Short-term load forecasting for power systems with high-penetration renewables based on multivariate data slicing transformer neural network

Abstract

Introduction: The characteristics of intermittency and volatility brought by a high proportion of renewable energy impose higher requirements on load forecasting in modern power system. Currently, load forecasting methods mainly include statistical models and machine learning methods, but they exhibit relative rigidity in handling the uncertainty, volatility, and nonlinear relationships of new energy, making it difficult to adapt to instantaneous load changes and the complex impact of meteorological factors. The Transformer model, as an algorithm used in natural language processing, with its self-attention mechanism and powerful nonlinear modeling capability, can help address the aforementioned issues.Methods: However, its current performance in time series processing is suboptimal. Therefore, this paper improves the Transformer model through two steps, namely, Data-Slicing and Channel-independence, enhancing its adaptability in load forecasting.Results: By using load data from Northern Ireland as an example, we compared GRU, CNN, and traditional Transformer models. We validated the effectiveness of this algorithm in short-term load forecasting using MAPE and MSE as indicators.Discussion: The results indicate that, in short-term load forecasting, the MDS method, compared to GRU, CNN, and traditional Transformer methods, has generally reduced the MSE by over 48%, and achieved a reduction of over 47.6% in MAPE, demonstrating excellent performance.