Reinforcement Learning-Based BEMS Architecture for Energy Usage Optimization
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/17/4918 |
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| author | Sanguk Park Sangmin Park Myeong-in Choi Sanghoon Lee Tacklim Lee Seunghwan Kim Keonhee Cho Sehyun Park |
| author_facet | Sanguk Park Sangmin Park Myeong-in Choi Sanghoon Lee Tacklim Lee Seunghwan Kim Keonhee Cho Sehyun Park |
| author_sort | Sanguk Park |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T16:41:50Z |
| format | Article |
| id | doaj.art-57f999d4eb964eaf8f6956f8a158d322 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T16:41:50Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-57f999d4eb964eaf8f6956f8a158d3222023-11-20T11:58:18ZengMDPI AGSensors1424-82202020-08-012017491810.3390/s20174918Reinforcement Learning-Based BEMS Architecture for Energy Usage OptimizationSanguk Park0Sangmin Park1Myeong-in Choi2Sanghoon Lee3Tacklim Lee4Seunghwan Kim5Keonhee Cho6Sehyun Park7School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, KoreaSchool of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, KoreaSchool of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, KoreaSchool of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, KoreaSchool of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, KoreaSchool of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, KoreaSchool of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, KoreaSchool of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, KoreaCurrently, 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.https://www.mdpi.com/1424-8220/20/17/4918reinforcement learning (RL)artificial intelligence (AI)building energy management system (BEMS)energy optimizationinternet of things (IoT) |
| spellingShingle | Sanguk Park Sangmin Park Myeong-in Choi Sanghoon Lee Tacklim Lee Seunghwan Kim Keonhee Cho Sehyun Park Reinforcement Learning-Based BEMS Architecture for Energy Usage Optimization Sensors reinforcement learning (RL) artificial intelligence (AI) building energy management system (BEMS) energy optimization internet of things (IoT) |
| title | Reinforcement Learning-Based BEMS Architecture for Energy Usage Optimization |
| title_full | Reinforcement Learning-Based BEMS Architecture for Energy Usage Optimization |
| title_fullStr | Reinforcement Learning-Based BEMS Architecture for Energy Usage Optimization |
| title_full_unstemmed | Reinforcement Learning-Based BEMS Architecture for Energy Usage Optimization |
| title_short | Reinforcement Learning-Based BEMS Architecture for Energy Usage Optimization |
| title_sort | reinforcement learning based bems architecture for energy usage optimization |
| topic | reinforcement learning (RL) artificial intelligence (AI) building energy management system (BEMS) energy optimization internet of things (IoT) |
| url | https://www.mdpi.com/1424-8220/20/17/4918 |
| work_keys_str_mv | AT sangukpark reinforcementlearningbasedbemsarchitectureforenergyusageoptimization AT sangminpark reinforcementlearningbasedbemsarchitectureforenergyusageoptimization AT myeonginchoi reinforcementlearningbasedbemsarchitectureforenergyusageoptimization AT sanghoonlee reinforcementlearningbasedbemsarchitectureforenergyusageoptimization AT tacklimlee reinforcementlearningbasedbemsarchitectureforenergyusageoptimization AT seunghwankim reinforcementlearningbasedbemsarchitectureforenergyusageoptimization AT keonheecho reinforcementlearningbasedbemsarchitectureforenergyusageoptimization AT sehyunpark reinforcementlearningbasedbemsarchitectureforenergyusageoptimization |