Deep Learning-Based Load Forecasting Approachfor Smart Grids
TL;DR
This paper focuses on three kinds of deep learning load forecasting methods: recurrent neural network variants, CNN hybrid architectures, and Transformers, and systematically conducts a comparative analysis of them from three dimensions: prediction accuracy, long-term dependency modeling ability and computational efficiency.
Abstract
The high penetration of renewable energy and the large-scale integration of electric vehicles have significantly enhanced the nonlinearity and non-stationarity of the smart grid load sequence. Traditional time series models struggle to meet the prediction requirements in complex scenarios. This paper focuses on three kinds of deep learning load forecasting methods: recurrent neural network variants, CNN hybrid architectures, and Transformers, and systematically conducts a comparative analysis of them from three dimensions: prediction accuracy, long-term dependency modeling ability and computational efficiency. The results show that the three types of architectures each have distinct advantages, and there is no universal optimal solution: the recurrent variant balances accuracy and efficiency in stationary short-term prediction, the CNN hybrid architecture is more robust in strong coupling scenarios such as extreme weather, and the Transformers' trend modeling ability is outstanding but the computational cost for long sequences is large. The pros and cons of each method are highly dependent on the stationarity of the specific scenario, the forecasting horizon, and resource constraints. The model selection must be based on the task characteristics rather than the pursuit of general solutions.