Accurately forecasting photovoltaic power generation is essential for the efficient integration of renewable energy into power grids. This paper presents a novel, high-accuracy framework for short-term photovoltaic productivity forecasting, tailored to the climatic conditions of the Algerian region of El-Oued. The framework automatically adapts the neural network forecast using a nature-inspired algorithm, eliminating the need for manual adjustments. It first addresses the complex, non-stationary nature of photovoltaic generation by incorporating a time-varying filter based on empirical mode decomposition, which decomposes the original photovoltaic data into multiple low-frequency components. Particle swarm optimization is then applied to enhance key elements of the framework, including the neural network structure and input variables such as the extracted components of photovoltaic data and weather parameters, along with their historical values. This optimization process efficiently identifies the near-optimal model structure, resulting in an improved forecaster whose performance is validated using real-world data measured in El-Oued. The proposed framework demonstrates impressive accuracy, with a Normalized Root Mean Squared Error ranging from 2.96% to 4.8% for annual forecasts, 2.28% for summer forecasts, and 4.97% for generalization ability. Similarly, the Normalized Mean Absolute Error ranges from 1.89% to 2.89% for annual forecasts, 1.61% for summer forecasts, and 3.76% for generalization ability. The correlation coefficient is outstanding, between 99.9% and 99.96% for annual forecasts, reaching 99.97% for summer forecasts, and 99.67% for generalization ability. The study confirms the effectiveness of the proposed framework in enhancing network stability and power distribution.

中文翻译：

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通过不断发展的神经网络改进短期光伏发电预测，该神经网络结合了基于经验模态分解的时变过滤


准确预测光伏发电对于将可再生能源高效并入电网至关重要。本文提出了一种新颖的、高精度的短期光伏生产力预测框架，该框架针对阿尔及利亚 El-Oued 地区的气候条件量身定制。该框架使用受自然启发的算法自动调整神经网络预测，无需手动调整。它首先通过结合基于经验模态分解的时变滤波器来解决光伏发电的复杂性、非平稳性，该滤波器将原始光伏数据分解为多个低频分量。然后应用粒子群优化来增强框架的关键元素，包括神经网络结构和输入变量，例如提取的光伏数据组件和天气参数及其历史值。此优化过程有效地识别了近乎最优的模型结构，从而改进了预报器，其性能使用 El-Oued 中测量的真实数据进行验证。所提出的框架表现出令人印象深刻的准确性，年度预报的归一化均方根误差范围为 2.96% 至 4.8%，夏季预报为 2.28%，泛化能力为 4.97%。同样，年度预报的归一化平均绝对误差范围为 1.89% 到 2.89%，夏季预报为 1.61%，泛化能力为 3.76%。相关系数突出，年度预报在 99.9% 到 99.96% 之间，夏季预报达到 99.97%，泛化能力达到 99.67%。 该研究证实了拟议框架在增强网络稳定性和配电方面的有效性。