A Training-free Neural Architecture Search for Electricity Load Prediction

Short-term load forecasting (STLF) in smart grids (SG) is crucial for maintaining grid stability and improving energy efficiency.As electricity usage patterns become more complex, traditional statistical methods might not be able to achieve predictive accuracy.That is why machine learning and deep learning become alternative solutions in recent years.However, designing high-quality neural network architectures is time-consuming and requires significant expert knowledge.This study proposes using training-free neural architecture search techniques to address this challenge by an architecture encoding method, a genetic algorithm, and a surrogate model.The proposed method discovered neural network architectures that performed well on two SG datasets according to the mean absolute error metric while showing average performance in root mean square error and normalized root mean square error metrics.Established a benchmark for the STLF task in SG, available at https://github.com/tinghsuan1214/Benchmark.