Understanding Degree Days

The degree day system predicts heating fuel consumption based on outdoor temperature. It's commonly used for automatic oil delivery scheduling to estimate when a customer will need a refill. 
 
 What is a degree day? 
 A degree day measures heating demand: 
 
 Heating Degree Day (HDD) : Measures how cold it is relative to a base temperature (usually 65°F) 
 Calculation : For each day, HDD = max(0, Base Temp - Average Outdoor Temp) 
 Accumulation : Sum HDDs over time to estimate fuel consumption 
 
 Example : 
 
 Base temperature: 65°F 
 Average outdoor temperature: 40°F 
 HDD for that day: 65 - 40 = 25 HDD 
 
 If it takes 5 HDD to burn 1 gallon of oil (K-factor = 5), then after 100 accumulated HDD, the customer has used approximately 20 gallons. 
 
 Key formulas 
 Daily heating degree days 
 $$
HDD_{day} = \max(0, T_{base} - T_{avg})
$$ 
 Where: 
 
 $T_{base}$ = Base temperature (typically 65°F) 
 $T_{avg}$ = Average outdoor temperature for the day 
 
 Accumulated degree days 
 $$
HDD_{accumulated} = \sum_{i=1}^{n} HDD_i
$$ 
 Sum all daily HDDs since the last delivery. 
 K-factor (consumption rate) 
 The K-factor represents the relationship between degree days and gallons consumed: 
 $$
K = \frac{HDD}{gallons}
$$ 
 Example : If a customer used 200 gallons over 1000 HDD, then: 
 $$
K = \frac{1000}{200} = 5.0
$$ 
 So it takes 5 HDD to consume 1 gallon. 
 Estimating gallons used 
 $$
gallons_{used} = \frac{HDD_{accumulated}}{K}
$$ 
 Remaining fuel estimate 
 $$
remaining = capacity - gallons_{used}
$$ 
 Where capacity is the effective tank capacity (total capacity minus a reserve buffer). 
 Triggering a delivery 
 Dispatch a delivery when: 
 $$
remaining \leq reserve_{threshold}
$$ 
 Typically, reserve threshold is 25-30% of capacity to avoid run-outs. 
 
 Configuration steps 
 1. Set base temperature 
 
 Standard : 65°F for most residential heating 
 Adjustments : Some buildings may use 60°F or 68°F based on insulation, thermostat settings, or climate 
 Configuration : Set at the service or system level 
 
 2. Determine K-factor 
 Initial K-factor (before delivery history): 
 
 Use industry defaults: 4.0–6.0 for typical homes 
 Adjust based on building type:
 
 Well-insulated modern homes: 6.0–8.0 (higher K = less consumption per HDD) 
 Older, poorly insulated: 3.0–4.5 
 Commercial buildings: Varies widely; consult historical data or energy audit 
 
 
 
 Computed K-factor (after deliveries): 
 
 After 2-3 deliveries, compute K-factor from actual consumption:
 
 Gallons delivered = amount filled 
 HDD accumulated = sum of degree days between deliveries 
 $K = \frac{HDD}{gallons}$ 
 
 
 Update the service's K-factor with the computed value for better accuracy 
 
 3. Initialize the system 
 On the first delivery (or when setting up): 
 
 Last delivery date : Date of most recent fill 
 Last delivery amount : Gallons delivered 
 Starting tank level : Estimated gallons remaining before the delivery (if known) 
 
 The system will start accumulating degree days from the last delivery date. 
 4. Set minimum days between deliveries 
 Prevent too-frequent deliveries by setting a minimum interval (e.g., 14 or 21 days). Even if degree day projections suggest a delivery is needed, the system will wait until the minimum interval passes. 
 5. Set trigger thresholds 
 Define when to dispatch: 
 
 Reserve threshold : Gallons or percentage remaining (e.g., 25% or 75 gallons for a 275-gallon tank) 
 Lookahead days : Optional; trigger delivery if projection shows run-out within X days 
 
 
 Example scenario 
 
 Customer : Residential, 275-gallon oil tank 
 Base temp : 65°F 
 K-factor : 5.0 (initial estimate) 
 Last delivery : January 1, filled 200 gallons 
 Effective capacity : 250 gallons (reserve 25 gallons) 
 Reserve threshold : 75 gallons 
 
 Degree day accumulation (simplified): 
 
 
 Jan 1–15: 300 HDD 
 
 
 Estimated gallons used: 300 / 5 = 60 gallons 
 
 
 Remaining: 250 - 60 = 190 gallons (above threshold, no delivery yet) 
 
 
 Jan 16–31: Another 300 HDD (total 600 HDD) 
 
 
 Estimated gallons used: 600 / 5 = 120 gallons 
 
 
 Remaining: 250 - 120 = 130 gallons (still above 75-gallon threshold) 
 
 
 Feb 1–15: Another 300 HDD (total 900 HDD) 
 
 
 Estimated gallons used: 900 / 5 = 180 gallons 
 
 
 Remaining: 250 - 180 = 70 gallons ( below 75-gallon threshold ) 
 
 
 Trigger delivery 
 
 
 
 Degree day data sources 
 
 National Weather Service (NWS) : Historical and forecast degree days 
 NOAA : Free weather data APIs 
 Third-party services : Commercial degree day providers with regional accuracy 
 Internal tracking : Some systems calculate degree days from local weather station data 
 
 Your system may automatically pull degree day data but at any time you can update or modify it. 
 
 Improving accuracy 
 We Recompute K-factor regularly 
 
 After each delivery, our system automatically recalculates K-factor and uses rolling averages of the last 3-5 deliveries to smooth out anomalies (e.g., vacations, extreme weather). 
 
 Adjust for customer behavior 
 
 Vacation adjustments : If a customer is away, consumption drops; this should be noted within the customer account or routing notes to avoid under-delivery 
 Building changes : New insulation, windows, or equipment can alter K-factor significantly 
 
 Combine with monitors 
 
 If a tank monitor is installed, use both degree day projections and real-time level data 
 Degree day provides a forecast; monitor provides ground truth 
 If monitor reading diverges from degree day estimate, adjust K-factor or investigate consumption anomalies 
 
 See: Monitoring Guide 
 
 Troubleshooting common issues 
 Projections are too conservative (deliveries too frequent) 
 
 Cause : K-factor is too low (overestimating consumption) 
 Fix : Increase K-factor based on actual delivery history 
 
 Projections are too aggressive (customer runs out) 
 
 Cause : K-factor is too high (underestimating consumption) 
 Fix : Decrease K-factor; consider lowering reserve threshold 
 
 Degree days don't match consumption 
 
 Cause : Weather data source is not local enough, or customer behavior changed 
 Fix : Use more localized weather data by changing the weather location within the admin settings. 
 
 Sudden change in usage 
 
 Cause : Equipment failure, thermostat adjustment, occupancy change, or building modification 
 Fix : Investigate and adjust K-factor or note the anomaly; monitor closely 
 
 
 Related guides 
 
 Window Strategies — Add time-of-day constraints to degree day deliveries 
 Calendar Scheduling — Combine recurring schedules with degree day 
 Monitoring — Use monitors to validate degree day estimates 
 Add Services — Apply degree day strategy to services 
 Adding Equipment — Track tank capacity for accurate projections