Accurate power load forecasting has an important impact on power systems. In order to improve the load forecasting accuracy, a new load forecasting model, VMD–CISSA–LSSVM, is proposed. The model combines the variational modal decomposition (VMD) data preprocessing method, the sparrow search algorithm (SSA) and the least squares support vector machine (LSSVM) model. A multi-strategy improved chaotic sparrow search algorithm (CISSA) is proposed to address the shortcomings of the SSA algorithm, which is prone to local optima and a slow convergence. The initial population is generated using an improved tent chaotic mapping to enhance the quality of the initial individuals and population diversity. Second, a random following strategy is used to optimize the position update process of the followers in the sparrow search algorithm, balancing the local exploitation performance and global search capability of the algorithm. Finally, the Levy flight strategy is used to expand the search range and local search capability. The results of the benchmark test function show that the CISSA algorithm has a better search accuracy and convergence performance. The volatility of the original load sequence is reduced by using VMD. The optimal parameters of the LSSVM are optimized by the CISSA. The simulation test results demonstrate that the VMD–CISSA–LSSVM model has the highest prediction accuracy and stabler prediction results.
Accurate and stable power load forecasting methods are essential for the rational allocation of power resources and grid operation. Due to the nonlinear nature of power loads, it is difficult for a single forecasting method to complete the forecasting task accurately and quickly. In this study, a new combined model for power loads forecasting is proposed. The initial weights and thresholds of the extreme learning machine (ELM) optimized by the chaotic sparrow search algorithm (CSSA) and improved by the firefly algorithm (FA) are used to improve the forecasting performance and achieve accurate forecasting. The early local optimum that exists in the sparrow algorithm is overcome by Tent chaotic mapping. A firefly perturbation strategy is used to improve the global optimization capability of the model. Real values from a power grid in Shandong are used to validate the prediction performance of the proposed FA-CSSA-ELM model. Experiments show that the proposed model produces more accurate forecasting results than other single forecasting models or combined forecasting models.
Inaccurate electricity load forecasting can lead to the power sector gaining asymmetric information in the supply and demand relationship. This asymmetric information can lead to incorrect production or generation plans for the power sector. In order to improve the accuracy of load forecasting, a combined power load forecasting model based on machine learning algorithms, swarm intelligence optimization algorithms, and data pre-processing is proposed. Firstly, the original signal is pre-processed by the VMD–singular spectrum analysis data pre-processing method. Secondly, the noise-reduced signals are predicted using the Elman prediction model optimized by the sparrow search algorithm, the ELM prediction model optimized by the chaotic adaptive whale algorithm (CAWOA-ELM), and the LSSVM prediction model optimized by the chaotic sparrow search algorithm based on elite opposition-based learning (EOBL-CSSA-LSSVM) for electricity load data, respectively. Finally, the weighting coefficients of the three prediction models are calculated using the simulated annealing algorithm and weighted to obtain the prediction results. Comparative simulation experiments show that the VMD–singular spectrum analysis method and two improved intelligent optimization algorithms proposed in this paper can effectively improve the prediction accuracy. Additionally, the combined forecasting model proposed in this paper has extremely high forecasting accuracy, which can help the power sector to develop a reasonable production plan and power generation plans.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.