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One-time script used to generate data/price_curves.json
Generate 30 days of realistic ISO New England-style hourly price data.
Run: python data/generate_prices.py
Output: data/price_curves.json
"""
import json
import math
import random
random.seed(12345)
def generate_day(day_type: str = "weekday") -> list[float]:
"""Generate 24 hourly prices for one day."""
prices = []
for hour in range(24):
# Base time-of-use price
if hour < 6: # overnight off-peak
base = 0.035 + random.gauss(0, 0.005)
elif hour < 8: # morning ramp
base = 0.07 + random.gauss(0, 0.008)
elif hour < 12: # morning peak
base = 0.16 + random.gauss(0, 0.020) + (0.05 if day_type == "weekday" else 0)
elif hour < 14: # midday moderate
base = 0.10 + random.gauss(0, 0.012)
elif hour < 17: # afternoon
base = 0.12 + random.gauss(0, 0.015)
elif hour < 21: # evening super-peak
base = 0.22 + random.gauss(0, 0.025) + (0.08 if day_type == "weekday" else 0.02)
elif hour < 23: # evening wind-down
base = 0.09 + random.gauss(0, 0.010)
else: # late night
base = 0.04 + random.gauss(0, 0.006)
# Seasonal variation (summer = higher peaks)
season_mult = 1.0
prices.append(round(max(0.02, base * season_mult), 4))
return prices
# Generate 30 days: 22 weekdays + 8 weekend days
days = []
day_types = (["weekday"] * 5 + ["weekend"] * 2) * 5 # 5 weeks → 35 days → take 30
for i, dt in enumerate(day_types[:30]):
day_data = {
"day": i + 1,
"day_type": dt,
"prices_usd_per_kwh": generate_day(dt),
"source": "synthetic_iso_new_england_style",
"region": "ISONE",
"currency": "USD",
}
days.append(day_data)
output = {
"description": "30 days of synthetic ISO New England-style hourly electricity price data",
"units": "USD/kWh",
"hours_per_day": 24,
"days": days,
}
with open("price_curves.json", "w") as f:
json.dump(output, f, indent=2)
print(f"Generated {len(days)} days of price data -> data/price_curves.json")
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