Abstract
This paper investigates and compares the performance of three distinct models for monitoring abnormal electricity consumption behavior: Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and a novel model that incorporates an attention mechanism known as PSO-ATT-LSTM (PAL), which integrates a Particle Swarm Optimization (PSO) algorithm with an attention mechanism into the LSTM framework. The PAL model is specifically designed to enhance forecasting accuracy by optimizing the network parameters through PSO and focusing on significant temporal features via the attention mechanism. This design allows PAL to outperform the other two models in predicting future electricity consumption, particularly in identifying anomalous patterns. The study utilizes a dataset with hourly electricity consumption values and evaluates the models using metrics such as Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The experimental results demonstrate the superiority of the PAL model in terms of predictive accuracy and its ability to capture abnormal consumption behaviors more effectively than SVR and LSTM.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call