Improved particle swarm optimization combined with least squares support vector machines for short-term load forecasting

Mingchong Han; Aiguo Tan; Jianwei Zhong

doi:10.1117/12.2636473

6 May 2022 Improved particle swarm optimization combined with least squares support vector machines for short-term load forecasting

Mingchong Han, Aiguo Tan, Jianwei Zhong

Author Affiliations +

Proceedings Volume 12176, International Conference on Algorithms, Microchips and Network Applications; 1217612 (2022) https://doi.org/10.1117/12.2636473
Event: International Conference on Algorithms, Microchips, and Network Applications 2022, 2022, Zhuhai, China

Abstract

Short-term power load forecasting is an important foundation to safe dispatch and economic operation of power system, and with the marketization of power system, the accuracy of load forecasting plays a direct role in the economy and stability of power system operation; however, the instability of load sequence increases the difficulty of forecasting. Traditional load forecasting methods cannot achieve satisfactory forecasting accuracy, but the rise of synthetic intelligence technology, especially the rapid development of deep learning and big data technology, has laid a good foundation for further improving the accuracy of load forecasting. Among them, Least Squares Support Vector Machine (LSSVM) not only maintains the advantages of Support Vector Machine (SVM), but also lowers the evaluation complexity and accelerates the solution speed, which provides a new research direction for short-term power load forecasting. In this paper, LSSVM is used for short-term power load forecasting. This paper applies LSSVM to shortterm power load forecasting and establishes a short-term power load forecasting model based on LSSVM. Moreover, an Improved Particle Swarm Optimization (IPSO) algorithm, which provides guidance for initial particle selection and avoids premature aggregation of particles based on population diversity information, is used to optimize the penalty factor and kernel parameters of LSSVM, and is validated with historical load data and meteorological data of a region. The experimental results display that the IPSO-LSSVM model used a higher prediction accuracy in this paper.

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Mingchong Han, Aiguo Tan, and Jianwei Zhong "Improved particle swarm optimization combined with least squares support vector machines for short-term load forecasting", Proc. SPIE 12176, International Conference on Algorithms, Microchips and Network Applications, 1217612 (6 May 2022); https://doi.org/10.1117/12.2636473

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KEYWORDS

Particles

Particle swarm optimization

Data modeling

Medium wave

Humidity

Atmospheric modeling

Meteorology

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