energy_app.py

import streamlit as st
import pickle
import numpy as np

heating_model = pickle.load(open("models/xgboost_regression_model1.pkl","rb"))
cooling_model = pickle.load(open("models/model_cooling.pkl", "rb"))


#https://tarjomefa.com/wp-content/uploads/2017/04/6453-English-TarjomeFa.pdf
#st.image("image.jpg")

st.title("Predict the heating and cooling loads of your building!")

st.write("This app predicts the heating and cooling loads and helps you find the right heating and cooling system.")

X5 = st.selectbox("Number of floors", ["One", "Two"])
if X5 == "One":
    X2 = st.slider("Surface area", 686, 808, 720)
else:
    X2 = st.slider("Surface area", 514, 661, 540)
X7 = st.selectbox("Window size:", ["Small", "Medium", "Large"])
X6 = st.selectbox("Building Orientation:", ["North", "East", "South", "West"])

st.write("")
st.write("")
if st.button("Predict"):
    X1 = -0.00119112 * X2 + 1.5642495965572887
    X8 = 3  # Example default value for X8
    
    if X5 == "One" :
        X3 = X2 - 441
        X4 = 220.5
        X5 = 3.5
    else:
        X3 = 0.68571429 * X2 - 78
        X4 = 110.25
        X5 = 7

    if X6 == "North":
        X6 = 2
    elif X6 == "East":
        X6 = 3
    elif X6 == "South":
        X6 = 4
    else:
        X6 = 5

    if X7 == "Small":
        X7 = 0.1
    elif X7 == "Medium":
        X7 = 0.25
    else:
        X7 = 0.4

    hpred = heating_model.predict(np.array([[X1,X2,X3,X4,X5,X6,X7,X8]]).astype(np.float64))
    cpred = cooling_model.predict(np.array([[X1,X2,X3,X4,X5,X6,X7,X8]]).astype(np.float64))

    col1, col2 = st.columns(2)
    with col1:
        st.metric(label="Heating Load (kVA)", value=int(hpred[0]))
    with col2:
        st.metric(label="Cooling Load (kVA)", value=int(cpred[0]))

    import math
    # Constants
    HEATING_PUMP_CAPACITY = 14  # in kWh
    AC_CAPACITY = 9  # in kWh
    HEATING_PUMP_COST = 11000  # in €
    AC_COST = 6000  # in €

    # Predictions from the model
    # Example predictions, replace with actual predictions from your model
    heating_load = int(hpred[0]) 
    cooling_load = int(cpred[0])

    # Calculating the number of heating pumps and ACs needed
    num_heating_pumps = math.ceil(heating_load / HEATING_PUMP_CAPACITY)
    num_acs_for_heating = math.ceil(max(0, heating_load - num_heating_pumps * HEATING_PUMP_CAPACITY) / AC_CAPACITY)
    num_acs_for_cooling = math.ceil(cooling_load / AC_CAPACITY)

    # Total number of ACs needed is the max of ACs needed for heating and cooling
    total_acs_needed = max(num_acs_for_heating, num_acs_for_cooling)

    # Calculating total costs
    total_cost = (num_heating_pumps * HEATING_PUMP_COST) + (total_acs_needed * AC_COST)

    # Displaying the results
    
    st.write(f"Number of heating pumps needed: {num_heating_pumps}")
    st.write(f"Total number of additional ACs needed: {total_acs_needed}")
    st.write(f"Estimated investment needed for covering your cooling and heating given the characteristics of your house: {total_cost:.2f} €")

    # Checking if additional ACs are needed for cooling load
    if total_acs_needed > num_acs_for_heating:
        additional_acs_for_cooling = total_acs_needed - num_acs_for_heating
        st.write(f"Out of the total ACs, {additional_acs_for_cooling} are additionally needed to cover the cooling load.")
    else:
        st.write("No additional ACs are needed beyond those required for the heating load.")


    # Costs for energy
    cost_per_unit = 0.31  # Costs per kWh in Euro

    # Asumed consumption heating based on the usage per m2
    average_annual_consumption_h = 95 * X2  # Durchschnittlicher Jahresverbrauch in kWh

    # Annual costs for warming pump
    yearly_heating_bill = average_annual_consumption_h * cost_per_unit

    # Ergebnis anzeigen
    st.write(f"Estimaed anual costs for heating based on your surface area: {yearly_heating_bill:.2f} €")

        # Asumed consumption cooling based on the usage per m2
    average_annual_consumption_c = 48 * X2  # Durchschnittlicher Jahresverbrauch in kWh

    # Annual costs for AC
    yearly_cooling_bill = average_annual_consumption_c * cost_per_unit

    # Ergebnis anzeigen
    st.write(f"Estimaed anual costs for cooling based on your surface area: {yearly_cooling_bill:.2f} €")