Enhanced Home Energy Forecasting With Parallel LSTM Networks

Abstract

The primary obstacles in addressing the energy consumption forecasting challenge revolve around ensuring reliability, stability, efficiency, and accuracy in forecasting methodologies. The current forecasting models face difficulties due to the unpredictable nature of energy consumption data volatility. There is a need for artificial intelligence models that can anticipate abrupt irregular changes and effectively capture long-term dependencies within the data. Within this study, a pioneering AI-boosted forecasting model is presented, combining Extreme Gradient Boosting (XGBoost) with parallel long short-term memory (PLSTM) neural networks. The integration of XGBoost with PLSTM neural networks contributes to the improved performance of the overall PLSTM network. The access the suggested model using the Mean Absolute Percentage Error (MAPE).