Building Cooling Load Forecasting Model Based on LS-SVM

Li, Xuemei; Lü, Jinhu; Ding, Lixing; Xu, Gang; Li, Jibin

doi:10.1109/apcip.2009.22

Cited by 23 publications

(9 citation statements)

References 6 publications

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“…Hou & Lian (Hou and Lian 2009) examine the accuracy of SVM with an autoregressive integrated moving average based model (MacArthur et al 1989) and demonstrate the supremacy of SVM regarding maximum and minimum error values. Xuemei et al 2009 developed a model based on Least Square SVM (LS-SVM) and used the same input parameters. This approach contributes to learning correction for limited training sets and enhanced prediction time efficiency to traditional SVM model in load forecasting.…”

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%

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

et al. 2018

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“…Let (1) where Ȧ is regression coefficient and b is model error value, is then constructed. The Ȧ and b are obtained by solving an optimization problem: …”

Section: A Basic Principle Of Svrmentioning

confidence: 99%

“…What is more, it is also useful for HVAC operations including adjusting the starting time of cooling to meet start-up loads, minimizing or limiting the electric on-peak demand, optimizing the costs and energy utilization in cool storage systems, and related energy and cost needs in other HVAC systems [1].…”

Section: Introductionmentioning

confidence: 99%

A novel building cooling load prediction based on SVR and SAPSO

Ding

2010

2010 International Symposium on Computer, Communication, Control and Automation (3CA)

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Accurate building cooling load forecasting is the precondition for the optimal control and energy saving operation of HVAC systems. Hourly cooling load forecasting is a difficult work as the load at a given point is dependent not only on the load at the previous hour but also on the load at the same hour on the previous day. So the accuracy of forecasting is influenced by many unpredicted factors. Support vector machine (SVM) is a novel type of learning machine, which has been successfully employed to solve nonlinear regression and time series problems. The key problem of SVM is the selection of SVM free parameters. In this paper, it is proposed a new optimal model, which is based on Stimulated Annealing Particle Swarm Optimization Algorithm (SAPSO) that combines the advantages of PSO algorithm and SA algorithm. The strong searching ability of SA was employed to PSO algorithm to avoid the premature convergence with better stability and astringency. The SA based PSO algorithm was used to optimize the free parameters of SVM model. The study used the proposed model to forecast load of cooling load system. The numerical simulation results demonstrate that the accuracy of SA-PSO based SVM model outperforms that of the traditional SVM load forecasting model.

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“…What is more, it is also useful for HV AC operations including adjusting the starting time of cooling to meet start-up loads, minimizing or limiting the electric on-peak demand, optimizing the costs and energy utilization in cool storage systems, and related energy and cost needs in other HVAC systems [1].…”

mentioning

confidence: 99%

“…fuZZY SUPPORT VECTOR MACHINE Support Vector Machines (SVM) is a new and promising technique for data classification and regression [1]- [3]. In many applications, SVM has been shown to provide higher performance than traditional learning machines [1].…”

mentioning

confidence: 99%

Building cooling load forecasting using fuzzy support vector machine and fuzzy C-mean clustering

Deng

Ding

et al. 2010

2010 International Conference on Computer and Communication Technologies in Agriculture Engineering

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Accurate building cooling load forecasting is the precondition for the optimal control and energy saving operation of HV AC systems. Hourly cooling load forecasting is a difficult work as the load at a given point is dependent not only on the load at the previous hour but also on the load at the same hour on the previous day. In this paper, a novel short-term cooling load forecasting approach is presented by conjunctive use of fuzz y C-mean clustering algorithm and fuzzy support vector machines (FSVMs). According to the similarity degree of input samples, the training samples are clustered by means of the homogenous characteristic, and then we apply a fuzzy membership to each input point such that different input points can make different contributions to the learning of decision surface. The results of experiment indicate that the proposed method can be used as an attractive and effective means for short-term cooling load forecasting.

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Building Cooling Load Forecasting Model Based on LS-SVM

Cited by 23 publications

References 6 publications

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

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

A novel building cooling load prediction based on SVR and SAPSO

Building cooling load forecasting using fuzzy support vector machine and fuzzy C-mean clustering

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