This paper presents the application of an adaptive neuro-fuzzy inference
system (ANFIS) to predict the generated electrical power in a combined cycle
power plant. The ANFIS architecture is implemented in matlab through a code
that utilizes a hybrid algorithm that combines gradient desc
本文提出了一种方法学习如何在能源领域中开发,部署和评估 AI 系统,以提高其可靠性、可控性和解释性,使用电力系统事件预测(PEF)作为示例应用,通过对同步相量测量单元(PMUs)测量的相量数据进行物理理解和采用基于机器学习的算法来预测所需需求,将物理维度与机器学习模型融合,实现降维和提高预测精度的目标。