Barış Caner, Yanarateş Cağfer, Altan Aytaç
Department of Electrical and Electronics Engineering, Zonguldak Bülent Ecevit University, Zonguldak, Turkey.
Department of Electrical and Energy, Kelkit Aydın Doğan Vocational School, Gümüşhane University, Gümüşhane, Turkey.
PeerJ Comput Sci. 2024 Oct 10;10:e2393. doi: 10.7717/peerj-cs.2393. eCollection 2024.
The global impacts of climate change have become increasingly pronounced in recent years due to the rise in greenhouse gas emissions from fossil fuels. This trend threatens water resources, ecological balance, and could lead to desertification and drought. To address these challenges, reducing fossil fuel consumption and embracing renewable energy sources is crucial. Among these, wind energy stands out as a clean and renewable source garnering more attention each day. However, the variable and unpredictable nature of wind speed presents a challenge to integrating wind energy into the electricity grid. Accurate wind speed forecasting is essential to overcome these obstacles and optimize wind energy usage. This study focuses on developing a robust wind speed forecasting model capable of handling non-linear dynamics to minimize losses and improve wind energy efficiency. Wind speed data from the Bandırma meteorological station in the Marmara region of Turkey, known for its wind energy potential, was decomposed into intrinsic mode functions (IMFs) using robust empirical mode decomposition (REMD). The extracted IMFs were then fed into a long short-term memory (LSTM) architecture whose parameters were estimated using the African vultures optimization (AVO) algorithm based on tent chaotic mapping. This approach aimed to build a highly accurate wind speed forecasting model. The performance of the proposed optimization algorithm in improving the model parameters was compared with that of the chaotic particle swarm optimization (CPSO) algorithm. Finally, the study highlights the potential of utilizing advanced optimization techniques and deep learning models to improve wind speed forecasting, ultimately contributing to more efficient and sustainable wind energy generation. This robust hybrid model represents a significant step forward in wind energy research and its practical applications.
近年来，由于化石燃料温室气体排放量的增加，气候变化的全球影响日益显著。这种趋势威胁着水资源、生态平衡，并可能导致荒漠化和干旱。为应对这些挑战，减少化石燃料消耗并采用可再生能源至关重要。其中，风能作为一种清洁的可再生能源，日益受到更多关注。然而，风速的多变性和不可预测性给将风能并入电网带来了挑战。准确的风速预测对于克服这些障碍和优化风能利用至关重要。本研究专注于开发一种强大的风速预测模型，该模型能够处理非线性动态，以尽量减少损失并提高风能效率。来自土耳其马尔马拉地区班德尔马气象站的风速数据，因其风能潜力而闻名，使用稳健经验模态分解（REMD）将其分解为固有模态函数（IMF）。然后将提取的IMF输入到一个长短期记忆（LSTM）架构中，其参数使用基于帐篷混沌映射的非洲秃鹫优化（AVO）算法进行估计。这种方法旨在构建一个高度准确的风速预测模型。将所提出的优化算法在改进模型参数方面的性能与混沌粒子群优化（CPSO）算法的性能进行了比较。最后，该研究强调了利用先进优化技术和深度学习模型来改进风速预测的潜力，最终有助于实现更高效和可持续的风能发电。这种强大的混合模型代表了风能研究及其实际应用向前迈出的重要一步。
