Energy Consumption Optimization Method of Intelligent Internet of Things Building Based on Big Data Analysis

This article explores methods for optimizing energy consumption in smart buildings using big data analysis and Internet of Things (IoT) technology. The research initiates with the extraction of pivotal attributes-including time, ambient temperature, humidity levels, and illumination intensity-via the processes of data cleansing and preparation. Following this, both descriptive and inferential statistical methodologies are applied to investigate the interplay between energy usage and a range of contributing elements. For predicting future energy consumption patterns of structures, the investigation employs the Nonlinear Autoregressive (NAR) neural network approach alongside the Long Short-Term Memory (LSTM) network to develop a predictive model. Central to this endeavor is the Intelligent Energy Management System (IEMS), which harnesses AI and machine learning (ML) techniques to facilitate real-time surveillance, precise forecasting, and smart optimization of a building's energy consumption metrics. By leveraging IoT technology, the IEMS gathers consumption data, applies advanced big data analytics combined with ML algorithms for processing and interpretation, and devises optimal strategies for energy utilization predicated on forecast outcomes and external influences. Empirical findings indicate that the deployment of these optimization tactics has led to a reduction in overall building energy consumption by approximately 20.35 %, with the most pronounced effect observed in air conditioning energy use, which saw a decrease of 25.23%. This research shows that the intelligent building energy consumption management system combining big data analysis and IoT technology has remarkable effects in improving energy utilization efficiency and reducing energy consumption, which provides strong technical support and practical reference for energy consumption optimization of intelligent buildings in the future.