Recent years have witnessed an exponential growth in developing deep learning (DL) models for the time-series electricity forecasting in power systems. However, most of the proposed models are designed based on the designers' inherent knowledge and experience without elaborating on the suitability of the proposed neural architectures. Moreover, these models cannot be self-adjusted to the dynamically changing data patterns due to an inflexible design of their structures. Even though several latest studies have considered application of the neural architecture search (NAS) technique for obtaining a network with an optimized structure in the electricity forecasting sector, their training process is quite time-consuming, computationally expensive and not intelligent, indicating that the NAS application in electricity forecasting area is still at an infancy phase. In this research study, we propose an intelligent automated architecture search (IAAS) framework for the development of time-series electricity forecasting models. The proposed framework contains two primary components, i.e., network function-preserving transformation operation and reinforcement learning (RL)-based network transformation control. In the first component, we introduce a theoretical function-preserving transformation of recurrent neural networks (RNN) to the literature for capturing the hidden temporal patterns within the time-series data. In the second component, we develop three RL-based transformation actors and a net pool to intelligently and effectively search a high-quality neural architecture. After conducting comprehensive experiments on two publicly-available electricity load datasets and two wind power datasets, we demonstrate that the proposed IAAS framework significantly outperforms the ten existing models or methods in terms of forecasting accuracy and stability.
翻译:近些年来,在为电力系统的时间序列电力预报开发深层学习(DL)模型方面出现了飞速增长;然而,大多数拟议模型的设计所依据的是设计者固有的知识和经验,没有详细说明拟议的神经结构的适宜性;此外,这些模型由于结构设计不灵活,因此无法对动态变化的数据模式进行自我调整;尽管最近几项研究考虑了神经结构搜索技术的应用,以获得电力预报部门具有优化结构的网络,但其培训过程相当耗时、计算费用昂贵且不智能,表明电力预报领域的NAS应用仍处于初级阶段;在本研究中,我们建议为开发时间序列电预报模型而建立一个智能自动结构搜索框架(IAS),因为其结构设计不灵活。尽管一些最新研究已考虑应用神经结构搜索技术,以获得电力预报部门优化结构的网络网络改造控制。在第一部分中,我们引入了理论性功能维护的二次神经网络网络网络网络预测(RNNN)的转换过程相当费,表明电力预报领域的NAS应用仍然处于初级阶段,在这个研究阶段,我们提出了一个智能结构后的两个数据序列中,一个智能数据结构中,一个是我们正在大量地进行智能数据搜索和智能结构。