Prediction of Residential Building Demand Response Potential Using Data-Driven Techniques

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Title: Prediction of Residential Building Demand Response Potential Using Data-Driven Techniques
Authors: Kapetanakis, Dimitrios-StavrosNeu, OlivierFinn, Donal
Permanent link: http://hdl.handle.net/10197/11545
Date: 9-Aug-2017
Online since: 2020-09-08T13:22:12Z
Abstract: This paper is concerned with the evaluation of the ability of data-driven predictive models to capture the demand response potential in residential buildings. A mid-floor apartment with an air to water heat pump for space heating, utilised as an archetype dwelling, is simulated using EnergyPlus. The research is focused on forecasting the electrical demand from the heating load for the coldest month of the year, considering two types of DR events, load reduction and load increase. After the generation of the synthetic database, an artificial neural network model and a support vector machine model are examined regarding their ability to predict the electrical demand from heating loads.
Type of material: Conference Publication
Publisher: IBPSA
Copyright (published version): 2017 the Authors
Keywords: Renewable energy resourcesBuilding electrical demandDemand responseHeating systemsData-driven models
DOI: 10.26868/25222708.2017.439
Other versions: http://buildingsimulation2017.org/
http://www.ibpsa.org/?page_id=962
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, San Francisco, United States of America, 7-9 August 2017
ISBN: 978-1-7750520-0-5
Appears in Collections:Mechanical & Materials Engineering Research Collection

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