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T2.3 · Grid Outage Forecaster + Appliance Prioritizer
Data Generator — reproducible synthetic dataset
Run: python generate_data.py
Outputs: grid_history.csv, appliances.json, businesses.json
"""
import numpy as np
import pandas as pd
import json
from datetime import datetime, timedelta
SEED = 42
np.random.seed(SEED)
# ── 1. GRID HISTORY ──────────────────────────────────────────────────────────
def sigmoid(x):
return 1 / (1 + np.exp(-x))
def generate_grid_history(days=180, seed=SEED):
np.random.seed(seed)
start = datetime(2024, 1, 1, 0, 0)
records = []
for d in range(days):
date = start + timedelta(days=d)
week = d // 7
# Rainy season: Apr-May, Oct-Nov (months 4,5,10,11)
month = date.month
rainy = month in [4, 5, 10, 11]
for h in range(24):
ts = date + timedelta(hours=h)
# Load: two peaks (morning ~8, evening ~19), weekly seasonality
morning_peak = 80 * np.exp(-0.5 * ((h - 8) / 2.5) ** 2)
evening_peak = 100 * np.exp(-0.5 * ((h - 19) / 2.0) ** 2)
base_load = 40
weekday_boost = 15 if date.weekday() < 5 else -10
rainy_noise = np.random.normal(0, 12 if rainy else 4)
load_mw = max(10, base_load + morning_peak + evening_peak +
weekday_boost + rainy_noise)
# Weather
temp_c = 22 + 6 * np.sin(2 * np.pi * (h - 14) / 24) + \
np.random.normal(0, 1.5) + (3 if rainy else 0)
humidity = 60 + (20 if rainy else 0) + 10 * np.sin(2 * np.pi * h / 24) + \
np.random.normal(0, 5)
humidity = np.clip(humidity, 30, 99)
wind_ms = max(0, np.random.exponential(3) + (2 if rainy else 0))
rain_mm = np.random.exponential(3) if (rainy and np.random.rand() < 0.4) else 0.0
# Outage probability: logistic model
load_lag1 = load_mw * (1 + np.random.normal(0, 0.02)) # approx lag
a0, a1, a2, a3 = -3.5, 0.015, 0.08, 0.04
log_odds = a0 + a1 * load_lag1 + a2 * rain_mm + a3 * (1 if h in range(7, 22) else 0)
p_outage = sigmoid(log_odds)
p_outage = np.clip(p_outage + (0.02 if rainy else 0), 0.01, 0.35)
outage = int(np.random.rand() < p_outage)
# Duration: LogNormal if outage
duration_min = 0
if outage:
duration_min = int(np.random.lognormal(mean=np.log(90), sigma=0.6))
duration_min = max(5, min(duration_min, 480))
records.append({
"timestamp": ts.strftime("%Y-%m-%d %H:%M:%S"),
"load_mw": round(load_mw, 2),
"temp_c": round(temp_c, 2),
"humidity": round(humidity, 2),
"wind_ms": round(wind_ms, 2),
"rain_mm": round(rain_mm, 2),
"outage": outage,
"duration_min": duration_min,
})
df = pd.DataFrame(records)
df.to_csv("grid_history.csv", index=False)
print(f"✓ grid_history.csv {len(df)} rows outage_rate={df.outage.mean():.3f}")
return df
# ── 2. APPLIANCES ─────────────────────────────────────────────────────────────
APPLIANCES = [
{"id": "fridge", "name": "Commercial Refrigerator", "category": "critical",
"watts_avg": 350, "start_up_spike_w": 700, "revenue_if_running_rwf_per_h": 2500},
{"id": "hair_dryer", "name": "Hair Dryer (2×)", "category": "critical",
"watts_avg": 2400, "start_up_spike_w": 2500, "revenue_if_running_rwf_per_h": 3000},
{"id": "clippers", "name": "Electric Clippers (3×)", "category": "critical",
"watts_avg": 120, "start_up_spike_w": 150, "revenue_if_running_rwf_per_h": 2000},
{"id": "water_pump", "name": "Water Pump", "category": "critical",
"watts_avg": 750, "start_up_spike_w": 1500, "revenue_if_running_rwf_per_h": 1500},
{"id": "lights", "name": "LED Lights", "category": "critical",
"watts_avg": 80, "start_up_spike_w": 80, "revenue_if_running_rwf_per_h": 1000},
{"id": "air_con", "name": "Air Conditioner", "category": "comfort",
"watts_avg": 1500, "start_up_spike_w": 3000, "revenue_if_running_rwf_per_h": 800},
{"id": "fan", "name": "Standing Fan", "category": "comfort",
"watts_avg": 75, "start_up_spike_w": 80, "revenue_if_running_rwf_per_h": 400},
{"id": "tv", "name": "TV / Display Screen", "category": "comfort",
"watts_avg": 150, "start_up_spike_w": 160, "revenue_if_running_rwf_per_h": 200},
{"id": "music", "name": "Music System", "category": "luxury",
"watts_avg": 200, "start_up_spike_w": 220, "revenue_if_running_rwf_per_h": 100},
{"id": "neon_sign", "name": "Neon Sign", "category": "luxury",
"watts_avg": 60, "start_up_spike_w": 65, "revenue_if_running_rwf_per_h": 50},
]
# ── 3. BUSINESSES ─────────────────────────────────────────────────────────────
BUSINESSES = [
{
"id": "salon",
"name": "Beauty Salon (Kigali)",
"archetype": "salon",
"description": "4-chair salon, open 07:00–20:00, 6 days/week",
"generator_kva": 2.0,
"appliance_ids": ["hair_dryer", "clippers", "lights", "fan", "tv", "music", "neon_sign"],
"peak_hours": [8, 9, 10, 15, 16, 17, 18],
"monthly_revenue_rwf": 1_800_000,
},
{
"id": "cold_room",
"name": "Cold Room / Butchery",
"archetype": "cold_room",
"description": "Meat storage + retail, 05:00–22:00, 7 days",
"generator_kva": 3.5,
"appliance_ids": ["fridge", "lights", "water_pump", "fan", "tv"],
"peak_hours": [5, 6, 7, 17, 18, 19, 20],
"monthly_revenue_rwf": 2_500_000,
},
{
"id": "tailor",
"name": "Tailor Shop",
"archetype": "tailor",
"description": "3 sewing machines + ironing, 08:00–18:00, 6 days",
"generator_kva": 1.5,
"appliance_ids": ["lights", "fan", "music", "tv"],
"peak_hours": [9, 10, 11, 14, 15, 16],
"monthly_revenue_rwf": 900_000,
},
]
def generate_appliance_files():
with open("appliances.json", "w") as f:
json.dump(APPLIANCES, f, indent=2)
print(f"✓ appliances.json {len(APPLIANCES)} appliances")
with open("businesses.json", "w") as f:
json.dump(BUSINESSES, f, indent=2)
print(f"✓ businesses.json {len(BUSINESSES)} businesses")
if __name__ == "__main__":
generate_grid_history()
generate_appliance_files()
print("\nAll data files generated successfully.")
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