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Version 1

Ranking Building Design and Operation Parameters for Residential Heating Demand Forecasting with Machine Learning - ASSOCIATED RESEARCH DATA

Published:24 October 2023|Version 1|DOI:10.17632/pybn6gb2m6.1
Contributors:
, Felicia Agatha Satriya, Jon Terés-Zubiaga,
, Unai Bermejo

Description

The results of the 24 machine-learning models are presented in this dataset. The spreadsheets display the results of the three algorithms, both with optimized parameters and with default settings. The first spreadsheet pertains to the Random Forest results, the second to XGBoost, and the third to Extra Trees.

Institutions

Institutions

Universidad del Pais Vasco

Categories

Energy Engineering, Machine Learning, Building Heating

Funders

CaixaBank

Spain

LCF/PR/SR20/52550013

Licence

Creative Commons Attribution 4.0 International

Version 2

Ranking Building Design and Operation Parameters for Residential Heating Demand Forecasting with Machine Learning - ASSOCIATED RESEARCH DATA

Published:20 February 2024|Version 2|DOI:10.17632/pybn6gb2m6.2
Contributors:
, Felicia Agatha Satriya,
,
,

Description

The results of the 24 machine-learning models are presented in this dataset. The spreadsheets display the results of the three algorithms, both with optimized parameters and with default settings. The first spreadsheet pertains to the Random Forest results, the second to XGBoost, and the third to Extra Trees.

Institutions

Institutions

Universidad del Pais Vasco

Categories

Energy Engineering, Machine Learning, Building Heating

Funders

CaixaBank

Spain

LCF/PR/SR20/52550013

Licence

Creative Commons Attribution 4.0 International