Feature Assessment in Data-Driven Models for Unlocking Building Energy Flexibility

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Title: Feature Assessment in Data-Driven Models for Unlocking Building Energy Flexibility
Authors: Kathirgamanathan, AnjukanDe Rosa, MattiaMangina, EleniFinn, Donal
Permanent link: http://hdl.handle.net/10197/11556
Date: 4-Sep-2019
Online since: 2020-09-08T15:17:58Z
Abstract: Data-driven approaches are playing an increased role in building automation. This can, in part, be attributed to building operation and energy management system data becoming more readily accessible. A particular application is models to allow predictive control harnessing building energy flexibility, which is of interest to different stakeholders including; energy utilities, aggregators and end-users. Given the possibility of thousands of data features, feature selection becomes a critical part of the model development process. This paper considers various filter, wrapper and embedded methods applied in conjunction with three predictors in addressing the problem of constructing a suitable data-driven model to facilitate predictive control and provision of energy flexibility in a large commercial building. The feature selection algorithms are generally shown to significantly reduce model evaluation time and, in some cases, increase model accuracy. A random forest model with embedded feature selection was found to be the optimal solution in terms of model accuracy.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: IBPSA
Keywords: Renewable energy resourcesDemand side managementFeature selection algorithms
Other versions: http://buildingsimulation2019.org/
Language: en
Status of Item: Peer reviewed
Is part of: Corrado, V. and Gasparella, A. (eds.). Proceedings of Building Simulation 2019: 16th Conference of IBPSA
Conference Details: The 16th Building Performance Simulation Association International Conference and Exhibition (Building Simulation 2019), Rome, Italy, 2-4 September 2019
Appears in Collections:Mechanical & Materials Engineering Research Collection
Computer Science Research Collection
Energy Institute Research Collection

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