  Recent years have witnessed exponential growth in developing deep learning
(DL) models for 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
dynamically changed data patterns due to the inflexible design of their
structures. Although several recent studies have considered the 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 computationally expensive and their search strategies are not
flexible, indicating that the NAS application in this area is still at an
infancy stage. In this study, we propose an intelligent automated architecture
search (IAAS) framework for the development of time-series electricity
forecasting models. The proposed framework contains three primary components,
i.e., network function-preserving transformation operation, reinforcement
learning (RL)-based network transformation control, and heuristic network
screening, which aim to improve the search quality of a network structure.
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. Finally, we perform an
ablation experiment to showcase the importance of critical components in the
proposed IAAS framework in improving forecasting accuracy.
