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

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

doi:10.1109/cec55065.2022.9870213

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Other1

Article1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Control Strategy of Central Air Conditioning using RF-NSGA-II

Yu,

Li,

Zhao

et al. 2024

2024 36th Chinese Control and Decision Conference (CCDC)

View full text Add to dashboard Cite

Control Strategy of Central Air Conditioning using RF-NSGA-II

Yu,

Li,

Zhao

et al. 2024

2024 36th Chinese Control and Decision Conference (CCDC)

View full text Add to dashboard Cite

Forecasting the Monash Microgrid for the IEEE-CIS Technical Challenge

Bean

2023

Energies

View full text Add to dashboard Cite

Effective operation of a microgrid depends critically on accurate forecasting of its components. Recently, internet forecasting competitions have been used to determine the best methods for energy forecasting, with some competitions having a special focus on microgrids and COVID-19 energy-use forecasting. This paper describes forecasting for the IEEE Computational Intelligence Society 3rd Technical Challenge, which required predicting solar and building loads of a microgrid system at Monash University for the month of November 2020. The forecast achieved the lowest error rate in the competition. We review the literature on recent energy forecasting competitions and metrics and explain how the solution drew from top-ranked solutions in previous energy forecasting competitions such as the Global Energy Forecasting Competition series. The techniques can be reapplied in other forecasting endeavours, while approaches to some of the time-series forecasting are more ad hoc and specific to the competition. Novel thresholding approaches were used to improve the quality of the input data. As the training and evaluation phase of the challenge occurred during COVID-19 lockdown and reopening, the building demand was subject to pandemic-related effects. Finally, we assess other data sources which would have improved the model forecast skill such as data from different numerical weather prediction (NWP) models, solar observations, and high-resolution price and demand data in the vicinity of the campus.

show abstract

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

Abstract: with details of the nature of the infringement. We will investigate the claim and if justified, we will take the appropriate steps.

Cited by 2 publications

References 11 publications

Control Strategy of Central Air Conditioning using RF-NSGA-II

Control Strategy of Central Air Conditioning using RF-NSGA-II

Forecasting the Monash Microgrid for the IEEE-CIS Technical Challenge

Contact Info

Product

Resources

About