On Efficient Tuning of LS-SVM Hyper-Parameters in Short-Term Load Forecasting: A Comparative Study

Afshin, M.; Sadeghian, Alireza; Raahemifar, K.

doi:10.1109/pes.2007.385613

Cited by 20 publications

(14 citation statements)

References 15 publications

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“…The kernel transformation allows the handling of nonlinear relationships in the data as a higher dimensional space is obtained in which a linear regression model can be built. The kernel function typically used for electric load forecasting purposes is the radial basis function, due to its ability to deal with nonlinearities . The radial basis function is defined in Equation (6),

K (x, y) = {exp}^{- γ \cdot {(‖‖, x - y)}^{2}}

where γ is viewed as another predefined constant that represents the width of the basis function.…”

Section: Forecasting Methods Based On Artificial Intelligencementioning

confidence: 99%

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Short-term load forecasting based on support vector regression and load profiling

Sousa

Jorge

Neves

2013

Int. J. Energy Res.

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SUMMARY The article proposes a methodology to forecast the electric load for the 24 h of the following day based on support vector regression. The study considers 24 distinct models, one for each predicted hour, where each individual model is treated independently. Its objective is to find the optimal combination of support vector machine parameters that could generalize low forecasting errors, using simulated annealing as a metaheuristic. The adopted methodology is compared to concurrent methods based on neural networks when applied to a simulated load diagram (to illustrate a distribution feeder supplying a sample of 740 consumers). The results have proven its effectiveness with mean absolute percentage errors being less than 5% for testing samples. The study also focuses on evaluating the potential benefits of adopting load profiling information as input in support vector regression, giving a consistent proof of its importance. Copyright © 2013 John Wiley & Sons, Ltd.

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K (x, y) = {exp}^{- γ \cdot {(‖‖, x - y)}^{2}}

where γ is viewed as another predefined constant that represents the width of the basis function.…”

Section: Forecasting Methods Based On Artificial Intelligencementioning

confidence: 99%

“…The selection of the three nonnegative parameters ( ε , C , and γ ) is of extreme importance to be able to forecast accurately . However, structural methods for confirming the efficient selection of parameters are lacking.…”

Section: Forecasting Methods Based On Artificial Intelligencementioning

confidence: 99%

Short-term load forecasting based on support vector regression and load profiling

Sousa

Jorge

Neves

2013

Int. J. Energy Res.

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“…In addition, a human-machine construct intelligence framework was proposed in [26] to determine the horizon year load for a long term load forecasting. Machine learning methods such as SVM and neural networks have been used in carrying out forecasting [27][28][29][30][31][32][33][34]. For example, Shi et al [28] developed a SVM-based model for one-day-ahead power output forecasting using the characteristics of weather classification.…”

Section: Related Work In Qosmentioning

confidence: 99%

Towards a Security Enabled and SOA-based QoS (for the Smart Grid) Architecture

Chrysoulas¹,

Pitropakis²

2018

EAI Endorsed Transactions on Industrial Networks and Intelligen

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QoS and Security features are playing an important role in modern network architecures. Dynamic selection of services and by extension of service providers are vital in today's liberalized market of energy. On the other hand it is equally important for Service Providers to spot the one QoS Module that offers the best QoS level in a given cost. Type of service, response time, availability and cost, consist a basic set of attributes that should be taken into consideration when building a concrete Grid network. In the proposed QoS architecture Prosumers request services based on the aforementioned set of attributes. The Prosumer requests the service through the QoS Module. It is then the QoS Module that seeks the Service Provider that best fits the needs of the client. The aforementioned approach is well supplemented with an in depth analysis on existing authentication and authorization protocols. The authors believe that QoS and security can work in parallel without adding extra burden in the Smart Grid infrastructure. This is feasible by building an in advance system for placing, scheduling, and assigning of the requests for energy consumption or production, thus decongesting the traffic in the whole network.

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“…Suykens and Vandewalle (1999) proposed a simplification of the SVM by using least squares support vector machines (LSSVMs). LSSVMs have been successfully applied in diverse fields (Gestel et al 2001, Sun and Guo 2005, Afshin et al 2007. The LSSVM has similar advantages to that of the SVM, but an additional advantage is that it only requires solving a set of linear equations, which is much easier and computationally more simple.…”

Section: Introductionmentioning

confidence: 99%

Streamflow forecasting using least-squares support vector machines

Shabri

Suhartono

2012

Hydrological Sciences Journal

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This paper investigates the ability of a least-squares support vector machine (LSSVM) model to improve the accuracy of streamflow forecasting. Cross-validation and grid-search methods are used to automatically determine the LSSVM parameters in the forecasting process. To assess the effectiveness of this model, monthly streamflow records from two stations, Tg Tulang and Tg Rambutan of the Kinta River in Perak, Peninsular Malaysia, were used as case studies. The performance of the LSSVM model is compared with the conventional statistical autoregressive integrated moving average (ARIMA), the artificial neural network (ANN) and support vector machine (SVM) models using various statistical measures. The results of the comparison indicate that the LSSVM model is a useful tool and a promising new method for streamflow forecasting.

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On Efficient Tuning of LS-SVM Hyper-Parameters in Short-Term Load Forecasting: A Comparative Study

Cited by 20 publications

References 15 publications

Short-term load forecasting based on support vector regression and load profiling

Short-term load forecasting based on support vector regression and load profiling

Towards a Security Enabled and SOA-based QoS (for the Smart Grid) Architecture

Streamflow forecasting using least-squares support vector machines

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