Evaluation of Machine Learning Algorithms for Demand Response Potential Forecasting

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Title: Evaluation of Machine Learning Algorithms for Demand Response Potential Forecasting
Authors: Kapetanakis, Dimitrios-StavrosChristantoni, DespoinaMangina, EleniFinn, Donal
Permanent link: http://hdl.handle.net/10197/11546
Date: 9-Aug-2017
Online since: 2020-09-08T13:49:45Z
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.
Type of material: Conference Publication
Publisher: IBPSA
Keywords: Building electrical demandDemand responseData-driven modelsNeural networksSupport vector machines
DOI: 10.26868/25222708.2017.441
Other versions: https://www.buildup.eu/en/events/building-simulation-bs2017-2017
Language: en
Status of Item: Peer reviewed
Is part of: Barnaby, C.S. and Wetter, M. (eds.). Building Simulation 2017 : Proceedings of the 15th IBPSA Conference
Conference Details: The 15th International Building Performance Simulation Association Conference (Building Simulation 2017), San Francisco, United States of America, 7-9 August 2017
ISBN: 978-1-7750520-0-5
Appears in Collections:Mechanical & Materials Engineering Research Collection
Computer Science Research Collection
Electrical and Electronic Engineering Research Collection

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