| Title |
Developing of Energy Consumption Prediction Model for Integrated System Using Photovoltaic-Thermal and Ground Source Heat Pump Based on Low-Cost Technologies |
| Authors |
Sangmu Bae ; Hyun-Jung Choi ; Gyeong-Seok Choi ; Yujin Nam |
| DOI |
https://doi.org/10.6110/KJACR.2024.36.11.560 |
| Keywords |
심층신경망; 에너지 소비량; 지열 히트펌프; 태양광열 Deep neural network; Energy consumption; Ground source heat pump; Photovoltaic-thermal |
| Abstract |
Research studies on energy and economic benefits of low-cost technologies of the integrated system using photovoltaic-thermal and ground source heat pump have been conducted mainly focusing on design and installation aspects. To maximize benefits of low-cost technologies from an operational perspective, it is necessary to apply AI-based predictive control, which can derive optimal solutions from complex interrelationships of variables. Therefore, this study aimed to develop an energy consumption prediction model for the integrated system using deep neural network, providing a technical foundation and reliable reference data for predictive control. The coefficient of variation of the root mean square error between prediction model results and output parameter was calculated to be 5%. Although there was a variation in energy consumption of the integrated system in output parameter, the prediction model accurately reflected variation, demonstrating a high prediction accuracy. |