Energy Prediction under Changed Demand Conditions: Robust Machine Learning Models and Input Feature Combinations

被引：1

作者：

Schranz, Thomas ^{[1
]}

Exenberger, Johannes ^{[1
]}

Legaard, Christian Mldrup ^{[2
]}

Drgona, Jan ^{[3
]}

Schweiger, Gerald ^{[1
]}

机构：

[1] Graz Univ Technol, Graz, Austria

[2] Aarhus Univ, Aarhus, Denmark

[3] Pacific Northwest Natl Lab, Richland, WA USA

来源：

PROCEEDINGS OF BUILDING SIMULATION 2021: 17TH CONFERENCE OF IBPSA | 2022年 / 17卷

关键词：

CONSUMPTION;

D O I：

10.26868/25222708.2021.30806

中图分类号：

学科分类号：

摘要：

Deciding on a suitable algorithm for energy demand prediction in a building is non-trivial and depends on the availability of data. In this paper we compare four machine learning models, commonly found in the literature, in terms of their generalization performance and in terms of how using different sets of input features affects accuracy. This is tested on a data set where consumption patterns differ significantly between training and evaluation because of the Covid-19 pandemic. We provide a hands-on guide and supply a Python framework for building operators to adapt and use in their applications.

引用

页码：3268 / 3275

页数：8

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