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Added U-value and perimeter estimation functions
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Khalim Conn-Kowlessar
parent
d40d20497f
commit
a565a35e9a
4 changed files with 280 additions and 6 deletions
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@ -15,6 +15,7 @@ class UvalueEstimations:
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self.walls_decile_data = {}
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self.roofs = None
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self.floors = None
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self.floors_decile_data = {}
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def get_estimates(self, cleaner: EpcClean):
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"""
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@ -150,6 +151,10 @@ class UvalueEstimations:
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]
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self.floors = u_value_summary
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self.floors_decile_data = {
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"decile_labels": decile_labels,
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"decile_boundaries": decile_boundaries
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}
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@staticmethod
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def classify_into_deciles(df: pd.DataFrame, column: str) -> (pd.DataFrame, list, list):
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@ -128,6 +128,13 @@ def handler():
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floors_df["address1"].values[2]
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floors_df["original_description"].values[2]
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df = pd.DataFrame(
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[
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x.data for x in input_properties
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]
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)
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df["property-type"].unique()
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from model_data.recommendations.FloorRecommendations import FloorRecommendations
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self = FloorRecommendations(property_instance=input_properties[2], uvalue_estimates=uvalue_estimates)
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123
model_data/rdsap_tables.py
Normal file
123
model_data/rdsap_tables.py
Normal file
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@ -0,0 +1,123 @@
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"""
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This script contains standard tables which are defined in rdsap. The most recent version of sap/rdsap is
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based on the 2012 version, however the government is currently working on releasing a new version, and there
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we will need to re-visit this
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"""
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age_band_data = [
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{
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"age_band": "A",
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"England_Wales": "before 1900",
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"Scotland": "before 1919",
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"Northern_Ireland": "before 1919",
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"Park_home_UK": None
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},
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{
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"age_band": "B",
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"England_Wales": "1900-1929",
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"Scotland": "1919-1929",
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"Northern_Ireland": "1919-1929",
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"Park_home_UK": None
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},
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{
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"age_band": "C",
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"England_Wales": "1930-1949",
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"Scotland": "1930-1949",
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"Northern_Ireland": "1930-1949",
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"Park_home_UK": None
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},
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{
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"age_band": "D",
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"England_Wales": "1950-1966",
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"Scotland": "1950-1964",
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"Northern_Ireland": "1950-1973",
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"Park_home_UK": None
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},
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{
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"age_band": "E",
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"England_Wales": "1967-1975",
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"Scotland": "1965-1975",
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"Northern_Ireland": "1974-1977",
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"Park_home_UK": None
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},
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{
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"age_band": "F",
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"England_Wales": "1976-1982",
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"Scotland": "1976-1983",
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"Northern_Ireland": "1978-1985",
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"Park_home_UK": "before 1983"
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},
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{
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"age_band": "G",
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"England_Wales": "1983-1990",
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"Scotland": "1984-1991",
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"Northern_Ireland": "1986-1991",
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"Park_home_UK": "1983-1995"
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},
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{
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"age_band": "H",
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"England_Wales": "1991-1995",
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"Scotland": "1992-1998",
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"Northern_Ireland": "1992-1999",
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"Park_home_UK": None
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},
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{
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"age_band": "I",
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"England_Wales": "1996-2002",
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"Scotland": "1999-2002",
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"Northern_Ireland": "2000-2006",
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"Park_home_UK": "1996-2005"
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},
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{
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"age_band": "J",
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"England_Wales": "2003-2006",
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"Scotland": "2003-2007",
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"Northern_Ireland": None,
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"Park_home_UK": None
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},
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{
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"age_band": "K",
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"England_Wales": "2007-2011",
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"Scotland": "2008-2011",
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"Northern_Ireland": "2007-2013",
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"Park_home_UK": "2006 onwards"
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},
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{
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"age_band": "L",
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"England_Wales": "2012 onwards",
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"Scotland": "2012 onwards",
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"Northern_Ireland": "2014 onwards",
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"Park_home_UK": None
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},
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]
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default_wall_thickness = [
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{
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"type": "stone", "A": 500, "B": 500, "C": 500, "D": 500, "E": 450, "F": 420, "G": 420, "H": 420,
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"I": 450, "J_K_L": 450
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},
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{
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"type": "solid brick", "A": 220, "B": 220, "C": 220, "D": 220, "E": 240, "F": 250, "G": 270, "H": 270,
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"I": 300, "J_K_L": 300
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},
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{
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"type": "cavity", "A": 250, "B": 250, "C": 250, "D": 250, "E": 250, "F": 260, "G": 270, "H": 270,
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"I": 300, "J_K_L": 300
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},
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{
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"type": "timber frame", "A": 150, "B": 150, "C": 150, "D": 250, "E": 270, "F": 270, "G": 270, "H": 270,
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"I": 300, "J_K_L": 300
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},
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{
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"type": "cob", "A": 540, "B": 540, "C": 540, "D": 540, "E": 540, "F": 540, "G": 560, "H": 560, "I": 590,
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"J_K_L": 590
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},
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{
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"type": "system build", "A": 250, "B": 250, "C": 250, "D": 250, "E": 250, "F": 300, "G": 300, "H": 300,
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"I": 300, "J_K_L": 300
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},
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{
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"type": "park home", "A": None, "B": None, "C": None, "D": None, "E": None, "F": 50, "G": None,
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"H": None, "I": 50, "J_K_L": 100
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},
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]
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@ -1,6 +1,8 @@
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import math
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from model_data.BaseUtility import BaseUtility
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from model_data.Property import Property
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from model_data.analysis.UvalueEstimations import UvalueEstimations
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from model_data.rdsap_tables import default_wall_thickness, age_band_data
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class FloorRecommendations(BaseUtility):
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@ -11,6 +13,10 @@ class FloorRecommendations(BaseUtility):
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# diminishing returns. This value should be verified with Osmosis (TODO)
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DIMINISHING_RETURNS_U_VALUE = 0.2
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REGION_LOOKUP = {
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"England and Wales": "England_Wales",
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}
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def __init__(self, property_instance: Property, uvalue_estimates: UvalueEstimations):
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self.property = property_instance
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self.uvalue_estimates = uvalue_estimates
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@ -20,9 +26,85 @@ class FloorRecommendations(BaseUtility):
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# Will contains a list of recommended measures
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self.recommendations = []
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@staticmethod
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def _estimate_perimeter(floor_area, num_rooms):
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# Compute average room size based on total floor area and number of rooms
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avg_room_size = floor_area / num_rooms
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# Estimate total side length for square layout
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total_side_length = math.sqrt(avg_room_size * num_rooms)
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# Compute the perimeter
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perimeter = total_side_length * 4
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return perimeter
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def _estimate_suspended_floor_u_value(
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self, floor_area, number_of_rooms, insulation_thickness, wall_type, region, age_band
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):
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"""
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Estimate the u-value of a suspended floor, based on RdSap methodology
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Default U-value for UNINSULATED suspended floor, based on RdSAP methodology
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https://files.bregroup.com/bre-co-uk-file-library-copy/filelibrary/SAP/2012/RdSAP-9.93/RdSAP_2012_9.93.pdf
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w = wall thickness, where these estimates are based on the RD SAP methodology, as in table S3
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A = floor area
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Exposed perimeter = P
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soil type clas thermal conductivity lambda_g = 1.5 W/mK
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Rsi = 0.17m^2K/W
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Rse = 0.04m^2K/W
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Rf = 0.001 * d_ins / 0.035 where d_ins is the insulation thickness in mm
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height above external ground h = 0.3m
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average wind speed at 10m height v=5m/s
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wind sheilding factor fw = 0.05
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vantilation factor E = 0.003 m^2/m
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U-value of walls to underfloor space Uw = 1.5 W/m^2K
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# Calulations for suspended ground floors, example for 5 bedroom house with permiter estimated at
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44.36214602563767
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1) dg = w + lambda_g x (Rsi + Rse) = 0.5 + 1.5 * (0.17 + 0.04) = 0.615
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2) B = 2 * A/P = 2 * 123.0 / 44.36214602563767 = 5.545268253204708
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3) Ug = 2 * lambda_g * log(pi * B/dg + 1)/(pi * B + dg) =
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2 * 1.5 * log(3.141592653589793 * 5.545268253204708/0.615 + 1) / (3.141592653589793 * 5.545268253204708
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+ 0.615) = 0.5619604457160708
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4) Ux = (2 * h * Uw /B) + (1450 * E * v * fw/B) = (2 * 0.3 * 1.5 / 5.545268253204708) + (1450 * 0.003 * 5 *
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0.05/5.545268253204708) = 0.35841367978030436
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5) U = 1/ (2 * Rsi + Rf + 1/(Ug + Ux)) = 1 / (2 * 0.17 + 0 + 1/(0.5619604457160708 + 0.35841367978030436)) =
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0.701
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"""
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age_band_letter = [x for x in age_band_data if x[region] == age_band][0]["age_band"]
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defaults = {
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# We need width in meters
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"w": [x[age_band_letter] for x in default_wall_thickness if x["type"] == wall_type][0] / 1000,
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"lambda_g": 1.5,
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"Rsi": 0.17,
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"Rse": 0.04,
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"Rf": 0.001 * insulation_thickness / 0.035,
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"h": 0.3,
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"v": 5,
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"fw": 0.05,
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"E": 0.003,
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"Uw": 1.5,
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}
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dg = defaults["w"] + defaults["lambda_g"] * (defaults["Rsi"] + defaults["Rse"])
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# P is the exposed perimeter, which we estimate as we not have this data
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p = self._estimate_perimeter(floor_area=floor_area, num_rooms=number_of_rooms)
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b = 2 * floor_area / p
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u_g = 2 * defaults["lambda_g"] * math.log(math.pi * b / dg + 1) / (math.pi * b + dg)
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u_x = (2 * defaults["h"] * defaults["Uw"] / b) + (1450 * defaults["E"] * defaults["v"] * defaults["fw"] / b)
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# This is the final estimated U-value
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u = 1 / (2 * defaults["Rsi"] + defaults["Rf"] + 1 / (u_g + u_x))
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return u
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def recommend(self):
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is_suspended = self.property.floor["is_suspended"]
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insulation_thickness = self.property.floor["insulation_thickness"]
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# Check which floor the property is on
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self.property.year_built
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self.property.data["floor-energy-eff"]
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self.property.data["floor-env-eff"]
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@ -35,10 +117,67 @@ class FloorRecommendations(BaseUtility):
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if is_suspended:
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if insulation_thickness == "none":
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uvalue = None
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else:
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uvalue = self.uvalue_estimates.get_estimate(
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component="floor",
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description="",
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thickness=insulation_thickness
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region_str, age_band = self.property.data["construction-age-band"].split(":")
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region_str = region_str.strip()
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age_band = age_band.strip()
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region = self.REGION_LOOKUP[region_str]
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uvalue = self._estimate_suspended_floor_u_value(
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floor_area=float(self.property.data["total-floor-area"]),
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number_of_rooms=float(self.property.data["number-habitable-rooms"]),
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insulation_thickness=0,
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wall_type='solid brick',
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region=region,
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age_band=age_band,
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)
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else:
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uvalue = self._get_floors_uvalue_estimate()
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def _get_floors_uvalue_estimate(self):
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"""
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Wrapper function which contains the methodology to extract a property's walls u-value estimate
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when we don't have a true value and if we can't base our assumption off of the material
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:return:
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"""
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total_floor_area_group_decile = self.uvalue_estimates.classify_decile_newvalues(
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decile_boundaries=self.uvalue_estimates.floors_decile_data["decile_boundaries"],
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decile_labels=self.uvalue_estimates.floors_decile_data["decile_labels"],
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new_values=[float(self.property.data["total-floor-area"])],
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)[0]
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u_value_estimate = self.uvalue_estimates.floors[
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(self.uvalue_estimates.floors["local-authority"] == self.property.data["local-authority"]) &
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(self.uvalue_estimates.floors["property-type"] == self.property.data["property-type"]) &
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(self.uvalue_estimates.floors["built-form"] == self.property.data["built-form"]) &
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(self.uvalue_estimates.floors["floor-energy-eff"] == self.property.data["floor-energy-eff"]) &
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(self.uvalue_estimates.floors["floor-env-eff"] == self.property.data["floor-env-eff"]) &
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(self.uvalue_estimates.floors["total-floor-area_group"] == total_floor_area_group_decile)
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]
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if u_value_estimate.empty:
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raise ValueError("No U-value estimate found for the given property")
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# Because of how spuriously populated the data is for number-habitable-rooms and number-heated-rooms,
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# we will try and filter on these to see if we get a result
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habitable_rooms_filter = (
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self.uvalue_estimates.walls["number-habitable-rooms"] == self.property.data["number-habitable-rooms"]
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)
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if any(habitable_rooms_filter):
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u_value_estimate = u_value_estimate[habitable_rooms_filter]
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heated_rooms_filter = (
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self.uvalue_estimates.walls["number-heated-rooms"] == self.property.data["number-heated-rooms"]
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)
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if any(heated_rooms_filter):
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u_value_estimate = u_value_estimate[heated_rooms_filter]
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# It's possible for us to have multiple rows if we didn't do a habitable/heated rooms filter so we
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# average
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return u_value_estimate["median_thermal_transmittance"].mean()
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