Syama S, Ramprabhakar J, Anand R, Meena V P, Guerrero Josep M
Department of Electrical and Electronics Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham, India.
Department of Electrical Engineering, National Institute of Technology Jamshedpur, Jharkhand, 831014, India.
Sci Rep. 2024 Dec 30;14(1):31657. doi: 10.1038/s41598-024-83836-z.
With rising demand for electricity, integrating renewable energy sources into power networks has become a key challenge. The fast incorporation of clean energy sources, particularly solar and wind power, into the existing power grid in the last several years has raised a major problem in controlling and managing the power grid due to the intermittent nature of these sources. Therefore, in order to ensure the safe RES integration providing high-quality power at a fair price and for the secure and reliable functioning of electrical systems, a precise one-day-ahead solar irradiation and wind speed forecast is essential for a stable and safe hybrid energy system. Here, we propose a novel hybrid methodology for wind speed and solar irradiance forecasting. The proposed integrated model employs complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose time series data into a sequence of intrinsic mode functions of lower complexity. Further, permutation entropy is employed to extract the complexity of IMFs for filtering and reconstruction of decomposed components to alleviate the difficulty of direct modeling. Then, a unique swarm intelligence technique, the non-linear dimension learning Hunting Whale Optimization Algorithm (NDLHWOA), is devised to optimize regularized extreme learning machine model parameters to capture the implicit information of each reconstructed sub-series. By integrating a non-linear convergence parameter and the dimension learning hunting approach, the performance of WOA can be drastically enhanced, leading to premature convergence, enhanced population variety, and effective global search. The final prediction outcome is obtained by summing the individual reconstructed sub-series prediction outcomes. To evaluate its efficacy, the proposed model is compared to five well-established models. The evaluation criteria demonstrate that the suggested method outperforms the existing methods in terms of prediction accuracy and stability, thus confirming that a hybrid forecasting model approach combining an efficient decomposition method with a simplified but efficient parameter-optimized neural network can enhance its accuracy and stability.
随着电力需求的不断增长，将可再生能源整合到电网中已成为一项关键挑战。在过去几年中，由于太阳能和风能等清洁能源具有间歇性，它们快速并入现有电网给电网的控制和管理带来了一个重大问题。因此，为了确保以合理价格安全整合可再生能源并提供高质量电力，以及确保电气系统安全可靠运行，对混合能源系统的稳定与安全而言，提前一天精确预测太阳辐照度和风速至关重要。在此，我们提出一种用于风速和太阳辐照度预测的新型混合方法。所提出的集成模型采用带自适应噪声的完备总体经验模态分解（CEEMDAN）将时间序列数据分解为一系列复杂度较低的本征模态函数。此外，采用排列熵来提取本征模态函数的复杂度，以便对分解后的分量进行滤波和重构，从而缓解直接建模的困难。然后，设计了一种独特的群体智能技术——非线性维度学习的座头鲸优化算法（NDLHWOA），用于优化正则化极限学习机模型参数，以捕捉每个重构子序列的隐含信息。通过整合非线性收敛参数和维度学习搜索方法，座头鲸优化算法的性能可得到大幅提升，避免过早收敛，增加种群多样性，并实现有效的全局搜索。最终的预测结果是通过对各个重构子序列的预测结果求和得到的。为了评估其有效性，将所提出的模型与五个成熟的模型进行了比较。评估标准表明，所建议的方法在预测准确性和稳定性方面优于现有方法，从而证实了一种将高效分解方法与简化但高效的参数优化神经网络相结合的混合预测模型方法可以提高其准确性和稳定性。
