Abstract
In recent years, the construction of China’s power grid has experienced rapid development, and its scale has leaped into the first place in the world. Accurate and effective prediction of power grid investment can not only help pool funds and rationally arrange investment in power grid construction, but also reduce capital costs and economic risks, which plays a crucial role in promoting power grid investment planning and construction process. In order to forecast the power grid investment of China accurately, firstly on the basis of analyzing the influencing factors of power grid investment, the influencing factors system for China’s power grid investment forecasting is constructed in this article. The method of grey relational analysis is used for screening the main influencing factors as the prediction model input. Then, a novel power grid investment prediction model based on DE-GWO-SVM (support vector machine optimized by differential evolution and grey wolf optimization) algorithm is proposed. Next, two cases are taken for empirical analysis to prove that the DE-GWO-SVM model has strong generalization capacity and has achieved a good prediction effect for power grid investment forecasting in China. Finally, the DE-GWO-SVM model is adopted to forecast power grid investment in China from 2018 to 2022.
Highlights
For social development, the power grid plays a crucial role which shoulders the important responsibilities of optimizing the allocation of energy resources and promoting social development, and it is the major implementation body of energy strategy
For forecasting the power grid investment of China accurately, based on the construction of influencing factors system for power grid investment forecasting, a novel power grid investment prediction model based on differential evolution (DE)-Grey wolf optimization (GWO)-support vector machine (SVM) algorithm is proposed in this article
DE-GWO-SVM: Support vector machine optimized by differential evolution algorithm and grey wolf optimization algorithm; GWO-SVM: Support vector machine optimized by grey wolf optimization algorithm; SVM: Support vector machine; back propagation (BP): Back propagation neural network
Summary
For social development, the power grid plays a crucial role which shoulders the important responsibilities of optimizing the allocation of energy resources and promoting social development, and it is the major implementation body of energy strategy. Compared with neural networks, support vector machine developed in the statistical learning theory has a more solid foundation of mathematical theory, which can effectively solve the high-dimensional data model construction problems under the condition of limited samples It has become one of the most popular research directions in the machine learning field with a stronger generalization ability. The three algorithms of differential evolution algorithm, grey wolf optimization algorithm, and support vector machine are combined for power grid investment forecasting. Three algorithms—differential evolution algorithm, grey wolf optimization algorithm, and support vector machine—are combined for power grid investment forecasting. The main structure of this article is arranged as follows: Section 2 introduces the methodology, including establishing the influencing factors system for power grid investment forecasting, introducing grey relational analysis which is used for the main influencing factors screening, and proposing the DE-GWO-SVM forecasting model.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
