DS-10 · Forecasting the grid

Can you forecast next-day hourly electricity demand for the PJM region better than a "same hour last week" baseline, and where does the model break?

PJM Data Miner hourly load metered feed (https://dataminer2.pjm.com/feed/hrl_load_metered). NOAA temperature (https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily).

Demand is driven by temperature non-linearly and in both directions — heating below, cooling above, flat in between. Holidays behave like Sundays and wreck a naive weekly baseline. Daylight-saving transitions produce a duplicated and a missing hour that silently corrupt joins.

An hourly day-ahead forecast beating the seasonal-naive baseline, with a temperature response curve the student can show and explain, and holiday and DST handling documented.

CT: recognizing a U-shaped relationship and not forcing a line through it. CR: encoding calendar effects. CO: splitting seasons across a pair and comparing. CM: a chart that makes the temperature response obvious. CZ: demand forecasting errors cause either blackouts or wasted generation.

A utility analyst, energy engineer, or data scientist reviews feature design at Checkpoint 1 and error analysis at Checkpoint 2. School-supervised.

R1: horizon and region fixed. R2: DST and holidays handled explicitly. R3: seasonal-naive baseline present. R4: worst errors located in time and explained. R5: the temperature curve is legible. R6: notes the asymmetric cost of under-forecasting.

(a) A linear temperature term makes the model worse in both summer and winter and the student concludes weather does not matter. (b) The DST duplicate hour shifts a whole year's join by one hour and the model looks mysteriously bad.