Paper Conference

Proceedings of Building Simulation 2017: 15th Conference of IBPSA


Evaluation of Machine Learning Algorithms for Demand Response Potential Forecasting

Dimitrios-Stavros Kapetanakis1, Despoina Christantoni2, Eleni Mangina3, Donal P. Finn1
1School of Mechanical and Materials Engineering, University College Dublin (UCD)
2School of Electrical and Electronic Engineering, University College Dublin (UCD)
3School of Computer Science, University College Dublin (UCD)

Abstract: This paper focuses on the ability of machine learning algorithms to capture the demand response (DR) potential when forecasting the electrical demand of a commercial building. An actual sports-entertainment centre is utilised as a testbed, simulated with EnergyPlus, and the strategy followed during the DR event is the modification of the chiller water temperature of the cooling system. An artificial neural network (ANN) and a support vector machine (SVM) predictive model, are utilised to predict the DR potential of the building, due to the significant amount of execution time of the EnergyPlus model. The data-driven models are trained and tested based on synthetic databases. Results demonstrate that both ANN and SVM models can accurately predict the building electrical power demand for the scenarios without or with daily DR events, whereas both predictive models are not accurate in forecasting the electrical demand during the rebound effect.
Pages: 1667 - 1676