{
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plant Efficiency Summary & Energy Waste Quantification\n",
    "\n",
    "**Abriliam Consulting** — Industrial Energy Management\n",
    "\n",
    "This final notebook brings together the findings from the diagnostic series to quantify the total energy waste attributable to the identified operational faults. We classify inefficient hours by their dominant cause — chiller performance, pumping excess, or tower fan issues — and estimate the energy (MWh) tied to each fault category.\n",
    "\n",
    "This is the \"so what?\" notebook — translating diagnostic findings into actionable business metrics.\n"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "outputs": [],
   "execution_count": null,
   "source": [
    "import matplotlib\n",
    "matplotlib.use('Agg')\n",
    "# Load the dataset generated in Notebook 01\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_csv('chiller_plant_data.csv', index_col=0, parse_dates=True)\n",
    "print(f'Loaded {len(df)} rows, columns: {list(df.columns)}')\n"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "outputs": [],
   "execution_count": null,
   "source": [
    "# Compute derived columns needed for this notebook\n",
    "df['chiller_kw_per_ton'] = df['chiller_kw'] / df['tons']"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "outputs": [],
   "execution_count": null,
   "source": [
    "# Compute derived columns needed for this notebook\n",
    "df['chiller_kw_per_ton'] = df['chiller_kw'] / df['tons']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "1743ba2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['oat_C', 'wb_C', 'occ', 'tons', 'chw_sup_C', 'chw_ret_C', 'chw_dT_C',\n",
       "       'chw_flow_m3h', 'cw_sup_C', 'cw_ret_C', 'cw_dT_C', 'cw_flow_m3h',\n",
       "       'approach_C', 'dp_kpa', 'chiller_kw', 'tower_fan_kw', 'chw_pump_kw',\n",
       "       'cw_pump_kw', 'plant_kw', 'kw_per_ton', 'plant_kw_per_ton',\n",
       "       'tower_fan_kw_per_ton', 'pumping_kw_per_ton', 'kw_per_ton_15_sma',\n",
       "       'kw_per_ton_5_sma', 'kw_per_ton_24_sma', 'kw_per_ton_240_sma',\n",
       "       'chiller_kw_per_ton', 'free_cooling_candidate', 'wb_margin_C',\n",
       "       'free_cooling', 'freecool_possible_oat', 'freecool_threshold_C',\n",
       "       'calculated_tons'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "c2cf7ba3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Energy tied to inefficient operation ===\n",
      "Timestep assumed: 1.000 h per row\n",
      "Rows analyzed (after cleaning + load gate): 1,344\n",
      "Bad rows (plant kW/ton > 4.0): 233 (17.3% of hours)\n",
      "Energy during bad rows: 99.1 MWh (10.2% of total 971.5 MWh)\n",
      "\n",
      "=== Subsystem dominance among bad hours ===\n",
      " chiller-dominated: 233 hours (100.0%)\n",
      "\n",
      "Average share of plant kW/ton during bad hours (by component):\n",
      "      chiller_kw_per_ton: 88.0%\n",
      "      pumping_kw_per_ton: 6.1%\n",
      "    tower_fan_kw_per_ton: 5.9%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "matplotlib.use('Agg')\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# =========================\n",
    "# CONFIG (tune as needed)\n",
    "# =========================\n",
    "KW_PER_TON_BAD = 4.0        # threshold for \"bad\" plant efficiency\n",
    "MIN_TONS = 20               # ignore tiny loads (set to 0 to disable)\n",
    "DOMINANCE_FRAC = 0.50       # \"dominated by subsystem\" if >= this share of plant kW/ton\n",
    "\n",
    "# =========================\n",
    "# Helper: estimate timestep hours\n",
    "# =========================\n",
    "def infer_timestep_hours(df: pd.DataFrame) -> float:\n",
    "    # If the index is datetime-like and monotonic, infer typical timestep\n",
    "    idx = df.index\n",
    "    if isinstance(idx, pd.DatetimeIndex) and len(idx) >= 3:\n",
    "        diffs = idx.to_series().diff().dropna().dt.total_seconds() / 3600.0\n",
    "        if len(diffs) > 0:\n",
    "            dt = float(diffs.median())\n",
    "            # guard against weirdness\n",
    "            if np.isfinite(dt) and dt > 0 and dt < 24*7:\n",
    "                return dt\n",
    "    # Otherwise assume 1 row = 1 hour\n",
    "    return 1.0\n",
    "\n",
    "dt_hr = infer_timestep_hours(df)\n",
    "\n",
    "# =========================\n",
    "# Clean + select columns\n",
    "# =========================\n",
    "need_cols = [\n",
    "    \"plant_kw_per_ton\", \"plant_kw\", \"tons\",\n",
    "    \"pumping_kw_per_ton\", \"chiller_kw_per_ton\"\n",
    "]\n",
    "# tower fan per ton is optional\n",
    "if \"tower_fan_kw_per_ton\" in df.columns:\n",
    "    need_cols.append(\"tower_fan_kw_per_ton\")\n",
    "\n",
    "work = df[need_cols].replace([np.inf, -np.inf], np.nan).dropna().copy()\n",
    "\n",
    "# Optional load gate\n",
    "if MIN_TONS > 0:\n",
    "    work = work[work[\"tons\"] >= MIN_TONS].copy()\n",
    "\n",
    "# =========================\n",
    "# 1) Energy savings quantification\n",
    "# =========================\n",
    "bad = work[\"plant_kw_per_ton\"] > KW_PER_TON_BAD\n",
    "n_total = len(work)\n",
    "n_bad = int(bad.sum())\n",
    "\n",
    "# MWh for \"bad hours\"\n",
    "# If each row is dt_hr hours: MWh = sum(kW * hours) / 1000\n",
    "bad_mwh = float((work.loc[bad, \"plant_kw\"] * dt_hr).sum() / 1000.0)\n",
    "total_mwh = float((work[\"plant_kw\"] * dt_hr).sum() / 1000.0)\n",
    "pct_energy_bad = 100.0 * (bad_mwh / total_mwh) if total_mwh > 0 else np.nan\n",
    "pct_hours_bad = 100.0 * (n_bad / n_total) if n_total > 0 else np.nan\n",
    "\n",
    "print(\"=== Energy tied to inefficient operation ===\")\n",
    "print(f\"Timestep assumed: {dt_hr:.3f} h per row\")\n",
    "print(f\"Rows analyzed (after cleaning + load gate): {n_total:,}\")\n",
    "print(f\"Bad rows (plant kW/ton > {KW_PER_TON_BAD}): {n_bad:,} ({pct_hours_bad:.1f}% of hours)\")\n",
    "print(f\"Energy during bad rows: {bad_mwh:,.1f} MWh ({pct_energy_bad:.1f}% of total {total_mwh:,.1f} MWh)\")\n",
    "\n",
    "# =========================\n",
    "# 2) Subsystem blame assignment (within bad hours)\n",
    "# =========================\n",
    "bad_df = work.loc[bad].copy()\n",
    "\n",
    "# Build per-ton components\n",
    "comp_cols = [\"chiller_kw_per_ton\", \"pumping_kw_per_ton\"]\n",
    "if \"tower_fan_kw_per_ton\" in bad_df.columns:\n",
    "    comp_cols.append(\"tower_fan_kw_per_ton\")\n",
    "\n",
    "# If plant_kw_per_ton exists, use it; otherwise sum components\n",
    "plant_kpt = bad_df[\"plant_kw_per_ton\"].copy()\n",
    "\n",
    "# Avoid divide-by-zero / negative artifacts\n",
    "plant_kpt = plant_kpt.where(plant_kpt > 0, np.nan)\n",
    "bad_df = bad_df.loc[plant_kpt.dropna().index].copy()\n",
    "plant_kpt = plant_kpt.dropna()\n",
    "\n",
    "# Fractions of plant kW/ton\n",
    "frac = pd.DataFrame(index=bad_df.index)\n",
    "for c in comp_cols:\n",
    "    frac[c] = bad_df[c] / plant_kpt\n",
    "\n",
    "# Dominance classification\n",
    "labels = pd.Series(\"mixed/other\", index=bad_df.index)\n",
    "\n",
    "p_dom = frac[\"pumping_kw_per_ton\"] >= DOMINANCE_FRAC\n",
    "c_dom = frac[\"chiller_kw_per_ton\"] >= DOMINANCE_FRAC\n",
    "t_dom = frac[\"tower_fan_kw_per_ton\"] >= DOMINANCE_FRAC if \"tower_fan_kw_per_ton\" in frac.columns else pd.Series(False, index=bad_df.index)\n",
    "\n",
    "# Priority logic: if one dominates, tag it; if multiple dominate (rare), tag as mixed/other\n",
    "labels[p_dom & ~c_dom & ~t_dom] = \"pumping-dominated\"\n",
    "labels[c_dom & ~p_dom & ~t_dom] = \"chiller-dominated\"\n",
    "if \"tower_fan_kw_per_ton\" in frac.columns:\n",
    "    labels[t_dom & ~p_dom & ~c_dom] = \"tower-fan-dominated\"\n",
    "\n",
    "share = labels.value_counts(normalize=True).sort_values(ascending=False) * 100\n",
    "counts = labels.value_counts().sort_values(ascending=False)\n",
    "\n",
    "print(\"\\n=== Subsystem dominance among bad hours ===\")\n",
    "for k in counts.index:\n",
    "    print(f\"{k:>18}: {counts[k]:,} hours ({share[k]:.1f}%)\")\n",
    "\n",
    "# Also compute average component shares during bad hours (useful even when not \"dominated\")\n",
    "avg_frac = frac.clip(lower=-10, upper=10).mean() * 100\n",
    "print(\"\\nAverage share of plant kW/ton during bad hours (by component):\")\n",
    "for c in avg_frac.index:\n",
    "    print(f\"  {c:>22}: {avg_frac[c]:.1f}%\")\n",
    "\n",
    "# =========================\n",
    "# Simple visuals (optional)\n",
    "# =========================\n",
    "plt.figure(figsize=(7,4))\n",
    "plt.bar(counts.index, counts.values)\n",
    "plt.ylabel(\"Count (hours/rows)\")\n",
    "plt.title(f\"Bad hours breakdown (plant kW/ton > {KW_PER_TON_BAD})\")\n",
    "plt.xticks(rotation=20, ha=\"right\")\n",
    "plt.tight_layout()\n",
    "plt.close(\"all\")\n",
    "\n",
    "plt.figure(figsize=(6,4))\n",
    "plt.bar([c.replace(\"_kw_per_ton\",\"\") for c in avg_frac.index], avg_frac.values)\n",
    "plt.ylabel(\"Average share of plant kW/ton (%)\")\n",
    "plt.title(\"Average component contribution during bad hours\")\n",
    "plt.tight_layout()\n",
    "plt.close(\"all\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "injected": true
   },
   "source": [
    "### Energy Waste Quantification\n",
    "\n",
    "Flagging all hours where plant kW/ton exceeds 4.0 and summing their energy consumption provides a conservative estimate of the energy tied to inefficient operation. The subsystem \"blame assignment\" uses dominance analysis — if a single subsystem (chiller, pumping, or tower fans) accounts for more than 50% of the plant's kW/ton during a bad hour, that hour is attributed to that subsystem.\n",
    "\n",
    "The bar charts show:\n",
    "- **Hours breakdown**: Which subsystem is most often responsible for inefficiency\n",
    "- **Average component share**: How the plant's per-ton energy consumption splits across subsystems during inefficient periods\n",
    "\n",
    "This analysis directly informs the priority of corrective actions — addressing the dominant source of waste first yields the largest savings.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "27eac2cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bad hours plotted: 233\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# -------------------------------\n",
    "# CONFIG\n",
    "# -------------------------------\n",
    "KWTON_BAD = 4.0\n",
    "MIN_TONS = 20\n",
    "FLOW_FLOOR = 40.0        # suspected CW flow floor (m3/h)\n",
    "COLOR_BY = \"pumping_kw_per_ton\"  # or \"cw_pump_kw\"\n",
    "\n",
    "# -------------------------------\n",
    "# Prepare bad-hours subset\n",
    "# -------------------------------\n",
    "cols = [\n",
    "    \"tons\", \"cw_flow_m3h\", \"plant_kw_per_ton\",\n",
    "    \"pumping_kw_per_ton\", \"cw_pump_kw\"\n",
    "]\n",
    "cols = [c for c in cols if c in df.columns]\n",
    "\n",
    "d = (\n",
    "    df[cols]\n",
    "    .replace([np.inf, -np.inf], np.nan)\n",
    "    .dropna(subset=[\"tons\", \"cw_flow_m3h\", \"plant_kw_per_ton\"])\n",
    ")\n",
    "\n",
    "d = d[(d[\"plant_kw_per_ton\"] > KWTON_BAD) & (d[\"tons\"] >= MIN_TONS)]\n",
    "\n",
    "print(f\"Bad hours plotted: {len(d)}\")\n",
    "\n",
    "# -------------------------------\n",
    "# Scatter plot\n",
    "# -------------------------------\n",
    "plt.figure(figsize=(9,6))\n",
    "sc = plt.scatter(\n",
    "    d[\"tons\"],\n",
    "    d[\"cw_flow_m3h\"],\n",
    "    c=d[COLOR_BY],\n",
    "    s=35,\n",
    "    alpha=0.7\n",
    ")\n",
    "\n",
    "plt.axhline(FLOW_FLOOR, linestyle=\"--\", linewidth=1)\n",
    "plt.colorbar(sc, label=COLOR_BY.replace(\"_\", \" \"))\n",
    "\n",
    "plt.xlabel(\"Cooling load (tons)\")\n",
    "plt.ylabel(\"Condenser water flow (m³/h)\")\n",
    "plt.title(\"Bad hours only: CW flow vs tons\\n(color = pumping intensity)\")\n",
    "plt.grid(True, alpha=0.2)\n",
    "plt.tight_layout()\n",
    "plt.close(\"all\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "injected": true
   },
   "source": [
    "### CW Flow During Inefficient Hours\n",
    "\n",
    "Examining condenser water flow during inefficient hours reveals a potential minimum flow constraint. The horizontal dashed line at 40 m³/h suggests a CW pump minimum — even at very low loads, the condenser water flow doesn't drop below this floor. This fixed flow at low loads contributes to the pumping-dominated inefficiency seen in the classification above.\n",
    "\n",
    "---\n",
    "\n",
    "## Summary of Findings\n",
    "\n",
    "Across this seven-notebook diagnostic series, we identified three distinct operational faults in the chiller plant:\n",
    "\n",
    "1. **Low delta-T syndrome** — CHW temperature differential collapsed from ~6°C to ~4.2°C around early July, forcing higher pump flows and degrading chiller evaporator performance\n",
    "2. **Cooling tower degradation** — Approach temperature drifted upward by approximately 1.5°C over the 8-week period, increasing condenser pressure and chiller power consumption\n",
    "3. **Night-time fan control bias** — Tower fans operate at elevated power during overnight hours despite minimal cooling loads\n",
    "\n",
    "### Recommended Actions\n",
    "\n",
    "| Priority | Action | Expected Impact |\n",
    "|----------|--------|----------------|\n",
    "| 1 | Investigate and correct the cause of low CHW delta-T (likely a control valve or bypass issue) | Reduce pump energy by 20-30%, improve chiller efficiency |\n",
    "| 2 | Inspect and clean cooling tower fill; check fan belt tension and blade pitch | Recover 1-1.5°C of approach, reducing chiller power |\n",
    "| 3 | Implement load-based fan speed control with overnight setback | Eliminate unnecessary fan energy during low-load hours |\n",
    "| 4 | Review CW pump minimum flow setpoint | Right-size the flow floor to actual minimum condenser requirements |\n"
   ]
  }
 ]
}