| Title |
Comparison of electricity savings in community units through ESS and PV generation using ANN-based prediction model under Korean climatic conditions |
| ID_Doc |
43022 |
| Authors |
Hong, SH; Seo, B; Jeon, HS; Choi, JM; Lee, KH; Rim, D |
| Title |
Comparison of electricity savings in community units through ESS and PV generation using ANN-based prediction model under Korean climatic conditions |
| Year |
2024 |
| Published |
|
| DOI |
10.1007/s12206-024-0739-z |
| Abstract |
Electrical energy saving was evaluated by taking advantage of PV and ESS in a community unit. An artificial neural network (ANN) and long short-term memory (LSTM) were employed to create a predictive model for PV generation. Annual demand data for residential buildings were estimated using EnergyPlus, while data for other buildings were collected from measurements in J Energy Town, Republic of Korea. Pearson correlation coefficients identified six crucial variables for the model. Comparative analysis of 310 cases revealed that the best-performing model was an ANN with three hidden layers and nodes of 14, 13 and 11. The model satisfied ASHRAE guidelines with a CV(RMSE) of 29.1 % and NMBE of -7.14 %. Evaluating electricity consumption in the community, case B (PV generation) showed a significant 46.3 % reduction compared to case A, while case D achieved a 5 % energy savings relative to case E over the year. |
| Author Keywords |
ANN; LSTM; PCC; Smart city; ESS |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001284609400002 |
| WoS Category |
Engineering, Mechanical |
| Research Area |
Engineering |
| PDF |
|