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 BermejoDescription
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.
Files
Institutions
Universidad del Pais Vasco
Categories
Energy Engineering, Machine Learning, Building Heating
Funding
'la Caixa' Foundation
LCF/PR/SR20/52550013