Load Forecasting Based on Weighted Grey Relational Degree and Improved ABC-SVM

Ru-xue, Luo; Liu, Shumin; Miao-na, You; Jican, Lin

doi:10.1007/s42835-021-00727-3

Cited by 11 publications

(1 citation statement)

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“…For example, the traditional particle swarm algorithm (PSO) is used to adjust the parameters of BPNN for PV power prediction [18]. In [19], the artificial bee colony (ABC) algorithm was used to optimize the SVM model to predict the load. However, traditional intelligence algorithms exhibit some inherent shortcomings, such as the well-known tendency of PSO to become trapped in local optima and the slow convergence rate of the ABC algorithm [20].…”

Section: Introductionmentioning

confidence: 99%

A Novel Photovoltaic Power Prediction Method Based on a Long Short-Term Memory Network Optimized by an Improved Sparrow Search Algorithm

Chen,

Li,

Zhao

2024

Electronics

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Photovoltaic (PV) power prediction plays a significant role in supporting the stable operation and resource scheduling of integrated energy systems. However, the randomness and volatility of photovoltaic power generation will greatly affect the prediction accuracy. Focusing on this issue, a prediction framework is proposed in this research by developing an improved sparrow search algorithm (ISSA) to optimize the hyperparameters of long short-term memory (LSTM) neural networks. The ISSA is specially designed from the following three aspects to support a powerful search performance. Firstly, the initial population variety is enriched by using an enhanced sine chaotic mapping. Secondly, the relative position of neighboring producers is introduced to improve the producer position-updating strategy to enhance the global search capabilities. Then the Cauchy–Gaussian variation is utilized to help avoid the local optimal solution. Numerical experiments on 20 test functions indicate that ISSA could identify the optimal solution with better precision compared to SSA and PSO algorithms. Furthermore, a comparative study of PV power prediction methods is provided. The ISSA-LSTM algorithm developed in this paper and five benchmark models are implemented on a real dataset gathered from the Alice Springs area in Australia. In contrast to the SSA-LSTM model, most MAE, MAPE, and RMSE values of the proposed model are reduced by 20∼60%, demonstrating the superiority of the proposed model under various weather conditions and typical seasons.

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Section: Introductionmentioning

confidence: 99%