Optimization and Analysis of Energy Consumption Prediction Model Based on Feature Engineering

In the context of global energy conservation and efficient utilization, accurate energy consumption forecasting plays a crucial role in promoting sustainable industrial development. Traditional energy consumption forecasting methods predominantly rely on time-series analysis based on statistics. However, when confronted with high-energy-consuming industries that are complex and influenced by multiple factors, these traditional methods often suffer from insufficient prediction accuracy and poor adaptability. This paper conducts an in-depth investigation of energy consumption forecasting models using machine learning techniques, integrating feature engineering and optimization algorithms. Specifically, feature engineering techniques are employed to extract key features that affect energy consumption, such as analyzing the magnitude and distribution of feature coefficients. Meanwhile, optimization algorithms are utilized to iteratively optimize model parameters, aiming to address issues of feature redundancy and low prediction accuracy in traditional models.