Model/etl/customers/l_and_g/risk_matrix.py

from itertools import product
from recommendations.recommendation_utils import estimate_external_wall_area, estimate_windows

import numpy as np

import pandas as pd


def app():
    # Given a combination of variables, this code attempts to break down the costs of works to achieve upgrade
    # targets

    upgrade_path = [
        "wall_insulation", "roof_insulation", "ventilation", "windows", "low_energy_lighting",
        "heating", "solar"
    ]

    pricing_matrix = {
        "Cavity wall insulation": 14.5,
        "ventilation": 350,
        "Room Roof Insulation": 210,
        "Loft insulation": 15,
        "Internal wall insulation": 131,
        "External wall insulation": 298.35,
        "Solid wall insulation": 131,
        "LEDs": 35,  # per light
        "Flat Roof Insulation": 195,
        "Double Glazing": 1140,
        "secondary_glazing": 970,
        "5kw ASHP feeding heating & Hot water (dual tariff)": 14738,
        "11.2kw ASHP feeding heating & Hot water (dual tariff)": 16541,
        "3 kWp Solar PV": 4552.32,
        "4 kWp Solar PV": 4892.8,
        "4.3 kWp Solar PV": 4961.44,
        "4.8 kWp Solar PV": 5414,
        '5 kWp Solar PV': 5509.71,
        '5.5 kWp Solar PV': 5631.92,
        "HHRSH (dual tariff)": 1000,  # per heater
        "Suspended floor insulation": 75
    }
    dwelling_types = [
        "Semi Detached House",
        "Detached house",
        "Mid Terrace house",
        "Mid Floor Flat",
        "Top Floor Flat",
        "Ground Floor Flat"
    ]
    num_floors_map = {
        "Semi-detached house": 2,
        "Detached house": 2,
        "Mid Terrace house": 2,
        "Mid Floor Flat": 1,
        "Top Floor Flat": 1,
        "Ground Floor Flat": 1
    }
    built_form_map = {
        "Semi-detached house": "Semi-Detached",
        "Detached house": "Detached",
        "Mid Terrace house": "Mid Terrace",
        "Mid Floor Flat": "Semi-Detached",
        "Top Floor Flat": "Semi-Detached",
        "Ground Floor Flat": "Semi-Detached"
    }
    lighting_count = {
        "Semi-detached house": 15,
        "Detached house": 19,
        "Mid Terrace house": 12,
        "Mid Floor Flat": 10,
        "Top Floor Flat": 10,
        "Ground Floor Flat": 10
    }

    contingency = 0.26

    epr_data = pd.read_excel(
        "/Users/khalimconn-kowlessar/Documents/hestia/Customers/L&G/Risk Matrix/EPR Data V2.xlsx", header=1
    )
    epr_data["Measure added"].value_counts()
    epr_data["row_id"] = epr_data.index
    # We need to calculate the costs
    cost_data = []
    for _, row in epr_data.iterrows():

        epc = row["EPC"][0]
        sap = int(row["EPC"][1:])

        n_floors = num_floors_map[row["Property Type"]]
        bf = built_form_map[row["Property Type"]]
        pt = "House" if "flat" not in row["Property Type"].lower() else "Flat"
        # Model the home as a box
        ground_floor_area = row["area"] / n_floors
        perimeter = np.sqrt(ground_floor_area) * 4

        # This is the amount of insulation required
        external_wall_area = estimate_external_wall_area(
            num_floors=n_floors,
            floor_height=2.5,
            perimeter=perimeter,
            built_form=bf
        )

        n_rooms = np.floor(row["area"] / 15)

        n_windows = estimate_windows(
            property_type=pt,
            built_form=bf,
            construction_age_band="",
            floor_area=row["area"],
            number_habitable_rooms=n_rooms
        )
        cost_upper_bound = None
        if pd.isnull(row["Measure added"]):
            unit_cost = None
        else:
            measure = row["Measure added"]
            unit_cost = pricing_matrix[measure]

        if pd.isnull(row["Measure added"]):
            cost = None
        elif row["Measure added"] == "Loft insulation":
            cost = unit_cost * ground_floor_area
        elif row["Measure added"] in ["Cavity wall insulation", "Internal wall insulation"]:
            cost = unit_cost * external_wall_area + pricing_matrix["ventilation"] * 3
        elif row["Measure added"] == "Solid wall insulation":
            cost = unit_cost * external_wall_area + pricing_matrix["ventilation"] * 3
            cost_upper_bound = pricing_matrix["External wall insulation"] * external_wall_area + pricing_matrix[
                "ventilation"] * 3
        elif row["Measure added"] == "Double Glazing":
            cost = unit_cost * n_windows
        elif row["Measure added"] == "LEDs":
            cost = unit_cost * lighting_count[row["Property Type"]]
        elif row["Measure added"] in [
            '5kw ASHP feeding heating & Hot water (dual tariff)',
            '11.2kw ASHP feeding heating & Hot water (dual tariff)',
            "3 kWp Solar PV",
            '4 kWp Solar PV',
            "4.3 kWp Solar PV",
            '4.8 kWp Solar PV',
            '5 kWp Solar PV',
            '5.5 kWp Solar PV'
        ]:
            cost = unit_cost
        elif row["Measure added"] == "HHRSH (dual tariff)":
            cost = unit_cost * (n_rooms + 1)
        elif row["Measure added"] == "Suspended floor insulation":
            cost = unit_cost * ground_floor_area
        else:
            raise Exception()

        cost_data.append(
            {
                "row_id": row["row_id"],
                "epc": epc,
                "sap": sap,
                "cost": cost,
                "cost upper bound": cost_upper_bound
            }
        )

    cost_data = pd.DataFrame(cost_data)

    risk_matrix = pd.merge(
        epr_data,
        cost_data,
        on="row_id",
    )

    risk_matrix["contingency"] = risk_matrix["cost"] * contingency
    risk_matrix["upper bound coningency"] = risk_matrix["cost upper bound"] * contingency

    pricing_df = pd.DataFrame(
        [
            {
                "Measure": k,
                "Unit Cost": v
            }
            for k, v in pricing_matrix.items()
        ]
    )

    with pd.ExcelWriter(
        "/Users/khalimconn-kowlessar/Documents/hestia/Customers/L&G/Risk Matrix/risk_matrix V2.xlsx") as writer:
        risk_matrix.to_excel(writer, sheet_name="Risk Matrix", index=False)
        pricing_df.to_excel(writer, sheet_name="Pricing Assumptions", index=False)