Department of Electrical & Electronics Engineering, Anna University, Coimbatore, Tamil Nadu, India.
Assistant Executive Engineer, Tamilnadu Generation and Distribution Corporation Ltd, Chennai, Tamil Nadu, India.
PLoS One. 2022 Sep 16;17(9):e0273632. doi: 10.1371/journal.pone.0273632. eCollection 2022.
A novel Variational Mode Decomposition (VMD) combined Fuzzy-Twin Support Vector Machine Model with deep learning mechanism is devised in this research study to forecast the solar Photovoltaic (PV) output power in day ahead basis. The raw data from the solar PV farms are highly fluctuating and to extract the useful stable components VMD is employed. A novel Fuzzy-Twin Support Vector Machine (FTSVM) model developed acts as the forecasting model for predicting the solar PV output power for the considered solar farms. The twin support vector machine (SVM) model formulates two separating hyperplanes for predicting the output power and in this research study a fuzzy based membership function identifies most suitable two SVM prediction hyperplanes handling the uncertainties of solar farm data. For the developed, new VMD-FTSVM prediction technique, their optimal parameters for the training process are evaluated with the classic Ant Lion Optimizer (ALO) algorithm. The solar PV output power is predicted using the novel VMD-FTSVM model and during the process multi-kernel functions are utilized to devise the two fuzzy based hyperplanes that accurately performs the prediction operation. Deep learning (DL) based training of the FTSVM model is adopted so that the deep auto-encoder and decoder module enhances the accuracy rate. The proposed combined forecasting model, VMD-ALO-DLFTSVM is validated for superiority based on a two 250MW PV solar farm in India. Results prove that the proposed model outperforms the existing model in terms of the performance metrics evaluated and the forecasted PV Power.
本研究提出了一种新的变分模态分解（VMD）与深度学习机制相结合的模糊双子支持向量机模型，用于预测日前的太阳能光伏（PV）输出功率。从太阳能光伏农场获得的原始数据波动很大，为了提取有用的稳定分量，采用了 VMD。开发的新型模糊双子支持向量机（FTSVM）模型作为预测模型，用于预测所考虑的太阳能农场的太阳能 PV 输出功率。双子支持向量机（SVM）模型为预测输出功率制定了两个分离的超平面，在本研究中，基于模糊的隶属函数确定最适合的两个 SVM 预测超平面，处理太阳能农场数据的不确定性。对于所开发的新的 VMD-FTSVM 预测技术，使用经典的蚂蚁狮子优化器（ALO）算法评估其训练过程的最佳参数。使用新的 VMD-FTSVM 模型预测太阳能 PV 输出功率，并在此过程中利用多核函数设计两个基于模糊的超平面，以准确执行预测操作。采用基于深度学习（DL）的 FTSVM 模型训练，以使深度自动编码器和解码器模块提高准确率。针对印度的两个 250MW PV 太阳能农场，对所提出的组合预测模型 VMD-ALO-DLFTSVM 进行了优越性验证。结果证明，在所评估的性能指标和预测的 PV 功率方面，所提出的模型优于现有模型。
