Research on Building Electrical Energy Consumption Prediction Based on an Improved Neural Network Algorithm

This study focuses on predicting building electrical energy consumption using an improved neural network algorithm. Accurate energy consumption forecasting is crucial for optimizing energy management, reducing costs, and enhancing the efficiency of electrical systems within buildings. Traditional predictive models often fail to capture the complex non-linear relationships and temporal dynamics inherent in energy consumption data. To overcome these limitations, an advanced neural network model is proposed, which incorporates enhanced algorithms for improved performance and predictive accuracy. The model integrates multiple features such as weather conditions, building occupancy, time-of-day, and historical energy usage patterns to better account for various influencing factors. Experimental results show that the improved neural network algorithm significantly outperforms traditional methods in terms of prediction accuracy, demonstrating its potential for real-time energy forecasting. This enhanced predictive model offers valuable insights for smart building energy management, contributing to more efficient and sustainable energy use in modern buildings.