An IoT Framework for Building Energy Optimization Using Machine Learning-based MPC

TL;DR

This paper presents an IoT-enabled machine learning-based MPC framework for building energy management, significantly reducing electricity consumption while maintaining user comfort, adaptable even for legacy systems.

Contribution

It introduces a novel IoT framework utilizing ANN for real-time thermal modeling to optimize AHU control with minimal energy use.

Findings

57.59% reduction in electricity consumption

High user satisfaction maintained

Effective in legacy systems

Abstract

This study proposes a machine learning-based Model Predictive Control (MPC) approach for controlling Air Handling Unit (AHU) systems by employing an Internet of Things (IoT) framework. The proposed framework utilizes an Artificial Neural Network (ANN) to provide dynamic-linear thermal model parameters considering building information and disturbances in real time, thereby facilitating the practical MPC of the AHU system. The proposed framework allows users to establish new setpoints for a closed-loop control system, enabling customization of the thermal environment to meet individual needs with minimal use of the AHU. The experimental results demonstrate the cost benefits of the proposed machine-learning-based MPC-IoT framework, achieving a 57.59\% reduction in electricity consumption compared with a clock-based manual controller while maintaining a high level of user satisfaction. The proposed framework offers remarkable flexibility and effectiveness, even in legacy systems with limited building information, making it a pragmatic and valuable solution for enhancing the energy efficiency and user comfort in pre-existing structures.