An Ensemble Framework for Probabilistic Short-Term Load Forecasting Based on BiTCN and Deep Attention Networks

Abstract

Probabilistic load forecasting is crucial for modern energy systems, providing point forecasts and uncertainty quantification to enhance decision-making and grid reliability. Achieving accurate forecasts often requires deeper hybrid ensemble models to capture power load complexities. However, increasing model depth can introduce challenges like vanishing gradients, hindering learning efficiency. To address this, we propose an ensemble framework integrating Bidirectional Temporal Convolutional Networks (BiTCN) to capture forward and backward temporal dependencies. An attention mechanism highlights key temporal features, improving accuracy, while skip connections mitigate vanishing gradients by preserving early outputs. A classical neural network models forecast distributions, ensuring a comprehensive uncertainty representation. Compared to state-of-the-art models like WaveNet and Transformers models, our framework achieves a 9.3\% improvement in mean standardized error on a public demand dataset of 500,000 data points. These results demonstrate the effectiveness and robustness of the proposed approach, offering a promising solution for probabilistic load forecasting in power systems.