Input variable selection for thermal load predictive models of commercial buildings

•Selection of input variables performing linear and monotonic correlation analysis.•Accuracy of predictive models maintained at the same level with selected variables.•Complexity of predictive models reduced with selected variables as inputs. Forecasting of commercial building thermal loads can be a...

Full description

Saved in:
Bibliographic Details
Published in:Energy and buildings 2017-02, Vol.137, p.13-26
Main Authors: Kapetanakis, Dimitrios-Stavros, Mangina, Eleni, Finn, Donal P.
Format: Article
Language:English
Subjects:
Building thermal loads
Buildings
Climate
Climatology
Commercial buildings
Commercial real estate
Computer simulation
Data analysis
Data processing
Datasets
Energy consumption
Energy management
Forecasting
Historical buildings
Input selection
Performance prediction
Prediction models
Predictive model
Simulation
Thermal analysis
Weather
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•Selection of input variables performing linear and monotonic correlation analysis.•Accuracy of predictive models maintained at the same level with selected variables.•Complexity of predictive models reduced with selected variables as inputs. Forecasting of commercial building thermal loads can be achieved using data from Building Energy Management (BEM) systems. Experience in building load prediction using historical data has shown that data analysis is a key factor in order to produce accurate results. This paper examines the selection of appropriate input variables, for data-driven predictive models, from wider datasets obtained from BEM systems sensors, as well as from weather data. To address the lack of available complete datasets from actual commercial buildings BEM systems, detailed representation of reference buildings using EnergyPlus were implemented. Different types of commercial buildings in various climates are examined to investigate the existence of patterns in the selection of input variables. Data analysis of the simulated results is used to detect the correlation between thermal loads and possible input variables. The selection process is validated by comparing the performance of predictive models when the full or the pre-selected set of variables is introduced as inputs.
ISSN:0378-7788
1872-6178
DOI:10.1016/j.enbuild.2016.12.016