Application of Fuzzy Support Vector Machine in Short-Term Power Load Forecasting

Yang, Jie; Tang, Yachun; Hua-bin, Duan

doi:10.4018/jcit.295248

Cited by 4 publications

(5 citation statements)

References 13 publications

(6 reference statements)

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“…Liang uses an improved support vector machine (SVM) method, and using the nonlinear relationship between load forecasting and load impact parameters, to establish holiday and non holiday power load forecasting models, and achieves short-term power load forecasting. With the development of the Internet of things and smart meter technology, researchers have introduced deep learning based on the Internet of things to get the characteristics of the data received and accurately predict the future before load values [7]. Guo and others have proposed machine learning-based predictive modeling.…”

Section: Literature Reviewmentioning

confidence: 99%

Short-Term Load Monitoring of a Power System Based on Neural Network

Yang

2023

International Transactions on Electrical Energy Systems

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In order to improve the accuracy of power load forecasting, this paper proposes a neural network-based short-term monitoring method. First, the original energy load signal is decomposed by the CEEMDAN algorithm to obtain several eigenmode function components and residual components; several eigenmode function components and residual functions are fed into the NARX neural network for computational purposes. The partial hypothesis is superimposed in the following part to obtain the final short-term forecast. According to the test results, the MAPE of the CEEMDAN-NARX model is 4.753%, 3.540%, and 0.343% lower than the SVM, RNN, and NARX models, respectively, and 3.741% and 2.682% lower than CEEMDAN-SVM and CEEMDAN-RNN, respectively. The MAPE and RMSE of the CEEMDAN-NARX model are 0.765% and 101.7 MW, respectively, which are 0.468% and 45.2 MW lower than NARX models, respectively. Compared to CEEMDAN-SVM, the MAPE of CEEMDAN-NARX and CEEMDAN-RNN decreased by 0.986% and 0.692%, respectively, and the RMSE of CEEMDAN-NARX decreased by 111.5 and 65.7 MW, respectively, compared to CEEMDAN-SVM. Conclusion is that the load forecasting model based on the combination of CEEMDAN algorithm and NARX neural network can effectively connect, reduce the negative impact of noise on forecasting results, and improve forecasting accuracy.

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Section: Literature Reviewmentioning

confidence: 99%

Short-Term Load Monitoring of a Power System Based on Neural Network

Yang

2023

International Transactions on Electrical Energy Systems

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“…For example, the real scene data of the large-scale uni ed test of spoken English as the basis and used the XGBoost algorithm model to recognize the speech and then conduct the evaluation. e results show that the method has strong robustness in dealing with noise; Wang et al [4] applied the XGBoost algorithm to English writing, processed the text by natural processing language, and used the XGBoost algorithm to mine, thereby improving English writing ability. It can be seen from the above research that deep learning algorithms are widely used in English research [5].…”

Section: Introductionmentioning

confidence: 99%

The Prediction of English Online Network Performance Based on the XGBoost Algorithm

Chen¹

2022

Mobile Information Systems

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The demand of C language informatization for English learning has increased greatly, and strengthening the application of informatization in teaching has become a current trend, while deep learning algorithms have been applied in various tasks due to their obvious advantages. In this paper, an English score prediction method based on the XGBoost algorithm is proposed. In order to verify the effectiveness of the model in English score prediction, the principle of the XGBoost algorithm is firstly analyzed as a basis. The English test scores of a university for 2019–2021 were used as the basic data source, and the output probabilities of the proposed model were used to compare the results under different conditions. The experimental results show that the predicted scores are basically consistent with the actual scores. From a scientific point of view, the ability to predict unknown data with low error suggests that it enables students and teachers to identify the underlying factors that make it difficult for students to answer questions. Understanding these causes is useful for designing high-quality courses and lesson plans.

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“…respectively. When predicting the electricity load for a week, compared with other models, the RMSE values of model proposed in this paper are reduced by 93.89, 60.55,16.47, and 12.83, respectively. MAPE decreased by 1.22%, 0.56%, 0.27%, and 0.14%, respectively.…”

mentioning

confidence: 90%

“…e machine learning method has a good ability to deal with nonlinear and complicated problems [12] and is widely used in the eld of load forecasting. Machine learning methods mainly include neural network [13][14][15], support vector regression machine [16], random forest [17,18], and so on. Support vector regression machine applies support vector to regression tasks and has strong learning ability on small sample data.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Power Load Forecasting Based on SAPSO-CNN-LSTM Model considering Autocorrelated Errors

Zhang

2022

Mathematical Problems in Engineering

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Accurate power load forecasting is essential for power grid operation and dispatching. To further improve the accuracy of power load forecasting, this study proposes a new power load forecasting method. Firstly, correlation coefficients of influential variables are calculated for feature selection. Secondly, the form of input data is changed to adjust for autocorrelated errors. Thirdly, data features are extracted by convolutional neural networks (CNN) to construct feature vectors. Finally, the feature vectors are input into long short-term memory (LSTM) network for training to obtain prediction results. Moreover, for solving the problem that network hyperparameters are difficult to set, the simulated annealing particle swarm optimization (SAPSO) algorithm is used to optimize the hyperparameters. Experiments show that the prediction accuracy of the proposed model is higher compared with LSTM, CNN-LSTM, and other models.

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Application of Fuzzy Support Vector Machine in Short-Term Power Load Forecasting

Cited by 4 publications

References 13 publications

Short-Term Load Monitoring of a Power System Based on Neural Network

Short-Term Load Monitoring of a Power System Based on Neural Network

The Prediction of English Online Network Performance Based on the XGBoost Algorithm

Short-Term Power Load Forecasting Based on SAPSO-CNN-LSTM Model considering Autocorrelated Errors

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