Combination Forecasting Model for Mid-long Term Load Based on Least Squares Support Vector Machines and a Mended Particle Swarm Optimization Algorithm

Niu, Dongxiao; Lv, Haitao; Zhang, Yunyun

doi:10.1109/ijcbs.2009.16

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…Niu et al [14] proposed a new model based on the LS-SVM and Particle Swarm Optimization (PSO) for mid-long term forecasting. They applied a mended PSO algorithm to optimize the parameters of the LS-SVM, which avoids opacity when selecting such parameters, and overcomes the phenomena of local optimization.…”

Section: Related Workmentioning

confidence: 99%

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Mid-Long Term Load Forecasting using Multi-Model Artificial Neural Networks

Nezzar¹,

Farah²,

Khadir³

et al. 2016

ijeei

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Electrical load is a major input factor in a country's economic development. To support economic growth and meet future demands for electricity, Load forecasting has become a very important task for electrical power management and planning. Several techniques have been employed to accomplish this task. One of those that is mostly used is Artificial Neural Networks (ANNs) method, which have seen the largest number of studies in the field. the load time series data are auto-correlated and are influenced by other variables, such as temperature and population, etc. Furthermore, a large data sample is needed to perform mid-long term forecasts. For the latter purpose and in order to overcome both the lack of data and the high complexity of adding exogenous variables, a multi-model method, based on neural networks, is proposed for the mid-long term load consumption forecasting. The proposed model decomposes, the initial time series into three components using the X12-ARIMA algorithm, and performs a forecast for each component using three Nonlinear Auto Regressive (NAR) neural networks, with a Feed Forward neural network combining the outputs of the NARs. The experiments that were conducted showed that the proposed model provided good performance even when using a small sample of available data when compared to benchmark models.

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Section: Related Workmentioning

confidence: 99%

“…Among these methods, Artificial Intelligence (AI) techniques offer themselves as valuable solutions. The most commonly used AI techniques are ANNs [6,7,8], fuzzy logic [9,10,11], swarm intelligence algorithms [12,13,14], Grey theory [15,16], Wavelet Neural Networks (WNNs) [17,18], Support Vector Machines (SVM) [19], etc.…”

Section: Introductionmentioning

confidence: 99%

Mid-Long Term Load Forecasting using Multi-Model Artificial Neural Networks

Nezzar¹,

Farah²,

Khadir³

et al. 2016

ijeei

View full text Add to dashboard Cite

show abstract

“…By comparing with several single prediction models, the proposed technique proved to be superior by yielding lowest Mean Absolute Percentage Error (MAPE). In other field, mid long term load prediction has been presented by Niu et al (2009) utilizing an improved PSO-LSSVM. A bit different with both studies, a modification in inertia weight leads the algorithm to faster searching.…”

Section: Introductionmentioning

confidence: 99%

A Hybridization of Enhanced Artificial Bee Colony-Least Squares Support Vector Machines for Price Forecasting

D.¹

2012

Journal of Computer Science

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Problem statement: As the performance of Least Squares Support Vector Machines (LSSVM) is highly rely on its value of regularization parameter, Î³ and kernel parameter, Ï², man-made approach is clearly not an appropriate solution since it may lead to blindness in certain extent. In addition, this technique is time consuming and unsystematic, which consequently affect the generalization performance of LSSVM. Approach: This study presents an enhanced Artificial Bee Colony (ABC) to automatically optimize the hyper parameters of interest. The enhancement involved modifications that provide better exploitation activity by the bees during searching and prevent premature convergence. Later, the prediction process is accomplished by LSSVM. Results and Conclusion: Empirical results obtained indicated that feasibility of proposed technique showed a satisfactory performance by producing better prediction accuracy as compared to standard ABC-LSSVM and Back Propagation Neural Network

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Long-term electric power load forecasting using fuzzy linear regression technique

Al-Hamadi

2011

2011 IEEE Power Engineering and Automation Conference

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Combination Forecasting Model for Mid-long Term Load Based on Least Squares Support Vector Machines and a Mended Particle Swarm Optimization Algorithm

Cited by 5 publications

References 7 publications

Mid-Long Term Load Forecasting using Multi-Model Artificial Neural Networks

Mid-Long Term Load Forecasting using Multi-Model Artificial Neural Networks

A Hybridization of Enhanced Artificial Bee Colony-Least Squares Support Vector Machines for Price Forecasting

Long-term electric power load forecasting using fuzzy linear regression technique

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