A coupled framework for power load forecasting with Gaussian implicit spatio temporal block and attention mechanisms network

Abstract

The increasing demand for electricity underscores the need for accurate power load forecasting to optimize grid management and resource allocation. With the emergence of more complex multi-energy hybrid systems, the resulting multivariate power load data pose significant challenges for precise forecasting. To address this, we propose a novel framework that integrates Variational Mode Decomposition (VMD) with an Encoder–Decoder architecture featuring customized Gaussian Implicit Spatio-Temporal (GIST) blocks to uncover implicit spatial dependencies across temporal and multi-feature dimensions. Initially, VMD decomposes the original time series into multiple resolution components, effectively reducing noise and extracting intrinsic temporal patterns. These components are then processed by an Encoder–Decoder network for prediction. Within each GIST block, token embedding is applied to the input before being fed into a Gaussian Mixture Model (GMM)-based implicit spatio-temporal representation module. Unlike conventional expectation–maximization (EM) algorithms, our learned Gaussian modeling approach provides a more adaptive and computationally efficient alternative for residential power load forecasting. Temporal dependencies are further captured through Long Short-Term Memory (LSTM) units and attention mechanisms across subsequent blocks, enhancing the model’s predictive capability. Experimental validation demonstrates the superior performance of our proposed model, achieving reductions of 7.98% and 9.32% in Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), respectively, compared to existing forecasting models. Notably, our GMM-based approach outperforms traditional two-dimensional convolution-based methods, yielding improvements of 11.3% and 5.72% in MAE and RMSE, highlighting the efficacy of our framework in handling complex multivariate power load data.