Medium and long-term load forecasting based on multi-factors modified PSO-SVM algorithm

Cao, Yang; Shuai, Haiyan

doi:10.1142/9789814740135_0071

Cited by 2 publications

(3 citation statements)

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“…Learning machine methods, such as SVM considered a promising alternative, either for multi-factors modified PSO-SVM algorithm [39], or classification and regression [40]. Abe introduced a tutorial about SVMs applications in pattern classification and recognition problems [41].…”

Section: Applied Svm To Load Forecastingmentioning

confidence: 99%

Long-Term Electrical Load Forecasting in Rwanda Based on Support Vector Machine Enhanced with Q-SVM Optimization Kernel Function

Uwimana,

Zhou,

Zhang

2023

JPEE

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In recent years, Rwanda's rapid economic development has created the "Rwanda Africa Wonder", but it has also led to a substantial increase in energy consumption with the ambitious goal of reaching universal access by 2024. Meanwhile, on the basis of the rapid and dynamic connection of new households, there is uncertainty about generating, importing, and exporting energy whichever imposes a significant barrier. Long-Term Load Forecasting (LTLF) will be a key to the country's utility plan to examine the dynamic electrical load demand growth patterns and facilitate long-term planning for better and more accurate power system master plan expansion. However, a Support Vector Machine (SVM) for long-term electric load forecasting is presented in this paper for accurate load mix planning. Considering that an individual forecasting model usually cannot work properly for LTLF, a hybrid Q-SVM will be introduced to improve forecasting accuracy. Finally, effectively assess model performance and efficiency, error metrics, and model benchmark parameters there assessed. The case study demonstrates that the new strategy is quite useful to improve LTLF accuracy. The historical electric load data of Rwanda Energy Group (REG), a national utility company from 1998 to 2020 was used to test the forecast model. The simulation results demonstrate the proposed algorithm enhanced better forecasting accuracy.

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Section: Applied Svm To Load Forecastingmentioning

confidence: 99%

Long-Term Electrical Load Forecasting in Rwanda Based on Support Vector Machine Enhanced with Q-SVM Optimization Kernel Function

Uwimana,

Zhou,

Zhang

2023

JPEE

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“…Ref. [1] established a long-term power load forecasting model by using a support vector machine (SVM) model based on the comprehensive consideration of economic factors, social factors, and energy market structure and optimizing a multi-factor medium and the parameters of SVM by using particle swarm optimization (PSO) and an improved cross-validation method. In Ref.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In Ref. [4], the author proposed a hybrid optimization grey model called Rolling-ALO-GM (1,1) for ECM based on known information from annual EC by using the grey control theory. In Ref.…”

Section: Literature Reviewmentioning

confidence: 99%

Electricity Consumption Forecasting using Support Vector Regression with the Mixture Maximum Correntropy Criterion

Duan

Tian

et al. 2019

Entropy

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The electricity consumption forecasting (ECF) technology plays a crucial role in the electricity market. The support vector regression (SVR) is a nonlinear prediction model that can be used for ECF. The electricity consumption (EC) data are usually nonlinear and non-Gaussian and present outliers. The traditional SVR with the mean-square error (MSE), however, is insensitive to outliers and cannot correctly represent the statistical information of errors in non-Gaussian situations. To address this problem, a novel robust forecasting method is developed in this work by using the mixture maximum correntropy criterion (MMCC). The MMCC, as a novel cost function of information theoretic, can be used to solve non-Gaussian signal processing; therefore, in the original SVR, the MSE is replaced by the MMCC to develop a novel robust SVR method (called MMCCSVR) for ECF. Besides, the factors influencing users’ EC are investigated by a data statistical analysis method. We find that the historical temperature and historical EC are the main factors affecting future EC, and thus these two factors are used as the input in the proposed model. Finally, real EC data from a shopping mall in Guangzhou, China, are utilized to test the proposed ECF method. The forecasting results show that the proposed ECF method can effectively improve the accuracy of ECF compared with the traditional SVR and other forecasting algorithms.

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Medium and long-term load forecasting based on multi-factors modified PSO-SVM algorithm

Cited by 2 publications

References 0 publications

Long-Term Electrical Load Forecasting in Rwanda Based on Support Vector Machine Enhanced with Q-SVM Optimization Kernel Function

Long-Term Electrical Load Forecasting in Rwanda Based on Support Vector Machine Enhanced with Q-SVM Optimization Kernel Function

Electricity Consumption Forecasting using Support Vector Regression with the Mixture Maximum Correntropy Criterion

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