Predictive Energy Management in Internet of Things: Optimization of Smart Buildings for Energy Efficiency

Dionisio Ponce Ruiz*, Rita Azucena D. Vasquez, Bolivar Villalta Jadan

Universidad Regional Autónoma de los Andes (UNIANDES), Ecuador

Email: uqdionisioponce@uniandes.edu.ec; ui.ritadiaz@uniandes.edu.ec; us.bolivarvillalta@uniandes.edu.ec

Text Box: Abstract As energy efficiency and sustainability become paramount in the face of growing urbanization and environmental concerns, predictive energy management in smart buildings has emerged as a promising avenue for mitigating energy consumption and optimizing resource utilization. In this paper, we investigate the application of advanced machine learning techniques, particularly a multi-layer Long Short-Term Memory (LSTM) model, within the framework of the Internet of Things (IoT), to predict and manage energy consumption. We rigorously evaluate our approach against a suite of machine learning baselines, including Linear Regression, Random Forest, Support Vector Machine, and Gradient Boosting, utilizing a comprehensive dataset encompassing power consumption data from smart home appliances and associated weather variables. Our experimental results demonstrate the superior predictive capabilities of the LSTM model, showcasing its ability to outperform traditional machine learning baselines across various metrics, including Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). These findings underscore the potential of deep learning models in capturing intricate temporal dependencies within energy consumption data, contributing to improved energy efficiency, cost savings, and environmental sustainability in smart building environments. The integration of predictive energy management models into IoT-enabled smart buildings holds the promise of a more intelligent and sustainable future in urban development and resource management. *Corresponding Author: uqdionisioponce@uniandes.edu.ec

Received: April 02, 2023  Revised: June 22, 2023  Accepted: September 16, 2023

Keywords: Internet of Things (IoT); Smart Buildings, Energy Efficiency; Predictive Energy Management; Sensor Networks; Data Analytics; Smart Grid;  Building Automation and Control Systems (BACS).