Solar energy conversion efficiency has improved by the advancement technology of photovoltaic (PV) and the involvement of administrations worldwide. However, environmental conditions influence PV power output, resulting in randomness and intermittency. These characteristics may be harmful to the power scheme. As a conclusion, precise and timely power forecast information is essential for the power networks to engage solar energy. To lessen the negative impact of PV electricity usage, the offered short-term solar photovoltaic (PV) power estimate design is based on an online sequential extreme learning machine with a forgetting mechanism (FOS-ELM) under this study. This approach can replace existing knowledge with new information on a continuous basis. The variance of model uncertainty is computed in the first stage by using a learning algorithm to provide predictable PV power estimations. Stage two entails creating a one-of-a-kind PI based on cost function to enhance the ELM limitations and quantify noise uncertainty in respect of variance. As per findings, this approach does have the benefits of short training duration and better reliability. This technique can assist the energy dispatching unit list producing strategies while also providing temporal and spatial compensation and integrated power regulation, which are crucial for the stability and security of energy systems and also their continuous optimization.

中文翻译：

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基于FOS-ELM算法的太阳能光伏短期功率优化预测区间模型

由于光伏 (PV) 的先进技术和全球政府的参与，太阳能转换效率得到了提高。然而，环境条件影响光伏功率输出，导致随机性和间歇性。这些特性可能对电源方案有害。总之，准确及时的电力预测信息对于电网利用太阳能至关重要。为了减轻光伏电力使用的负面影响，本研究中提供的短期太阳能光伏 (PV) 功率估算设计基于具有遗忘机制的在线顺序极限学习机 (FOS-ELM)。这种方法可以不断地用新信息替换现有知识。在第一阶段通过使用学习算法来计算模型不确定性的方差，以提供可预测的 PV 功率估计。第二阶段需要基于成本函数创建独一无二的 PI，以增强 ELM 限制并量化关于方差的噪声不确定性。根据调查结果，这种方法确实具有训练时间短和可靠性更高的优点。该技术可以辅助能源调度单元列表生成策略，同时提供时空补偿和综合功率调节，这对于能源系统的稳定性和安全性及其持续优化至关重要。第二阶段需要基于成本函数创建独一无二的 PI，以增强 ELM 限制并量化关于方差的噪声不确定性。根据调查结果，这种方法确实具有训练时间短和可靠性更高的优点。该技术可以辅助能源调度单元列表生成策略，同时提供时空补偿和综合功率调节，这对于能源系统的稳定性和安全性及其持续优化至关重要。第二阶段需要基于成本函数创建独一无二的 PI，以增强 ELM 限制并量化关于方差的噪声不确定性。根据调查结果，这种方法确实具有训练时间短和可靠性更高的优点。该技术可以辅助能源调度单元列表生成策略，同时提供时空补偿和综合功率调节，这对于能源系统的稳定性和安全性及其持续优化至关重要。