Support vector regression for predicting building energy consumption in southern China
Authors:
- Zhitong MaZMZhitong Ma
- Cantao YeCYCantao Ye
- Weibin MaWMWeibin Ma
Open Access
- 1 February 2019
- journal article
- Published by Elsevier in Energy Procedia
- Vol. 158, 3433-3438
Abstract
No abstract availableKeywords
This publication has 10 references indexed in Scilit:
- A relevant data selection method for energy consumption prediction of low energy building based on support vector machineEnergy and Buildings, 2017
- Time series forecasting for building energy consumption using weighted Support Vector Regression with differential evolution optimization techniqueEnergy and Buildings, 2016
- Prediction of building energy consumption using an improved real coded genetic algorithm based least squares support vector machine approachEnergy and Buildings, 2015
- Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracyApplied Energy, 2014
- Hybrid Predictive Models for Accurate Forecasting in PV SystemsEnergies, 2013
- An annual load forecasting model based on support vector regression with differential evolution algorithmApplied Energy, 2012
- Prediction model of annual energy consumption of residential buildings2010
- Annual electricity consumption forecasting by neural network in high energy consuming industrial sectorsEnergy Conversion and Management, 2008
- Climatic influences on solar modelling in ChinaRenewable Energy, 2008
- Applying support vector machines to predict building energy consumption in tropical regionEnergy and Buildings, 2005