The legacy water_heating_efficiency(901, main_code) returns 0.80 (gas
boiler default) when sap_main_heating_code is None — even if the main
system is a heat pump (category=4, efficiency 2.30). For "from main
system" water codes (901/902/914), we must inherit through the FULL
main-heating cascade including the category fallback.
Discovered by hand-tracing cert 0320-2850 (Semi-detached bungalow,
heat-pump main with no SAP code lodged, actual SAP 70, predicted 49).
HW was being charged at 0.80 eff for a 2.30-eff dwelling — 2.9× too
much HW fuel.
100-cert parity probe:
MAE 4.66 → 4.53 (-0.13)
RMSE 6.27 → 5.96
bias -0.70 → -0.57
within ±10: 94% → 95%
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Hand-tracing cert 0800-1364 (Detached bungalow, code 191/direct-electric,
actual SAP 71, predicted 37) showed the cert assessor applies off-peak
rates to direct-electric main heating despite SAP 10.2 Table 12a
specifying 90% high-rate. Adds e7_eligible_main_codes to PriceTable so
each price source carries its own rule:
- SAP_10_2_SPEC_PRICES: {401-409, 421-425} (storage only, per Table 12a)
- CERT_CALIBRATION: {191-196, 401-409, 421-425} (empirically what
the cert software does)
100-cert parity probe:
MAE 4.99 → 4.66 (recovered to pre-S-B9 best state)
bias -1.03 → -0.70
within ±1: 23% → 24%
within ±3: 47% → 48%
within ±10: 93% → 94%
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Separates the SAP-spec source of truth from the empirical cert-
calibration prices. cert_to_inputs() now accepts a `prices: PriceTable`
parameter defaulting to SAP_10_2_SPEC_PRICES (3.64 gas, 16.49 elec,
9.40 7h-low — verbatim from SAP 10.2 §12.2 / Table 12). Parity probe
passes the empirical cert_calibration_prices() factory from
domain.sap.tables.table_12_cert_calibration which carries the lower
prices that match the cert assessor software's actual output (3.48,
13.19, 5.50).
This split is documented in both table modules: cert calibration is
explicitly NOT spec-correct, it just matches observed cert behaviour
for parity testing.
100-cert parity probe with cert-calibration prices:
MAE 6.66 → 4.99 (recovered from spec-price regression; also -0.41
from absolute baseline thanks to other S-B fixes)
RMSE 10.29 → 7.13
bias -4.66 → -1.03
within ±1: 20% → 23%
within ±3: 38% → 47%
within ±5: 63% → 67%
within ±10: 82% → 93%
Session-B progress overall (S-B2 baseline → here): MAE 8.41 → 4.99,
within ±1 doubled (10% → 23%).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Verified against the SAP 10.2 spec (14-03-2025): Table 12 unit prices
are IDENTICAL to SAP 10.3 Table 12. Both specs mandate (§12.2): "Fuel
costs are calculated using the fuel prices given in Table 12. Other
prices must not be used for calculation of SAP ratings." The legacy
ML-pipeline prices in domain.ml.sap_efficiencies (3.48 gas, 13.19 elec,
5.50 E7-low) do NOT match either SAP 10.2 or 10.3 and appear to be a
pre-2022 holdover.
New module domain.sap.tables.table_12 carries the spec-correct
values:
mains gas: 3.64 (was 3.48 legacy)
standard electricity: 16.49 (was 13.19)
7h-low / Economy-7: 9.40 (was 5.50)
24h-heating: 14.04 (was 6.61)
Also corrects an S-B4 bug: SAP 10.2 Table 12a shows direct-acting
electric heating (codes 191-196) runs at 90% high-rate on 7h tariffs,
not 0% — only true storage heaters (401-409, 421-425) bill at the
low rate. _E7_SPACE_HEATING_CODES narrowed accordingly.
100-cert parity probe with spec-correct prices:
MAE 4.66 → 6.66 (regression vs legacy prices)
bias -0.70 → -4.66 (over-counting cost)
spec-correctness: SAP 10.2 verbatim
The MAE regression confirms the corpus's lodged ratings were NOT
calculated against the published SAP 10.2 Table 12 prices. The cert
ratings appear to use the legacy lower prices despite reporting
sap_version=10.2. Three paths forward documented in next commit's
discussion thread.
Also adds the SAP 10.2 spec PDF to docs/sap-spec/.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
When the main heating is electric storage / direct-electric (codes
191-196, 401-409, 421-425), the cert almost always carries an
Economy-7 tariff and the immersion HW cylinder runs on the off-peak
timer. Bill HW at the 7h-low rate (5.5 p/kWh) in that case, falling
back to the lower of {7h-low, water_heating_fuel rate} so we never
over-charge an HW fuel that's already cheaper than off-peak.
100-cert parity probe:
MAE 4.90 → 4.66 (-0.24)
bias -1.44 → -0.70 (over-correction halved)
within ±3: 46% → 48%
within ±5: 67% → 68%
within ±10: 93% → 94%
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
SAP 10.3 §12 charges fuel costs by end-use, not by main heating fuel.
For a gas-heated dwelling with an electric immersion hot-water cylinder,
HW bills at the electric rate (13.19 p/kWh) not the gas main-heating
rate (3.48 p/kWh) — a 3.8× cost difference for HW that propagates
straight to ECF. Lighting, central-heating pumps, and fans always
electric regardless of main fuel.
Discovered by hand-tracing cert 8035-9023 (Detached bungalow, actual
SAP 43, predicted 63). Trace showed our hot-water + lighting + pumps
lines were charging mains-gas rates throughout, under-counting cost by
~£290/yr.
100-cert parity probe (biggest single Session-B slice so far):
MAE 5.70 → 4.90 (-0.80, -14%)
RMSE 7.48 → 6.68 (-11%)
within ±1: 20% → 24%
within ±3: 37% → 46%
within ±5: 54% → 67%
bias +1.50 → -1.44 (over-corrected by ~3 SAP points)
The over-correction (bias now slightly negative) means we're now
under-predicting on average. Next slice tackles where we're charging
too much electricity — probably HW on dwellings with combi boilers (no
immersion, water still on main fuel) and the water_heating_code 901
("from main system") inheritance path.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Replaces the two hardcoded glazing defaults (g⊥=0.63, FF=0.7) in the
cert→inputs mapper with spec-driven lookups:
- g_perpendicular by glazing_type (Table 6b):
single → 0.85, double 2002+ → 0.72, low-E soft → 0.63,
secondary → 0.76, triple → 0.68. Default 0.72 when missing.
- frame_factor by frame_material (Table 6c):
wood/PVC/composite → 0.70, aluminium/steel/metal → 0.83.
Measured values from window_transmission_details / SapWindow.frame_factor
still take precedence. Overshading factor stays at 0.77 ("average") since
RdSAP 10 doesn't lodge a per-window overshading code.
100-cert parity probe:
MAE 5.65 → 5.70 (flat)
exact-match within ±1: 18% → 20%
bias +1.13 → +1.50
Slight bias drift toward over-prediction is expected — bigger solar
gains reduce predicted heating demand. Net: the engine is now more
spec-correct (more exact matches), but composition of errors elsewhere
needs the next slice to bring bias back toward 0.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Maps the Table 9 main_heating_control code to SAP control type 1/2/3:
codes 2101-2104 = type 1, 2105-2109 = type 2, 2110+ = type 3. Default
remains type 2 when code is missing or unrecognised.
Two other fixes tried-and-reverted in this slice based on the 100-cert
parity probe:
- NI-thickness → None (the "wall insulated but thickness unknown,
use 50mm row" path): over-corrected in aggregate because many "NI"
certs are genuinely uninsulated. Reverted to legacy NI→0 with a
note to revisit once wall_insulation_type is used as a stronger
signal.
- boiler-age efficiency rescue (cat 1/2, A-F → 0.74, K-M → 0.85):
same issue — stacked with NI fix it over-shot, on its own it gave
marginal MAE without bias improvement. Dropped pending further
investigation.
100-cert parity probe:
MAE 5.72 → 5.65 (-0.07; control-type-only is a small net win)
RMSE 7.58 → 7.48 (-0.10)
bias +1.20 → +1.13
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Splits the single CalculatorInputs.fuel_unit_cost_gbp_per_kwh into three
end-use lines — space_heating, hot_water, other — to match SAP 10.3 §12
which charges different tariffs per end-use on Economy-7 dwellings.
cert→inputs rule: when sap_main_heating_code is in the electric-storage
(401-409), high-heat-retention storage (421-425), or direct-electric
(191-196) ranges, space heating bills at the 7h-low rate (5.5p/kWh)
while hot water + lighting + pumps stay on standard electricity
(13.19p/kWh). All other fuels use a single rate across all three end-
uses.
100-cert parity probe impact:
MAE 7.53 → 5.72 (-1.81, -24%)
RMSE 11.60 → 7.58 (-4.02, -35%)
worst residual -56 → -25 (Semi-detached bungalow)
within ±10: 85% → 91%
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Adds services/ml_training_data/src/ml_training_data/sap_parity_probe.py
— samples N certs from the v18a corpus, streams them via BulkZipReader,
runs Sap10Calculator, prints MAE/RMSE/bias + worst-N residuals. Baseline
across 100 certs: MAE 8.41, RMSE 13.98, bias -2.65, 0 errors.
docs/sap-spec/PARITY_FINDINGS.md captures the dominant failure pattern
(flats + bungalows under-predicted, 10 of the worst-15 are flats whose
floor/roof are party with neighbouring dwellings) and the priority-
ordered Session B iteration backlog (S-B-flat-surfaces first).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Pure-function ParityCase / ParityReport / build_parity_report for the
Session B 1000-cert parity check (ADR-0009). Aggregates per-cert
(predicted, actual) sap pairs into global + typical-subset MAE, RMSE,
bias, and the worst-N residuals for spec-iteration. Cert→case mapping
(corpus load, calculator run, actual-sap lookup) sits at a higher
layer; this module is trivial to test so the harder integration code
inherits its testing.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Adds domain.sap.rdsap.cert_to_inputs.cert_to_inputs(epc) which produces a
typed CalculatorInputs from an EpcPropertyData, and a thin
Sap10Calculator.calculate(epc) entry point that wraps the mapper + the
S-A7a orchestrator. Defaults follow RdSAP 10 (Table 27 for living-area
fraction, Table 5 for ventilation, Table 12 for fuel cost + CO2 factor)
and SAP 10.3 Tables 4a/4b for heating efficiency via the existing
domain.ml.sap_efficiencies cascade.
Deferred to Session B: conservatory modes, room-in-roof, secondary
heating split (Table 11), multi-fuel weighted cost, thermal-mass
parameter from construction type, control-temp adjustment from
main_heating_control code.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Wires SAP 10.3 §§5-13 into a 12-month heat-balance loop driven by a typed
CalculatorInputs aggregate, returning a typed SapResult with the score,
ECF, costs/CO2 totals, and a 12-entry monthly breakdown. Physics
assembly only — the cert→inputs mapper lands in S-A7b. η/T_internal
solved with two-pass iteration per SAP 10.3 §7.3.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Tenth slice of the SAP10 Calculator Session A (ADR-0009). Ships four
pure functions under domain.sap.worksheet.rating implementing the SAP
10.3 rating formulas:
energy_cost_factor(total_cost_gbp, total_floor_area_m2)
-> equation (7): ECF = 0.36 × cost / (TFA + 45)
Deflator 0.36 sourced from Table 12 (page 191).
sap_rating(ecf)
-> equations (8)/(9), continuous (un-rounded) SAP value:
ECF ≥ 3.5: 108.8 − 120.5 × log10(ECF)
ECF < 3.5: 100 − 16.21 × ECF
Naturally rises above 100 for net energy exporters (negative ECF).
sap_rating_integer(ecf)
-> integer SAP value as published on the EPC: round to nearest, clamp
to minimum 1 per §13.
environmental_impact_rating(co2_emissions_kg_per_yr, total_floor_area_m2)
-> equations (10)-(12), continuous EI rating:
CF = CO2 / (TFA + 45)
CF ≥ 28.3: 200 − 95 × log10(CF)
CF < 28.3: 100 − 1.34 × CF
8 AAA cycles cover: ECF formula hand-computed, SAP linear branch (typical
home), SAP log branch (high cost), boundary continuity at ECF=3.5,
net-exporter SAP > 100, integer rounding + min-1 clamp, EI linear branch,
EI log branch.
Orchestrator (S-A7) wires these into Sap10Calculator alongside the monthly
heat balance loop from S-A5e.
Ninth slice of the SAP10 Calculator Session A (ADR-0009). Ships
monthly_heat_requirement_kwh implementing the Table 9c step-10 formula:
L_m = H × (T_i,m − T_e,m) (W)
Q_heat,m = 0.024 × (L_m − η_m × G_m) × n_m (kWh)
with the table's clamp: Q_heat is set to 0 when negative or below 1 kWh
per month (summer months and well-insulated dwellings in shoulder
months).
The orchestrator (S-A6) iterates utilisation factor + mean internal
temperature until they converge before calling this function.
5 AAA cycles cover: typical-winter-month hand-computed worked example,
summer month with gains exceeding losses clamping to 0, gains-scaling
direction check, external-temperature direction check, and the sub-1-kWh
clamp per the Table 9c note.
Eighth slice of the SAP10 Calculator Session A (ADR-0009). Implements
SAP 10.3 mean internal temperature with three public helpers under
domain.sap.worksheet.mean_internal_temperature:
elsewhere_heating_temperature_c(hlp, control_type)
-> Table 9 T_h2 formula:
control type 1: T_h2 = 21 − 0.5 × HLP
control type 2 or 3: T_h2 = 21 − HLP + HLP² / 12
HLP clamped to 6.0 per Table 9 note (e).
off_period_temperature_reduction_c(t_off, T_h, T_e, R, G, H, η, τ)
-> Table 9b u value (°C drop below T_h over an off-period):
t_c = 4 + 0.25·τ
T_sc = (1−R)(T_h−2) + R·(T_e + η·G/H)
quadratic branch when t_off ≤ t_c, linear when t_off > t_c.
mean_internal_temperature_c(...)
-> Table 9c steps 1-8: living-area zone (off 7+8 h, T_h1=21°C) and
elsewhere zone (off 7+8 h for control 1/2 or 9+8 h for control 3,
T_h2 from above), blended by living_area_fraction, plus the
Table 4e control-type temperature adjustment.
Step 9 (re-compute utilisation factor with the new T_i) and step 10
(Q_heat = 0.024 × (L − η·G) × n_m) live in the next slice's monthly loop.
7 AAA cycles cover: T_h2 formulas for control types 1 vs 2, HLP > 6 clamp
per note (e), off-period u quadratic branch (t_off ≤ t_c), off-period u
linear branch (t_off > t_c), full mean_internal_temperature hand-computed
worked example, and control-type-3 longer first off-period dropping mean
temp slightly below control-type-2.
Seventh slice of the SAP10 Calculator Session A (ADR-0009). Ships
utilisation_factor(*, total_gains_w, heat_loss_rate_w, time_constant_h)
implementing SAP 10.3 Table 9a:
a = 1 + τ / 15
γ = G / L
if γ > 0 and γ ≠ 1: η = (1 − γ^a) / (1 − γ^(a+1))
if γ = 1: η = a / (a + 1)
if heat_loss_rate ≤ 0: η = 1 (dwelling in net surplus)
η caps the contribution of internal + solar gains when they outpace the
heat-loss rate. The orchestrator computes time_constant_h = TMP /
(3.6 × HLP) and passes it in here; that's a future slice.
5 AAA cycles cover: small γ → η ≈ 1, γ = 1 special-case formula,
zero/negative heat loss returning η = 1, large γ dropping η well below
0.5, and higher τ (more thermal mass) raising η for the same γ.
Sixth slice of the SAP10 Calculator Session A (ADR-0009). Two layers
under domain.sap.worksheet.solar_gains:
1. surface_solar_flux_w_per_m2(orientation, pitch_deg, region, month)
— implements Appendix U §U3.2 polynomial that converts the horizontal
solar irradiance from Table U3 to per-orientation per-pitch surface
flux:
S(orient, p, m) = S_h,m × R_h-inc
R_h-inc = A cos²(φ-δ) + B cos(φ-δ) + C
where A, B, C are cubics in sin(p/2) with coefficients k1-k9 from
Table U5. Reads latitude φ from Table U4 and solar declination δ
from Table U3 footer (already in domain.sap.climate.appendix_u).
2. window_solar_gain_w(area_m2, surface_flux, g⊥, FF, Z)
— implements §6.1 equation (5): G = 0.9 × A × S × g⊥ × FF × Z.
Orientation enum maps the 8 SAP cardinal codes to the 5 Table U5 columns:
N/S to their own column; NE/NW share; E/W share; SE/SW share.
7 AAA cycles cover: UK average South vertical July hand-computed flux,
rooflight pitch=0 collapses to horizontal Table U3 directly, North-vertical
summer > winter (diffuse signal), NE/NW share constants symmetry, equation
(5) window gain, zero-area edge case, out-of-range region validation.
Tables 6b (g⊥), 6c (frame factor), 6d (overshading Z) defaults deferred
to the cert→inputs mapper slice — callers pass them explicitly here so
the physics stays cert-shape-independent.
Fourth slice of the SAP10 Calculator Session A (ADR-0009). Ports the
per-element conduction HLC logic out of domain.ml.envelope into a typed
HeatTransmission breakdown under domain.sap.worksheet. Aggregates Σ U×A
across walls, roof, floor, party walls, windows, doors, plus thermal-
bridging y × total exposed area, summed across every building part.
The orchestrator can now read walls_w_per_k / roof_w_per_k / floor_w_per_k
etc. directly off the result for audit + monthly-loop wiring, rather than
seeing a single envelope_heat_loss scalar.
U-value cascade still routes through domain.ml.rdsap_uvalues (migrates to
domain.sap.rdsap.cascade_defaults in Session B per ADR-0009 module-layout
plan). domain.ml.envelope stays in place to keep the ML transform's
physics-feature pipeline running until Session B.
6 AAA cycles cover: per-element breakdown for a baseline age-G cavity
mid-terrace, window net-wall subtraction, insulated-door U-value blending,
cavity-party-wall contribution per Table 15, thermal-bridging scaling by
age band per Table 21, and multi-part (main + extension) aggregation.
192 tests pass across domain.sap + domain.ml — no regressions.
Third slice of the SAP10 Calculator Session A (ADR-0009). Ports the SAP
10.2 / RdSAP10 §4.1 air-change-rate worksheet for the no-pressure-test
path. Returns an InfiltrationBreakdown carrying each named worksheet line
so callers can audit per SAP convention:
(8) openings_ach — Table 2.1 rate × count / volume
(10) additional_ach — (storey_count − 1) × 0.1
(11) structural_ach — 0.25 steel/timber-frame, 0.35 masonry
(12) floor_ach — 0.2 unsealed timber / 0.1 sealed / 0
(13) draught_lobby_ach — 0.05 absent, 0.0 present
(15) window_ach — 0.25 − 0.2 × (pct_dp / 100)
(16) total_ach — sum of all of the above
Table 2.1 rates: open chimney 80, open flue 20, closed-fire chimney 10,
solid-fuel-boiler chimney 20, other-heater chimney 35, blocked chimney
20, intermittent fan 10, passive vent 10, flueless gas fire 40 (all
m³/hour per opening).
9 AAA cycles cover the baseline calculation, each Table 2.1 opening
contribution, frame-vs-masonry structural baseline, suspended-timber
floor sealed/unsealed split, draught-lobby presence, window draught-
proofing scale, multi-opening aggregation, and volume_m3 ≤ 0 validation.
Pressure-test override (worksheet lines 17-21) and mechanical-ventilation
adjustments (Table 4g, n_eff formula §2.6.6) are out of scope for this
slice — separate later slices per ADR-0009.
Second slice of the SAP10 Calculator Session A (ADR-0009). Ships a frozen
Dimensions dataclass + dimensions_from_cert(epc) pure function under
domain/sap/worksheet/. Aggregates geometry across every sap_building_parts
entry (main dwelling + each extension): total floor area, volume, storey
count, area-weighted average storey height, ground/top floor area,
ground-floor heat-loss perimeter, gross wall area, party wall area.
Top-level epc.total_floor_area_m2 is the authoritative TFA; per-storey
sums drive the wall-area calculations. Volume = TFA × avg_storey_height.
5 AAA cycles cover: single-storey single-part, two-storey scaling,
main+extension aggregation, empty-cert fallback to default 2.5 m height,
and a non-default-height terrace exercising party-wall scaling.
Edge cases (porches, conservatories, integral garages, RIR storey
treatment) deferred to later slices per ADR-0009 Session A scope.
First slice of the SAP10 Calculator Session A (ADR-0009). Ships the three
SAP 10.3 Appendix U monthly tables across 22 climate regions (region 0 =
UK average; 1-21 named per spec) as a pure-data module under the new
domain/sap/ package:
- Table U1: mean external temperature (°C)
- Table U2: wind speed (m/s)
- Table U3: mean global solar irradiance on horizontal plane (W/m²)
- Table U3 footer: monthly solar declination (°, region-independent)
Lookups validate region (0..21) and month (1..12) and raise ValueError
on out-of-range inputs. 11 AAA tests cover happy-path lookups across
multiple regions/months plus boundary and error cases.
Promotes ADR-0009 from Proposed to Accepted after the grill-with-docs
session resolved all seven open questions. Bundles the SAP 10.3 and
RdSAP 10 specifications under docs/sap-spec/ plus a calculator design
sketch (module layout, monthly-loop pseudo-code, status table).
CONTEXT.md adds three new domain terms parallel to existing performance
language:
- Calculated SAP10 Performance (parallel to Effective / Lodged)
- SAP10 Calculation (process; implemented by Sap10Calculator)
- Measure Application (process; implemented by MeasureApplicator)
ML pipeline is NOT retired — it stays as the residual head once the
calculator reaches parity in Session B. ADR-0009 §"Grill outcomes" carries
the seven binding scope decisions plus three Session-A-scope changes
discovered during the grill (RdSAP §19 EER formula, SAP 10.2 Appendix A
cross-reference, RdSAP Table 29 cascade defaults).
v20a added ventilation_heat_loss_w_per_k as a standalone feature but never
connected it to the HLC inside predicted_space_heating_kwh, so the
downstream physics aggregates (predicted_ecf, predicted_total_fuel_cost,
predicted_log10_ecf — the top-10 model features) never saw the
infiltration signal. Importance for ventilation_heat_loss_w_per_k was rank
58/196 (importance 30) vs envelope's rank 21 (86).
Adds the ventilation column to the envelope-conduction HLC before
applying HDH and efficiency, so chimney + draught-proofing signals flow
through the physics aggregates the model actually uses. Default 0 keeps
backwards compatibility.
Adds SAP10.2 §C tracer-bullet infiltration model as a new physics-as-feature
column alongside envelope_heat_loss_w_per_k. ACH = structural baseline
(0.35 masonry / 0.25 timber-or-system-built) + open chimneys at 40 m³/h each
minus a draught-proofing reduction scaled by window_pct_draught_proofed,
then volumed and converted to W/K. Targets the d0 catastrophic-low-SAP tail
where chimney + leakage signals dominate but envelope conduction alone
under-counts heat loss.
Scope deferred to follow-ups: MVHR/MEV factors (mechanical_ventilation is
100% null in the corpus), pressure-test override (pressure_test also 100%
null - slice 18e mapper fix), open flues / passive vents / flueless gas
fires (sap_ventilation sparsely populated).
Many real certs carry main_heating_category=4 (heat pump) but null
sap_main_heating_code, so seasonal_efficiency() was returning the 0.80
gas-boiler default — a 3x COP under-count that dragged the high-SAP
heat-pump tail. Adds main_heating_category + main_fuel_type fallbacks:
cat=4 -> 2.30, cat=7 -> 1.00, cat=10 routes by fuel
(electric=1.00, gas=0.55, oil=0.65), cat=5 warm air -> 0.76.
Explicit SAP codes still win.
When wall_construction integer is missing or WALL_UNKNOWN, u_wall now
parses the top-level walls[i].description for material keywords
(sandstone/limestone/granite/whinstone/cob/system built/timber frame/
solid brick/cavity) before falling through to the cavity-by-age default.
Explicit construction codes still win. Threaded through
envelope_heat_loss_w_per_k via a joined wall description string off the
top-level walls list.
Table 18 age-band roof defaults assume joist insulation >= 100mm, which
mis-rates heritage roofs the surveyor explicitly described as
uninsulated. u_roof now reads roofs[i].description and routes
"no insulation" / "uninsulated" -> 2.30 W/m^2K and "limited insulation"
-> 1.50 W/m^2K, threaded through envelope_heat_loss_w_per_k via a single
joined description string off the top-level roofs list.
Explicit insulation_thickness_mm still wins over description.
predicted_total_fuel_cost_gbp was silently mispricing every non-gas
property because primary_main_fuel_type / water_heating_fuel store the
gov EPC API enum (26=mains gas, 27=LPG, 28=oil, 29=electricity) and our
_FUEL_UNIT_PRICE dict is keyed by Table 32 codes (1=gas, 4=oil, 30=elec).
Codes 26-29 hit the dict's default 3.48 p/kWh -- silently treating
electric immersion as gas.
Concrete impact on OX1 5LR Sep 2025 cert (worst-predicted SAP=41, model
84): water_heating_fuel=29 (electric immersion). Real DHW cost 2941 kWh
* 13.19p = £388/yr; we computed 2941 * 3.48 = £102 (4x under). Net
predicted_total_fuel_cost £292 vs implied real £2513 -- predicted_ecf
0.49 (~SAP 93) vs real ECF 4.24 (SAP 41).
Effect: every off-gas property's predicted_ecf was systematically too
low, dragging the model's catastrophic-low-SAP predictions toward
mid-band. Expected to substantially reduce decile-0 bias on retrain.
New _API_TO_TABLE32 map covers codes 0-29. 4 new AAA tests; VERSION
2.2.0 -> 2.3.0 (MINOR; behavioural fix to existing column values).
The 17a-baseline residuals showed cylinder_insulation_thickness_mm,
cylinder_size and cylinder_insulation_type at ranks 3/6/9 for hot_water_kwh
because the crude 16d formula didn't use them -- the model had to learn
storage physics from raw features.
Now predicted_hot_water_kwh sums:
useful_demand (existing, unchanged)
+ distribution_loss = useful * 0.15
+ storage_loss = volume * insulation_factor * 365 * 0.6
(volume from cylinder_size, factor from
cylinder_insulation_thickness_mm or age-default)
+ primary_circuit_loss = 245 (age A-J) / 60 (age K-M)
- wwhrs_credit = useful * 0.12 if number_baths_wwhrs > 0
- solar_hw_credit = 250 if solar_water_heating
all / efficiency_water = delivered kWh
Same inputs we already extract; just plumbed through. Expected:
predicted_hot_water_kwh feature usage jumps from rank 10 to top tier,
hot_water_kwh MAPE drops from 7.17%, and predicted_ecf gets tighter for
gas-heat + electric-DHW mid-band homes -> SAP MAPE marginally better.
5 new AAA tests; VERSION 2.1.0 -> 2.2.0 (MINOR; column semantics enriched).
Closes the high-SAP under-prediction gap diagnosed in 16h. 40% of SAP-85+
properties have PV; predicted_ecf was 1.74 mean at that band -> SAP ~88
via the formula, vs label SAP 90+. Inverse: PV homes had HIGHER predicted_ecf
than non-PV at the same band because cost reconstruction had zero export
credit.
New helper: predicted_pv_generation_kwh(kWp, region) -> kWh/yr from a
SAP10.2 Table 6e regional yield factor (UK avg 850 kWh/kWp/yr; Highland
650; Thames 920).
predicted_total_fuel_cost_gbp now subtracts pv_kwh * standard electricity
price (Table 32 code 30, both self-consumption and export at 13.19 p/kWh).
New feature column predicted_pv_generation_kwh exposed alongside the
adjusted cost so the model sees both signals.
VERSION 2.0.0 -> 2.1.0 (MINOR: column added; existing column semantics
shifted but pre-deploy so no consumer break).
train_baseline now returns mae + rmse alongside mape/smape/r2. MAE is the
user-facing metric ("predicted SAP within N points"); RMSE the quadratic
counterpart. Both come straight from sklearn.
New sample_weight_fn parameter: callable(y_train) -> per-row weights.
Threads into LGBMRegressor.fit's sample_weight argument. Default None
preserves existing behaviour.
Default tail strategy exposed as low_sap_tail_weight(y, threshold=58,
weight=3): 3x weight where SAP < 58. Threshold picked from slice 16h's
per-decile residuals — decile 0 (SAP 1-58) carries 17% MAPE vs <5% body.
Three TDD tracers, all AAA.
250k retrain showed objective='mape' loses ~0.6 percentage points of
global sap_score MAPE (3.92% with regression vs 4.50% with mape) and
~0.7 pts on peui_ucl. The mape objective over-weights the low-SAP tail
(weight ~1/y) and drags the body MAPE up by more than it gains in the
tail.
Body MAPE on v16 features is already strong (2.38% on deciles 1-8); the
remaining tail bias at decile 0 (SAP<58, +3.1 bias) needs a different
fix -- sample weights or stratified loss -- queued as slice 16i.
User reverted the transaction_type drop after noting that it doesn't help
detect full-SAP assessments (that's `assessment_type` on the bulk-register
record, filtered out at build_features.py:37).
tenure removal stays; v2.0.0 still MAJOR (a column was removed).
Neither field physically affects SAP rating; they're dataset-side metadata
(owner-occupied vs rented, sale vs marketed) and any correlation with
sap_score is confounded with age/condition that the model already sees
through built_form / property_type / construction_age_band.
Dropping reduces feature count and removes a source of spurious split-gain.
MAJOR per ADR-0007 versioning policy (column removal): 1.0.0 -> 2.0.0.
Per ADR-0008: the v15 baseline reports MAPE but optimises MSE, which
under-weights tail rows. Switching to objective='mape' applies gradient
proportional to 1/|y| and lets the model focus where MAPE penalises.
Targets co2_emissions, space_heating_kwh, hot_water_kwh, and peui_raw
retain the default 'regression' objective (some rows have ~zero CO2 from
heavy PV; MAPE objective destabilises near zero).
Sample weights deferred to slice 16i if slice 16h's per-decile residuals
still show tail bias after the objective switch.
12 columns renamed; extension_2_* not added (88% null on 250k corpus;
envelope_heat_loss_w_per_k already sums extension_2+ via part-iterator).
ADR-0008.
VERSION 0.4.0 -> 1.0.0 (MAJOR per ADR-0007 versioning policy). Coordinated
cutover with AutoGluon repo + scoring lambda required at deploy time.
features_v16.txt is regenerated from transform.schema() at write-parquet time
(data/ml_training is gitignored; not committed).
ECF reconstruction per SAP10 §20.1 (Mid physics, ADR-0008):
total_cost_gbp = (space_kwh*p_space + dhw_kwh*p_dhw + light_kwh*p_elec) / 100
ECF = 0.42 * total_cost / (TFA + 45)
log10_ecf = log10(ECF) [0 for non-positive]
p_* are Table 32 unit prices via fuel_unit_price_p_per_kwh. Standing
charges deliberately omitted (constant fuel-mix offset; ADR-0008).
predicted_sap_score is NOT emitted as a feature (ADR-0008 Mid not Deep):
the model is left to learn the piecewise log/linear transform from
log10_ecf -> SAP itself, keeping the data layer SAP-version-agnostic.
VERSION 0.3.0 -> 0.4.0 (MINOR).
New module domain.ml.envelope sums Sigma(U*A) + y*A_exposed across every
sap_building_part on a cert. U-values come from rdsap_uvalues' cascade
defaults, so the feature is never null.
Per-part inputs: wall / roof / floor / party-wall / windows / doors.
Windows + doors are apportioned to the main part (first in the list)
per RdSAP10 convention.
Wired into EpcMlTransform.to_row; transform VERSION 0.1.0 -> 0.2.0
(MINOR bump for an additive column per the ADR-0007 policy).
7 envelope unit tests + 2 transform-level tests, all AAA. Reference
geometry: 100 m^2 age-G mid-terrace -> ~208 W/K; doubles for two
storeys; drops with better insulation; sums across extensions.
Adds `_per_decile_residuals` and writes `residuals_<target>.json` next to
metrics.json. Buckets test-set rows by deciles of the true target value;
each bucket carries count + MAPE + MAE + mean residual + true_min/max.
Lets us tell whether errors concentrate in the tails of the true distribution
(e.g. SAP<40 / SAP>85) vs the mid-band — which the global MAPE alone hides.
Baseline for slice 16's MAPE-improvement ablations.
Previously kept the full list of EpcPropertyData in memory before calling
EpcMlTransform.to_rows. For the 25k slice that's ~30 MB; for the 580k
full-2026 corpus it OOM-killed the process silently. Now: parse cert ->
to_row -> append dict -> drop EpcPropertyData reference, so memory is
O(row-dict * n) instead of O(EpcPropertyData * n). Same end-of-frame
post-processing (categorical casts, column-order pin).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
