  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.

Machine Learning