from collections import deque from src.energy_prediction.EnergyPredictionModel import EnergyPredictionModel from src.energy_prediction.EnergyPredictionPipeline import EnergyPredictionPipeline from src.vav.VAVAnomalizer import VAVAnomalizer from src.vav.VAVPipeline import VAVPipeline import streamlit as st import pandas as pd import numpy as np import matplotlib.pyplot as plt import mqtt_client import time from src.rtu.RTUPipeline import RTUPipeline from src.rtu.RTUAnomalizer1 import RTUAnomalizer1 from src.rtu.RTUAnomalizer2 import RTUAnomalizer2 import plotly.express as px rtu_data_pipeline = RTUPipeline( scaler1_path="src/rtu/models/scaler_rtu_1_2.pkl", scaler2_path="src/rtu/models/scaler_rtu_3_4.pkl", ) rtu_anomalizers = [] rtu_anomalizers.append( RTUAnomalizer1( prediction_model_path="src/rtu/models/lstm_2rtu_smooth_04.keras", clustering_model_paths=[ "src/rtu/models/kmeans_rtu_1.pkl", "src/rtu/models/kmeans_rtu_2.pkl", ], pca_model_paths=[ "src/rtu/models/pca_rtu_1.pkl", "src/rtu/models/pca_rtu_2.pkl", ], num_inputs=rtu_data_pipeline.num_inputs, num_outputs=rtu_data_pipeline.num_outputs, ) ) rtu_anomalizers.append( RTUAnomalizer2( prediction_model_path="src/rtu/models/lstm_2rtu_smooth_03.keras", clustering_model_paths=[ "src/rtu/models/kmeans_rtu_3.pkl", "src/rtu/models/kmeans_rtu_4.pkl", ], pca_model_paths=[ "src/rtu/models/pca_rtu_3.pkl", "src/rtu/models/pca_rtu_4.pkl", ], num_inputs=rtu_data_pipeline.num_inputs, num_outputs=rtu_data_pipeline.num_outputs, ) ) vav_pipelines = [] vav_anomalizers = [] for i in range(1, 2): vav_pipelines.append( VAVPipeline(rtu_id=i, scaler_path=f"src/vav/models/scaler_vav_{i}.pkl") ) for i in range(1, 2): vav_anomalizers.append( VAVAnomalizer( rtu_id=i, prediction_model_path=f"src/vav/models/lstm_vav_0{i}.keras", clustering_model_path=f"src/vav/models/kmeans_vav_{i}.pkl", pca_model_path=f"src/vav/models/pca_vav_{i}.pkl", num_inputs=vav_pipelines[i - 1].num_inputs, num_outputs=vav_pipelines[i - 1].num_outputs, ) ) 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" # ) # Set the layout of the page to 'wide' st.set_page_config(layout="wide") # Energy data generating used in Energy Usage Over Time plot ---- REPLACE WITH ACTUAL DATA ---- def generate_energy_data(): times = pd.date_range("2021-01-01", periods=200, freq="1min") energy = np.random.randn(200).cumsum() return pd.DataFrame({"Time": times, "Energy": energy}) # Create three columns for the header header_row1_col1, header_row1_col2, header_row1_col3 = st.columns([0.8, 3, 1]) # Add logo to the first column of the header with header_row1_col1: st.image("logo.png") # Add title to the second column of the header with header_row1_col2: st.markdown( "

Building 59 - HVAC Dashboard

", unsafe_allow_html=True, ) # Add Time and Date to the third column of the header mqtt_client.start_mqtt_client() placeholder_header_time = header_row1_col3.empty() # Create three columns for the first row row1_col1, row1_col2, row1_col3 = st.columns([1.1, 1, 0.75]) # Use a container for RTU Status rtu_status_container = row1_col1.container() rtu_status_container.markdown( """

RTU Status

""", unsafe_allow_html=True, ) rtu_placeholders = [] rtu_columns = rtu_status_container.columns(4) # Initial placeholder, does not update with streaming for i in range(4): with rtu_columns[i]: placeholder = {"box": st.empty(), "sa_temp": st.empty(), "ra_temp": st.empty()} rtu_placeholders.append(placeholder) placeholder["box"].markdown( f"""

RTU{i+1}

""", unsafe_allow_html=True, ) placeholder["sa_temp"].markdown("**SA temp:** -- °F") placeholder["ra_temp"].markdown("**RA temp:** -- °F") # Temperatures streaming and updates 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: rtu_placeholders[i]["box"].markdown( f"""

RTU{i+1}

""", unsafe_allow_html=True, ) # Zones with row1_col2: st.markdown( """

Zones

""", unsafe_allow_html=True, ) tab1, tab2, tab3, tab4 = st.tabs(["RTU 1", "RTU 2", "RTU 3", "RTU 4"]) with tab1: zones_ = {36, 37, 38, 39, 40, 41, 42, 64, 65, 66, 67, 68, 69, 70} num_cols = 7 rows = 2 for i in range(rows): cols = st.columns(num_cols) if i == 0: for j in range(num_cols): zone_number = (i + 1) * (j + 1) + 35 if zone_number in zones_: button_html = f'' with cols[j]: st.markdown(button_html, unsafe_allow_html=True) else: with cols[j]: st.write("") else: for j in range(num_cols): zone_number = (i + 1) * 30 + j + 4 if zone_number in zones_: button_html = f'' with cols[j]: st.markdown(button_html, unsafe_allow_html=True) else: with cols[j]: st.write("") with tab2: zones_ = [ 19, 20, 27, 28, 29, 30, 31, 32, 33, 34, 35, 43, 44, 49, 50, 57, 58, 59, 60, 62, 63, 71, 72, ] zones_list = list(zones_) num_cols = 7 rows = 4 zones_list_rows = [ zones_list[i * num_cols : (i + 1) * num_cols] for i in range(rows) ] for row in zones_list_rows: cols = st.columns(num_cols) for col, zone_number in zip(cols, row): button_html = f'' with col: st.markdown(button_html, unsafe_allow_html=True) with tab3: zones_ = [18, 25, 26, 45, 48, 55, 56, 61] zones_list = sorted(zones_) num_cols = 7 rows = 2 zones_list_rows = [ zones_list[i * num_cols : (i + 1) * num_cols] for i in range(rows) ] for row in zones_list_rows: cols = st.columns(num_cols) for col, zone_number in zip(cols, row): button_html = f'' with col: st.markdown(button_html, unsafe_allow_html=True) with tab4: zones_ = [16, 17, 21, 22, 23, 24, 46, 47, 51, 52, 53, 54] zones_list = sorted(zones_) num_cols = 7 rows = 2 zones_list_rows = [ zones_list[i * num_cols : (i + 1) * num_cols] for i in range(rows) ] for row in zones_list_rows: cols = st.columns(num_cols) for col, zone_number in zip(cols, row): button_html = f'' with col: st.markdown(button_html, unsafe_allow_html=True) # Faults with row1_col3: fault_placeholder = {"heading": st.empty(), "dataframe": st.empty()} fault_placeholder["heading"].markdown( """

Fault Log

""", 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'] 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'] current_stat[i] = False fault_placeholder["dataframe"].dataframe(df_faults) # Details with st.container(): st.markdown( """

Details

""", unsafe_allow_html=True, ) # Create three columns row2_row1_col1, row2_row1_col2 = st.columns([0.9, 1.5]) # Floor Plan with row2_row1_col1: st.subheader("Floor Map") st.image("floor_plan.jpg", use_column_width=True) # Energy Comsumption Plots with row2_row1_col2: # Create two rows and two columns row2_row2_col1, row2_row2_col2 = st.columns(2) # cols = st.columns(2) with row2_row2_col1: st.subheader("Energy Usage - North Wing") north_wing_energy_container = st.empty() # with row2_row2_col2: st.subheader("Energy Usage - South Wing") south_wing_energy_container = st.empty() # Energy Comsumption Statistics with row2_row2_col2: st.subheader("Energy Usage Statistics") st.text( f"Average: 475 kWh\nHighest: 600 kWh" ) # ---- REPLACE WITH ACTUAL DATA ---- distances = [] def create_residual_plot(resid_pca_list, distance, rtu_id,lim=8): if rtu_id % 2 == 1: ax1 = 0 ax2 = 1 elif rtu_id % 2 == 0: ax1 = 2 ax2 = 3 fig = px.scatter( x=resid_pca_list[:, ax1], y=resid_pca_list[:, ax2], color=distance, labels={"x": "Time", "y": "Residual"}, width=500, height=500, color_discrete_sequence=px.colors.qualitative.Set2, ) fig.update_layout( xaxis_range=[-lim, lim], yaxis_range=[-lim, lim], xaxis=dict(showgrid=True, gridwidth=1, gridcolor="lightgray"), yaxis=dict(showgrid=True, gridwidth=1, gridcolor="lightgray"), margin=dict(l=20, r=20, t=20, b=20), hovermode="closest", showlegend=False, autosize=False, hoverlabel=dict(bgcolor="white", font_size=12), hoverlabel_align="left", hoverlabel_font_color="black", hoverlabel_bordercolor="lightgray", ) # fig.update_traces(marker=dict(size=5, color="blue")) return fig resid_placeholder = st.empty() resid_vav_placeholder = st.empty() while True: if mqtt_client.data_list: all_data = pd.concat([all_data, pd.DataFrame(mqtt_client.data_list)], axis=0) if len(all_data) > 10080: all_data = all_data.iloc[-10080:] df = pd.DataFrame(all_data) df_time = df["date"].iloc[-1] # Obtain the latest datetime of data with placeholder_header_time: placeholder_header_time.markdown( f"""

🕒 {df_time}

""", unsafe_allow_html=True, ) # Loop to update dist = None resid_pca_list_rtu = None resid_pca_list_rtu_2 = None resid_pca_list_vav_1 = None rtu_1_distance = None rtu_2_distance = None fault_1 = None fault_2 = None rtu_3_distance = None 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) ) vav_1_df_new, vav_1_df_trans = vav_pipelines[0].fit( pd.DataFrame(mqtt_client.data_list) ) vav_anomalizers[0].num_inputs = vav_pipelines[0].num_inputs vav_anomalizers[0].num_outputs = vav_pipelines[0].num_outputs if ( not df_new1 is None and not df_trans1 is None and not df_new2 is None and not df_trans2 is None ): ( actual_list, pred_list, resid_list, resid_pca_list_rtu, dist, rtu_1_distance, rtu_2_distance, fault_1, fault_2 ) = rtu_anomalizers[0].pipeline( df_new1, df_trans1, rtu_data_pipeline.scaler1 ) ( actual_list_2, pred_list_2, resid_list_2, resid_pca_list_rtu_2, dist_2, rtu_3_distance, rtu_4_distance, fault_3, fault_4 ) = rtu_anomalizers[1].pipeline( df_new1, df_trans1, rtu_data_pipeline.scaler1 ) if not vav_1_df_new is None: ( actual_list_vav_1, pred_list_vav_1, resid_list_vav_1, resid_pca_list_vav_1, dist_vav_1, ) = vav_anomalizers[0].pipeline( vav_1_df_new, vav_1_df_trans, vav_pipelines[0].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: 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) 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) 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) 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) st.plotly_chart(fig) if resid_pca_list_vav_1 is not None: # print(resid_pca_list_vav_1) 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 ) 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) mqtt_client.data_list.clear()