.load(r'src\rtu\models\kmeans_model4.pkl')
-# pca = joblib.load('pca_model.pkl')
-
-st.set_page_config(
-    page_title = 'Real-Time Data Buliding 59',
-    page_icon = '✅',
-    layout = 'wide'
-)
-
-st.title("Buliding 59 Dashboard")
-placeholder = st.empty()
-
-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]
-
-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']
-    
-    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)
-        
-        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)
-            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.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.write(fig2)
-            
-            st.markdown("### Detailed Data View")
-            st.dataframe(df[len_df-5:len_df])
-            # time.sleep(1)
- 
-
-
-client = mqtt.Client(mqtt.CallbackAPIVersion.VERSION1)
-client.on_message = on_message
-client.connect(broker_address, broker_port)
-client.subscribe(topic)
-client.loop_forever()
-
-
-
-
-
-
-
-
-
-