An IoT Framework for Building Energy Optimization Using Machine Learning-based MPC
This provides a pragmatic solution for enhancing energy efficiency and user comfort in pre-existing buildings, particularly legacy systems, though it is incremental as it applies existing methods like ANN and MPC to a specific domain.
The study tackled building energy optimization by proposing a machine learning-based Model Predictive Control (MPC) approach integrated with an IoT framework for Air Handling Unit (AHU) systems, achieving a 57.59% reduction in electricity consumption compared to a manual controller while maintaining user satisfaction.
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.