Dual possibilistic regression models of support vector machines and application in power load forecasting

Yang, Xianfei; Yu, Xiang; Lu, Hui

doi:10.1177/1550147720921636

Cited by 4 publications

(1 citation statement)

References 52 publications

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“…Ahmet et al used Genetic Algorithm (GA) to optimize parameters of LSTM and proposed GA-LSTM multi-step prediction model for influenza outbreak. Then, the experiments shown that the prediction effect of this model is better than that of other traditional models such as SVM ( Kara, 2021 ; Yang, Yu & Lu, 2020 ). Beiranvand & Rajaee (2022) used Back Propagation Neural Network(BPNN) optimized by Lion Swarm Optimization (LSO) algorithm to predict the uniaxial compressive strength (UCS) of a novel rubber-sand concrete (RSC) material.…”

Section: Introductionmentioning

confidence: 92%

Strip thickness prediction method based on improved border collie optimizing LSTM

Sun

Lin²,

Zhou

et al. 2022

PeerJ Computer Science

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Background The thickness accuracy of strip is an important indicator to measure the quality of strip, and the control of the thickness accuracy of strip is the key for the high-quality strip products in the rolling industry. Methods A thickness prediction method of strip based on Long Short-Term Memory (LSTM) optimized by improved border collie optimization (IBCO) algorithm is proposed. First, chaotic mapping and dynamic weighting strategy are introduced into IBCO to overcome the shortcomings of uneven initial population distribution and inaccurate optimization states of some individuals in Border Collie Optimization (BCO). Second, Long Short-Term Memory (LSTM) which can effectively deal with time series data and alleviate long-term dependencies is adopted. What’s more, IBCO is utilized to optimize parameters to mitigate the influence of hyperparameters such as the number of hidden neurons and learning rate on the prediction accuracy of LSTM, so IBCO-LSTM is established. Results The experiments are carried out on the measured strip data, which proves the excellent prediction performance of IBCO-LSTM. The experiments are carried out on the actual strip data, which prove that IBCO-LSTM has excellent capability of prediction.

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Section: Introductionmentioning

confidence: 92%

Strip thickness prediction method based on improved border collie optimizing LSTM

Sun

Lin²,

Zhou

et al. 2022

PeerJ Computer Science

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Ultra-short-term electricity load forecasting based on improved random forest algorithm

et al. 2023

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Electricity load forecasting is one of the important tasks of the power marketing department, and accurate load forecasting is extremely important to ensure real-time dispatch and security of the power system. In order to obtain accurate and reliable load forecasting results, an ultra-short-term power load forecasting model based on an improved random forest regression algorithm is proposed in this paper. First, data pre-processing is performed on the original dataset. Then the pre-processed time data and historical load data are used as inputs to the model, and optimization of the model using the Gaussian mixture-based tree-structured Parzen estimator algorithm is carried out. Finally, the final prediction results were derived. Experimental analysis was conducted with real load data from a region of China, and the experimental results show that the method has better prediction accuracy than the original random forest algorithm and other traditional machine learning algorithms.

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Short-term Load Forecasting System for Power System Based on Big Data

Yang

2021

J. Phys.: Conf. Ser.

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Short-term load forecasting of power system is an important task of power distribution system. Accurate short-term load forecasting provides the best configuration for grid power generation and distribution, maximizing energy saving and ensuring stable operation. This paper aims to study the design of short-term load forecasting system of power system based on big data. On the basis of analyzing power system load forecasting algorithms, classification of load forecasting, characteristics of load forecasting and system design principles, each module of the system is designed in detail, and finally tested the performance of the system. The test results show that the system has no adverse reactions in the use of a large number of users and repeated operation for a long time. In the case of large throughput, the system has a satisfactory response time and relatively reliable system stability.

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Dual possibilistic regression models of support vector machines and application in power load forecasting

Cited by 4 publications

References 52 publications

Strip thickness prediction method based on improved border collie optimizing LSTM

Strip thickness prediction method based on improved border collie optimizing LSTM

Ultra-short-term electricity load forecasting based on improved random forest algorithm

Short-term Load Forecasting System for Power System Based on Big Data

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