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This paper presents a solution to a predict then optimise problem which goalis to reduce the electricity cost of a university campus. The proposedmethodology combines a multi-dimensional time series forecast and a novelapproach to large-scale optimization. Gradient-boosting method is applied toforecast both generation and consumption time-series of the Monash universitycampus for the month of November 2020. For the consumption forecasts we employlog transformation to model trend and stabilize variance. Additionalseasonality and trend features are added to the model inputs when applicable.The forecasts obtained are used as the base load for the schedule optimisationof university activities and battery usage. The goal of the optimisation is tominimize the electricity cost consisting of the price of electricity and thepeak electricity tariff both altered by the load from class activities andbattery use as well as the penalty of not scheduling some optional activities.The schedule of the class activities is obtained through evolutionaryoptimisation using the covariance matrix adaptation evolution strategy and thegenetic algorithm. This schedule is then improved through local search bytesting possible times for each activity one-by-one. The battery schedule isformulated as a mixed-integer programming problem and solved by the Gurobisolver. This method obtains the second lowest cost when evaluated against 6other methods presented at an IEEE competition that all used mixed-integerprogramming and the Gurobi solver to schedule both the activities and thebattery use. The code and data used for the paper are publicly available.

Evolutionary scheduling of university activities based on consumption forecasts to minimise electricity costs