Predictive modelling for solar thermal energy systems: A comparison of support vector regression, random forest, extra trees and regression trees

Predictive modelling for solar thermal energy systems: A comparison of support vector regression, random forest, extra trees and regression trees

Predictive analytics play an important role in the management of decentralised energy systems. Prediction models of uncontrolled variables (e.g., renewable energy sources generation, building energy consumption) are required to optimally manage electrical and thermal grids, making informed decisions...

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Bibliographic Details
Main Authors: Ahmad, M.W (Author), Reynolds, J. (Author), Rezgui, Y. (Author)
Format: Article
Language:English
Published: Elsevier Ltd 2018
Subjects:
Artificial intelligence
Building energy consumption
Computationally efficient
Decision trees
Energy utilization
Ensemble algorithms
Extra trees
Extra-trees
Fault detection
Fault detection and diagnosis
Forecasting
Learning systems
Mean square error
Predictive analytics
Random forest
Random forests
Regression analysis
Solar energy
Solar heating
Solar thermal energy
Solar thermal energy systems
Statistical tests
Support vector regression (SVR)
Thermal energy
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