  This report provides a description of the methodology I used in the IEEE-CIS
3rd Technical Challenge.
  For the forecast, I used a quantile regression forest approach using the
solar variables provided by the Bureau of Meterology of Australia (BOM) and
many of the weather variables from the European Centre for Medium-Range Weather
Forecasting (ECMWF).
  Groups of buildings and all of the solar instances were trained together as
they were observed to be closely correlated over time. Other variables used
included Fourier values based on hour of day and day of year, and binary
variables for combinations of days of the week.
  The start dates for the time series were carefully tuned based on phase 1 and
cleaning and thresholding was used to reduce the observed error rate for each
time series.
  For the optimization, a four-step approach was used using the forecast
developed. First, a mixed-integer program (MIP) was solved for the recurring
and recurring plus once-off activities, then each of these was extended using a
mixed-integer quadratic program (MIQP).
  The general strategy was chosen from one of two ("array" from the "array" and
"tuples" approaches) while the specific step improvement strategy was chosen
from one of five ("no forced discharge").
