Abstract
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.
Highlights
With the improvement in economic and social development, high-quality electric energy supply provides an important guarantee for the efficient and stable development of the whole country
Based on the above problems, this paper proposes a combined power load forecasting model based on machine learning, swarm intelligence optimization algorithms, and data pre-processing
The combined model is based on the Elman model, the Nowadays, accurate electrical loads help the power sector to make rational work plans and production decisions, and to reduce the waste of resources and economic losses
Summary
With the improvement in economic and social development, high-quality electric energy supply provides an important guarantee for the efficient and stable development of the whole country. The accuracy of electricity load forecasting is directly related to the economic efficiency and reliability of each energy supply sector. The intrinsic properties of the electrical load make it fundamentally different from other commodities This is due to the non-storable nature of the electricity load, which is influenced by the dynamic balance between supply and demand and the reliability of the intelligent transmission network [5]. For the supply sector of the power system, accurate load forecasting enables rational control of the capacity of generating units and rational dispatch of generating capacity, reducing energy wastage and costs. The model combines a data pre-processing method (VMD–singular spectrum analysis noise reduction method), two novel combinatorial intelligent optimization algorithms (the CAWOA and EOBL-CSSA algorithm), and three independent and efficient forecasting models (ELMAN neural network, LSSVM model, ELM neural network). The weighting coefficients of the three prediction models are calculated using the simulated annealing (SA) algorithm and weighted to obtain the prediction results
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