  In this report, we provide a technical sequence on tackling the solar PV and
demand forecast as well as optimal scheduling problem proposed by the IEEE-CIS
3rd technical challenge on predict + optimize for activity and battery
scheduling. Using the historical data provided by the organizers, a simple
pre-processing approach with a rolling window was used to detect and replace
invalid data points. Upon filling the missing values, advanced time-series
forecasting techniques, namely tree-based methods and refined motif discovery,
were employed to predict the baseload consumption on six different buildings
together with the power production on their associated solar PV panels. An
optimization problem is then formulated to use the predicted values and the
wholesale electricity prices to create a timetable for a set of activities,
including the scheduling of lecture theatres and battery charging and
discharging operation, for one month ahead. The valley-filling optimization was
done across all the buildings with the objective of minimizing the total energy
cost and achieving net-zero imported power from the grid.
