Deep learning integration optimization of electric energy load forecasting and market price based on the ANN–LSTM–transformer method

Abstract

Introduction: Power load forecasting and market price analysis have become crucial in the context of complex power energy systems and volatile market prices. Deep learning technology has gained significant attention in time series forecasting, and this article aims to enhance the accuracy and reliability of power load and market price predictions by integrating and optimizing deep learning models.Methods: We propose a deep learning framework that combines artificial neural networks (ANNs), long short-term memory (LSTM), and transformer models to address key challenges in electricity load forecasting and market price prediction. We leverage ANNs for their versatility and use LSTM networks for sequence modeling to generate initial predictions. Additionally, we introduce transformer technology and utilize its self-attention mechanism to capture long-distance dependencies within the data, further enhancing the model’s performance.Results: In our experiments, we validate the proposed framework using multiple public datasets. We compare our method with traditional forecasting approaches and a single-model approach. The results demonstrate that our approach outperforms other methods in predicting power load and market prices. This increased accuracy and reliability in forecasting can be of significant value to decision-makers in the energy sector.Discussion: The integration of deep learning models, including ANN, LSTM, and transformer, offers a powerful solution for addressing the challenges in power load and market price prediction. The ability to capture long-distance dependencies using the transformer's self-attention mechanism improves forecasting accuracy. This research contributes to the field of energy and finance by providing a more reliable framework for decision-makers to make informed choices in a complex and dynamic environment.