A Power Load Forecasting Model Based on FA-CSSA-ELM

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 ne...

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Bibliographic Details
Published in:Mathematical problems in engineering 2021, Vol.2021, p.1-14
Main Authors: Wang, Zuoxun, Wang, Xinheng, Ma, Chunrui, Song, Zengxu
Format: Article
Language:English
Subjects:
Accuracy
Artificial neural networks
Decomposition
Electricity
Electricity consumption
Electricity distribution
Forecasting
Global optimization
Heuristic methods
Intelligence
Machine learning
Mathematical models
Neural networks
Optimization algorithms
Perturbation
Population
Power
Search algorithms
Support vector machines
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Summary: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.
ISSN:1024-123X
1563-5147
DOI:10.1155/2021/9965932