Abstract
Currently, many intelligent building energy management systems (BEMSs) are emerging for saving energy in new and existing buildings and realizing a sustainable society worldwide. However, installing an intelligent BEMS in existing buildings does not realize an innovative and advanced society because it only involves simple equipment replacement (i.e., replacement of old equipment or LED (Light Emitting Diode) lamps) and energy savings based on a stand-alone system. Therefore, artificial intelligence (AI) is applied to a BEMS to implement intelligent energy optimization based on the latest ICT (Information and Communications Technologies) technology. AI can analyze energy usage data, predict future energy requirements, and establish an appropriate energy saving policy. In this paper, we present a dynamic heating, ventilation, and air conditioning (HVAC) scheduling method that collects, analyzes, and infers energy usage data to intelligently save energy in buildings based on reinforcement learning (RL). In this regard, a hotel is used as the testbed in this study. The proposed method collects, analyzes, and infers IoT data from a building to provide an energy saving policy to realize a futuristic HVAC (heating system) system based on RL. Through this process, a purpose-oriented energy saving methodology to achieve energy saving goals is proposed.
Highlights
To help realize a worldwide sustainable society by saving energy in new and existing buildings, many intelligent building energy management systems (BEMSs) are emerging
BEMS refers to a system that integrates the internet of things (IoT) technology into a building to manage multiple building facilities [1,2,3]
New BEMSs into which smart IoT technology is integrated from the planning stage of building construction are emerging
Summary
To help realize a worldwide sustainable society by saving energy in new and existing buildings, many intelligent building energy management systems (BEMSs) are emerging. The wastage of energy can be reduced by replacing obsolete and expensive facilities with BEMS retrofits in existing buildings; the most reasonable option is to install and apply light and low-cost IoT [6,7,8] devices inside the building to monitor environmental and energy usage information. IoT devices are installed on the building in high proximity to the user and the environment, and they do not require the replacement of heavy and expensive equipment to implement a BEMS in an existing building. By analyzing user and environmental information from large amounts of data collected from IoT devices over a period of time, the most efficient guidelines for saving energy can be applied to the HVAC system. Artificial intelligence (AI) is the most extensively used state-of-the-art technology for data analysis [10,11]
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