Syah Rahmad, Rezaei Mohammad, Elveny Marischa, Majidi Nezhad Meysam, Ramdan Dadan, Nesaht Mehdi, Davarpanah Afshin
Data Science & Computational Intelligence Research Group, Universitas Medan Area, Medan, Indonesia.
Computer Science and Engineering Department, University of Texas at Arlington, Arlington, USA.
Sci Rep. 2021 Aug 30;11(1):17375. doi: 10.1038/s41598-021-96501-6.
Due to focal liberality in electricity market projection, researchers try to suggest powerful and successful price forecasting algorithms. Since, the accurate information of future makes best way for market participants so as to increases their profit using bidding strategies, here suggests an algorithm for electricity price anticipation. To cover this goal, separate an algorithm into three steps, namely; pre-processing, learning and tuning. The pre-processing part consists of Wavelet Packet Transform (WPT) to analyze price signal to high and low frequency subseries and Variational Mutual Information (VMI) to select valuable input data in order to helps the learning part and decreases the computation burden. Owing to the learning part, a new Least squares support vector machine based self-adaptive fuzzy kernel (LSSVM-SFK) is proposed to extract best map pattern from input data. A new modified HBMO is introduced to optimally set LSSVM-SFK variables such as bias, weight, etc. To improve the performances of HBMO, two modifications are proposed that has high stability in HBMO. Suggested forecasting algorithm is examined on electricity markets that has acceptable efficiency than other models.
由于电力市场预测中存在局部宽松性，研究人员试图提出强大且成功的价格预测算法。因为，未来的准确信息为市场参与者提供了最佳途径，以便他们通过投标策略增加利润，在此提出一种电价预测算法。为实现这一目标，将算法分为三个步骤，即预处理、学习和调优。预处理部分包括小波包变换（WPT），用于将价格信号分析为高频和低频子序列，以及变分互信息（VMI），用于选择有价值的输入数据，以帮助学习部分并减轻计算负担。在学习部分，提出了一种基于最小二乘支持向量机的自适应模糊核（LSSVM - SFK），用于从输入数据中提取最佳映射模式。引入了一种新的改进的蜂群算法（HBMO）来优化设置LSSVM - SFK的变量，如偏差、权重等。为提高HBMO的性能，提出了两种改进方法，它们在HBMO中具有很高的稳定性。所建议的预测算法在电力市场上进行了检验，其效率比其他模型更高。
