Causal-Aware Time Series Regression for IoT Energy Consumption Using Structured Attention and LSTM Networks

This study focuses on energy consumption modeling for IoT devices and proposes a regression prediction method that integrates causal features with Long Short-Term Memory (LSTM) networks. The method introduces a structured causal attention mechanism to model the influence of external factors on energy consumption. It combines this with LSTM to dynamically learn temporal dependencies, forming a structure-enhanced prediction framework. At the input layer, the model integrates multiple features such as device operating states, transmission parameters, and task identifiers. A causal mask guides the computation of attention weights to enhance the model’s sensitivity to key variables. At the prediction layer, a fusion mechanism combines the LSTM outputs with the weighted causal features as input to the regression module, producing the final energy consumption predictions. The experiments are conducted on a real industrial IoT dataset. Performance is evaluated under different network depths, training data proportions, and environmental fluctuations through sensitivity analysis. Results show that the proposed model outperforms traditional baseline models on metrics including RMSE, MAE, and MAPE. It demonstrates higher accuracy and robustness. The study also shows that the causal modeling mechanism plays a key role in improving model stability and enhancing the ability to capture complex dependencies. The method is suitable for predicting energy consumption patterns across diverse devices and nonlinear usage behaviors.