  This paper presents a solution to a predict then optimise problem which goal
is to reduce the electricity cost of a university campus. The proposed
methodology combines a multi-dimensional time series forecast and a novel
approach to large-scale optimization. Gradient-boosting method is applied to
forecast both generation and consumption time-series of the Monash university
campus for the month of November 2020. For the consumption forecasts we employ
log transformation to model trend and stabilize variance. Additional
seasonality and trend features are added to the model inputs when applicable.
The forecasts obtained are used as the base load for the schedule optimisation
of university activities and battery usage. The goal of the optimisation is to
minimize the electricity cost consisting of the price of electricity and the
peak electricity tariff both altered by the load from class activities and
battery use as well as the penalty of not scheduling some optional activities.
The schedule of the class activities is obtained through evolutionary
optimisation using the covariance matrix adaptation evolution strategy and the
genetic algorithm. This schedule is then improved through local search by
testing possible times for each activity one-by-one. The battery schedule is
formulated as a mixed-integer programming problem and solved by the Gurobi
solver. This method obtains the second lowest cost when evaluated against 6
other methods presented at an IEEE competition that all used mixed-integer
programming and the Gurobi solver to schedule both the activities and the
battery use. The code and data used for the paper are publicly available.
