Apr 8, 2019 · This paper presented the MVO-SVM approach for predicting energy consumption in residential buildings.

This paper optimization the parameters of a support vector machine (SVM) using a multi-verse optimizer (MVO) without the grid search algorithm, ...

This paper presented the MVO-SVM approach for predicting energy consumption in residential buildings. The proposed approach examined a UCI repository dataset.

The present study aims to predict energy consumption with higher accuracy and lower run time. We optimize the parameters of a support vector machine (SVM) using ...

Estimates of residential building energy consumption using a multi-verse optimizer-based support vector machine with k-fold cross-validation.

Estimates of residential building energy consumption using a multi-verse optimizer-based support vector machine with k-fold cross-validation. H Tabrizchi, MM ...

306 Citations · Estimates of residential building energy consumption using a multi-verse optimizer-based support vector machine with k-fold cross-validation.

Apr 25, 2024 · Estimates of residential building energy consumption using a multi-verse optimizer-based support vector machine with k-fold cross-validation.

Jun 1, 2018 · Estimates of energy consumption in China using a self-adaptive multi-verse optimizer-based support vector machine with rolling cross-validation.

Estimates of residential building energy consumption using a multi ... optimize the support vector machine and to foresee residential building energy consumption.