IoT-based intelligent energy management system for optimal planning of HVAC devices in net-zero emissions PV-battery building considering demand compliance

Abstract

To achieve the goal of carbon neutrality, renewable energy resources are now being widely deployed in the building sector, forming the hybrid energy system. For such hybrid energy system, the development of energy management system (EMS) is of great significance for their maximum utilization. In this study, an Internet of things-based (IoT-based) intelligent EMS is developed for a real net-zero emissions photovoltaic-battery (PV-battery) building, where its main purpose is to optimally schedule the heating, ventilation, and air conditioning (HVAC) devices in order to tradeoff between user comfort level and battery remaining energy. The developed EMS mainly consists of two modules, including data-driven forecasting module and mixed-integer programming module. The data-driven forecasting module is firstly developed to forecast future 24-h PV generation, baseload demands, and room thermal dynamic using several data-driven methods. Then, based on the forecast results, the optimal planning problem of HVAC devices is formulated as a multi-objective optimization problem, which is solved by the mixed-integer programming module. Moreover, a novel concept named as demand compliance is proposed, where the user-defined demand important coefficient is considered in the optimization problem. Comprehensive case studies are conducted to compare the developed EMS and existing rule-based control method. The experimental results demonstrated that the developed EMS could achieve similar user comfort level comparing with rule-based method, while its battery remaining energy was significantly higher. This study might provide a new perspective for the demand-side management program of net-zero emissions building.