Research on a hybrid LSSVM intelligent algorithm in short term load forecasting

Xiong, Junhua; Wang, Tingling; Li, Ruisheng

doi:10.1007/s10586-018-1740-z

Cited by 9 publications

(5 citation statements)

References 11 publications

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“…The least squares support vector machine (LSSVM) is an improved traditional support vector machine model. It uses the least squares linear equation as its loss function to transform the inequality constraints of the standard SVM into equality constraints [6,7].…”

Section: Principle Of Lssvm Evaluationmentioning

confidence: 99%

Digital Investment Risk Evaluation Model of Power Grid Enterprises Based on FAHP-AOA-LSSVM

Lan,

Wu,

et al. 2023

Atlantis Highlights in Intelligent Systems

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The digital transformation of the economy represents the general trend. In order to effectively control the investment risk of grid digitization projects and adopt risk-coping strategies with foresight, construct an investment risk evaluation model for grid digitization projects by optimizing the kernel function parameters and regularization parameters of least squares support vector machines through the Archimedes algorithm. Questionnaires and expert judgment are used to analyze the risk factors facing digitization projects' investment environment and establish an investment risk evaluation system. A fuzzy hierarchical analysis method is applied to evaluate the investment risk of 40 completed projects according to the actual engineering situation, and the evaluation results are normalized and processed as the input vector of the evaluation model for training. The results show that the Archimedes optimization algorithm improves the least squares support vector machine model prediction with an average absolute percentage error of 3.5026%, which can more accurately assess the riskiness of digital projects and provide a reference basis for digital project investment risk control.

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Section: Principle Of Lssvm Evaluationmentioning

confidence: 99%

Digital Investment Risk Evaluation Model of Power Grid Enterprises Based on FAHP-AOA-LSSVM

Lan,

Wu,

et al. 2023

Atlantis Highlights in Intelligent Systems

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“…AIbased methods are generally adaptive and robust to non-stationary data and make the prediction results with high accuracy. The most widely used AI-based methods include neural network models [37][38][39][40], the least square support vector machine optimized by various intelligent optimization algorithms [41][42][43][44], and extreme learning machine method optimized by various intelligent optimization approaches [45][46][47].…”

Section: Literature Reviewmentioning

confidence: 99%

Uncertain Interval Forecasting for Combined Electricity-Heat-Cooling-Gas Loads in the Integrated Energy System Based on Multi-Task Learning and Multi-Kernel Extreme Learning Machine

Zhao

Guo

2021

Mathematics

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The accurate prediction of electricity-heat-cooling-gas loads on the demand side in the integrated energy system (IES) can provide significant reference for multiple energy planning and stable operation of the IES. This paper combines the multi-task learning (MTL) method, the Bootstrap method, the improved Salp Swarm Algorithm (ISSA) and the multi-kernel extreme learning machine (MKELM) method to establish the uncertain interval prediction model of electricity-heat-cooling-gas loads. The ISSA introduces the dynamic inertia weight and chaotic local searching mechanism into the basic SSA to improve the searching speed and avoid falling into local optimum. The MKELM model is established by combining the RBF kernel function and the Poly kernel function to integrate the superior learning ability and generalization ability of the two functions. Based on the established model, weather, calendar information, social–economic factors, and historical load are selected as the input variables. Through empirical analysis and comparison discussion, we can obtain: (1) the prediction results of workday are better than those on holiday. (2) The Bootstrap-ISSA-MKELM based on the MTL method has superior performance than that based on the STL method. (3) Through comparing discussion, we discover the established uncertain interval prediction model has the superior performance in combined electricity-heat-cooling-gas loads prediction.

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“…Although this prediction method relying solely on the neural network model has achieved good prediction results, it does not consider importance of data preprocessing. In recent years, the decomposition technology in data preprocessing has attracted the attention of researchers, and some achievements have been made in time series prediction [13][14][15][16][17][18][19]. Li et al [13] proposed a chaotic time series prediction model of monthly precipitation based on the combination of variational mode decomposition and extreme learning machine (ELM).…”

Section: Introductionmentioning

confidence: 99%

“…e model can predict the precipitation trend and improve the prediction accuracy. Xiong et al [14] used a combination of wavelet decomposition and LSSVM to achieve short-term prediction of wind speed. Büyükşahin and Ertekin [15] proposed a hybrid prediction method combining ARIMA and artificial neural network (ANN) and added empirical mode decomposition technology to further improve the prediction accuracy of time series.…”

Section: Introductionmentioning

confidence: 99%

Underwater Acoustic Signal Prediction Based on MVMD and Optimized Kernel Extreme Learning Machine

Yang

Gao

2020

Complexity

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Aiming at the chaotic characteristics of underwater acoustic signal, a prediction model of grey wolf-optimized kernel extreme learning machine (OKELM) based on MVMD is proposed in this paper for short-term prediction of underwater acoustic signals. To solve the problem of K value selection in variational mode decomposition, a new K value selection method MVMD is proposed from the perspective of mutual information, which avoids the blindness of variational mode decomposition (VMD) in the preset modal number. Based on the prediction model of kernel extreme learning machine (KELM), this paper uses grey wolf optimization (GWO) algorithm to optimize and select its regularization parameters and kernel parameters and proposes an optimized kernel extreme learning machine OKELM. To further improve the prediction performance of the model, combined with MVMD, an underwater acoustic signal prediction model based on MVMD-OKELM is established. MVMD-OKELM prediction model is applied to Mackey–Glass chaotic time series prediction and underwater acoustic signal prediction and is compared with ARIMA, EMD-OKELM, and other prediction models. The experimental results show that the proposed MVMD-OKELM prediction model has a higher prediction accuracy and can be effectively applied to the prediction of underwater acoustic signal series.

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Research on a hybrid LSSVM intelligent algorithm in short term load forecasting

Cited by 9 publications

References 11 publications

Digital Investment Risk Evaluation Model of Power Grid Enterprises Based on FAHP-AOA-LSSVM

Digital Investment Risk Evaluation Model of Power Grid Enterprises Based on FAHP-AOA-LSSVM

Uncertain Interval Forecasting for Combined Electricity-Heat-Cooling-Gas Loads in the Integrated Energy System Based on Multi-Task Learning and Multi-Kernel Extreme Learning Machine

Underwater Acoustic Signal Prediction Based on MVMD and Optimized Kernel Extreme Learning Machine

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