File size: 3,966 Bytes
ba7b0bc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 | # unihvac/tables.py
from __future__ import annotations
import pandas as pd
import numpy as np
def print_monthly_tables_split(df: pd.DataFrame, location: str, time_step_hours: float):
"""
Table 1: Monthly HVAC electricity + temps
Table 2: Monthly occupancy
"""
drop_cols = [c for c in df.columns if c.startswith("has_") or c == "feature_registry"]
df_clean = df.drop(columns=drop_cols, errors="ignore").copy()
if "month" not in df_clean.columns:
return
df_clean["month"] = df_clean["month"].round().astype(int)
# 1. Electricity / Energy Calculation
energy_cols = []
peak_cols = []
if "elec_power" in df_clean.columns:
if "elec_power_kw" not in df_clean.columns:
df_clean["elec_power_kw"] = df_clean["elec_power"] / 1000.0
if "elec_energy_kwh" not in df_clean.columns:
df_clean["elec_energy_kwh"] = df_clean["elec_power_kw"] * time_step_hours
energy_cols.append("elec_energy_kwh")
peak_cols.append("elec_power_kw")
# 2. Temperature Aggregation
temp_cols = [c for c in ["outdoor_temp", "core_temp", "perim1_temp", "perim2_temp", "perim3_temp", "perim4_temp"]
if c in df_clean.columns]
agg1 = {c: "sum" for c in energy_cols}
agg1.update({c: "max" for c in peak_cols})
agg1.update({c: "mean" for c in temp_cols})
tbl1 = df_clean.groupby("month").agg(agg1).sort_index()
# 3. Occupancy Aggregation
occ_cols = [c for c in df_clean.columns if c.endswith("_occ_count")]
tbl2 = df_clean.groupby("month")[occ_cols].mean().sort_index() if occ_cols else pd.DataFrame()
if not tbl2.empty:
tbl2["occ_mean_total"] = tbl2.sum(axis=1)
print("\n" + "=" * 110)
print(f"MONTHLY ELECTRICITY + TEMPERATURE — {location}")
print("=" * 110)
print(tbl1.round(2).to_string())
print("\n" + "=" * 110)
print(f"MONTHLY OCCUPANCY — {location}")
print("=" * 110)
print(tbl2.round(3).to_string())
print("=" * 110 + "\n")
def print_monthly_tables_extra(df: pd.DataFrame, location: str) -> None:
d = df.copy()
if "month" not in d.columns:
return
d["month"] = d["month"].round().astype(int)
violation_cols = [c for c in ["comfort_violation_degCh", "comfort_violation_fixed_degCh"] if c in d.columns]
tbl_sums = d.groupby("month")[violation_cols].sum()
occ_cols = [c for c in d.columns if c.endswith("_occ_count")]
total_occ = d[occ_cols].sum(axis=1)
is_occupied = total_occ > 1e-6
d_occ = d[is_occupied].copy()
def person_weighted_ppd(group):
occ = group[occ_cols].sum(axis=1)
raw_ppd = group["ppd_weighted"]
return (raw_ppd * occ).sum() / occ.sum() if occ.sum() > 0 else np.nan
if not d_occ.empty and "ppd_weighted" in d_occ.columns:
ppd_monthly = d_occ.groupby("month", group_keys=False).apply(person_weighted_ppd)
ppd_monthly = ppd_monthly.clip(lower=5.0)
pmv_monthly = d_occ.groupby("month")["pmv_weighted"].mean()
rh_monthly = d_occ.groupby("month")["rh_weighted"].mean()
tbl_means = pd.DataFrame({
"ppd_weighted": ppd_monthly,
"pmv_weighted": pmv_monthly,
"rh_weighted_%": rh_monthly
})
tbl3a = pd.concat([tbl_sums, tbl_means], axis=1).sort_index()
else:
tbl3a = tbl_sums
outdoor_vars = [c for c in ["outdoor_temp", "outdoor_dewpoint", "outdoor_wetbulb"] if c in d.columns]
tbl3b = d.groupby("month")[outdoor_vars].mean().sort_index() if outdoor_vars else None
print("\n" + "=" * 110)
print(f"MONTHLY COMFORT OUTCOMES (Occupancy Weighted) — {location}")
print("=" * 110)
print(tbl3a.round(3).to_string())
print("=" * 110)
if tbl3b is not None:
print("\n" + "=" * 110)
print(f"MONTHLY OUTDOOR CONDITIONS — {location}")
print("=" * 110)
print(tbl3b.round(3).to_string())
print("=" * 110)
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