Atwa Walid, Almazroi Abdulwahab Ali, Ayub Nasir
College of Computing and Information Technology at Khulais, Department of Information Technology, University of Jeddah, Jeddah, Saudi Arabia.
Department of Creative Technologies, Air University, Islamabad, Pakistan.
PeerJ Comput Sci. 2024 May 23;10:e2067. doi: 10.7717/peerj-cs.2067. eCollection 2024.
Accurate prediction of electricity generation from diverse renewable energy sources (RES) plays a pivotal role in optimizing power schedules within RES, contributing to the collective effort to combat climate change. While prior research often focused on individual energy sources in isolation, neglecting intricate interactions among multiple sources, this limitation frequently leads to inaccurate estimations of total power generation. In this study, we introduce a hybrid architecture designed to address these challenges, incorporating advanced artificial intelligence (AI) techniques. The hybrid model seamlessly integrates a gated recurrent unit (GRU) and a ResNext model, and it is tuned with the modified jaya algorithm (MJA) to capture localized correlations among different energy sources. Leveraging its nonlinear time-series properties, the model integrates meteorological conditions and specific energy source data. Additionally, principal component analysis (PCA) is employed to extract linear time-series data characteristics for each energy source. Application of the proposed AI-infused approach to a renewable energy system demonstrates its effectiveness and feasibility in the context of climate change mitigation. Results reveal the superior accuracy of the hybrid framework compared to more complex models such as decision trees and ResNet. Specifically, our proposed method achieved remarkable performance, boasting the lowest error rates with a normalized RMSE of 6.51 and a normalized MAPE of 4.34 for solar photovoltaic (PV), highlighting its exceptional precision in terms of mean absolute errors. A detailed sensitivity analysis is carried out to evaluate the influence of every element in the hybrid framework, emphasizing the importance of energy correlation patterns. Comparative assessments underscore the increased accuracy and stability of the suggested AI-infused framework when compared to other methods.
准确预测多种可再生能源（RES）的发电量，对于优化可再生能源内部的电力调度起着关键作用，有助于共同应对气候变化。虽然先前的研究通常孤立地关注单个能源，而忽略了多个能源之间的复杂相互作用，但这种局限性常常导致对总发电量的估计不准确。在本研究中，我们引入了一种混合架构来应对这些挑战，该架构融合了先进的人工智能（AI）技术。该混合模型无缝集成了门控循环单元（GRU）和ResNext模型，并使用改进的jaya算法（MJA）进行调整，以捕捉不同能源之间的局部相关性。该模型利用其非线性时间序列特性，整合了气象条件和特定能源数据。此外，主成分分析（PCA）用于提取每个能源的线性时间序列数据特征。将所提出的注入人工智能的方法应用于可再生能源系统，证明了其在缓解气候变化方面的有效性和可行性。结果表明，与决策树和ResNet等更复杂的模型相比，混合框架具有更高的准确性。具体而言，我们提出的方法取得了显著的性能，太阳能光伏（PV）的归一化均方根误差（RMSE）为6.51，归一化平均绝对百分比误差（MAPE）为4.34，误差率最低，突出了其在平均绝对误差方面的卓越精度。进行了详细的敏感性分析，以评估混合框架中每个元素的影响，强调了能源相关模式的重要性。比较评估强调了与其他方法相比，所建议的注入人工智能的框架具有更高的准确性和稳定性。
