Evolutionary Scheduling of University Activities Based on Consumption Forecasts to Minimise Electricity Costs

Julian Ruddick, Evgenii Genov, Luis Ramirez Camargo, Thierry Coosemans, Maarten Messagie

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

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 available1.

Original languageEnglish
Title of host publication2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781665467087
DOIs
Publication statusPublished - 18 Jul 2022
Externally publishedYes
Event2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Padua, Italy
Duration: 18 Jul 202223 Jul 2022

Publication series

Name2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings

Conference

Conference2022 IEEE Congress on Evolutionary Computation, CEC 2022
Country/TerritoryItaly
CityPadua
Period18/07/2223/07/22

Keywords

  • covariance matrix adaptation evolution strategy (CMA-ES)
  • demand response
  • evolutionary algorithms
  • evolutionary scheduling
  • genetic algorithm
  • load forecasting
  • mixed-integer programming

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