Interval forecasting for heating load using support vector regression and error correcting Markov chains

Zhang, Yongming; Wei-gui, QI

doi:10.1109/icmlc.2009.5212405

Cited by 5 publications

(1 citation statement)

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“…However, the information about original and selected features are missing. The further improvement of similar SVM based cooling load prediction has been demonstrated using a fuzzy C-mean algorithm for clustering samples (Xuemei et al 2010), simulated annealing particle swarm optimisation to prevent premature convergence and Markov chains to the farther forecast of the interval after primitive prediction (Zhang and Qi 2009).…”

Section: Support Vector Machinementioning

confidence: 99%

Machine learning for estimation of building energy consumption and performance: a review

et al. 2018

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Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy efficiency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most effective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy efficiency at a very early design stage. On the other hand,efficient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, artificial intelligence (AI) in general and machine learning (ML) techniques in specific terms have been proposed for forecasting of building energy consumption and performance. This paper provides a substantial review on the four main ML approaches including artificial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance.

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Section: Support Vector Machinementioning

confidence: 99%