Skip to main navigation
Skip to search
Skip to main content
Teesside University's Research Portal Home
Search content at Teesside University's Research Portal
Home
Profiles
Research units
TeesRep
Student theses
Projects
Datasets
Equipment
Press/Media
Tuning machine learning models for prediction of building energy loads
Saleh Seyedzadeh
, Farzad Rahimian
, Parag Rastogi
, Ivan Glesk
SCEDT Engineering
Centre for Sustainable Engineering
Research output
:
Contribution to journal
›
Article
›
peer-review
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Tuning machine learning models for prediction of building energy loads'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Machine Learning Models
100%
Building Energy Loads
100%
Sensitivity Analysis
50%
Popular
25%
Informed Decision-making
25%
Model Optimization
25%
Time Complexity
25%
Historical Data
25%
Model Complexity
25%
Machine Learning Techniques
25%
Computational Cost
25%
Model Fitting
25%
Cross-validation Method
25%
Building Cooling Load
25%
Load Carrying
25%
Variable Model
25%
Cooling Load
25%
EnergyPlus
25%
Building Energy Data
25%
Heating Load
25%
Grid Search
25%
Ecotect
25%
Building Heating Load
25%
Fast Model
25%
Engineering
Energy Building
100%
Learning System
100%
Cooling Load
50%
Thermal Load
50%
Bridging
25%
Model Parameter
25%
Computational Cost
25%
Simulation Tool
25%
Historical Data
25%
Research Work
25%
Retro-Fitting
25%
Relative Importance
25%
Machine Learning Technique
25%
Great Deal
25%
Grid Search
25%
EnergyPlus
25%
Building Heating
25%