지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.12
- 수록면
- 1 - 12 (12page)
이용수
초록· 키워드
오류제보하기
A Renewable Energy Monitoring System (REMS) was launched to inform facility owners and local governments of failures. However, as the scale of renewable energy deployment increases and its impact on the national grid grows, it is becoming important to forecast power generation for not only commercial solar facilities but also self-consumption solar facilities. As of April 2024, more than 130,000 solar installations have been connected to REMS. It is not economically efficient to forecast the generation of these small installations individually, and the level of accuracy required for self-consuming solar facilities is not as high. Therefore, suitable monitoring techniques are required. In this study, Chungcheongbuk-do, situated midway in the ranking of REMS-connected municipal solar facilities, was selected as the priority target. The objective is to group 11,268 power plants in the Chungcheongbuk-do region and select a representative power plant for each group. The clustering algorithm employs various factors, including latitude, longitude, insolation, altitude, and power generation history, to group power plants. It utilizes both K-means and Autoencoder techniques. First, the location of each solar facility was converted into latitude and longitude values using the address information of the solar facilities connected to REMS. The altitude and insolation information for each location were then extracted based on the latitude and longitude values for clustering analysis. Second, a methodology is proposed using power utilization data from January to December 2023 to identify normally operating facilities and cluster power generation by targeting them.
목차
Abstract
1. 서론
2. 클러스터링 방법
3. 클러스터링 결과
4. 결론
REFERENCES
참고문헌 (11)
참고문헌 신청
KEA, Development of a Plan to Utilize Big Data and Build a Platform for Integrated Renewable Energy Monitoring System, 2022. https://rems.energy.or.kr/admin/monitor/view/monitorCmb. last accessed on 22th May 2024.
Moon, H. and Ahn, J., Forecasting Residential Solar Photovoltaic (PV) Capacity for Net Metering in South Korea, Journal of Climate Change Research, Vol. 14, No. 2, pp. 181-188, 2023.
KEA, Renewable EnergyMonitoring System. https://rems.energy.or.kr/admin/monitor/view/monitorBld. last accessed on 22th May 2024.
Pierro, M., Data-driven Upscaling Methods for Regional Photovoltaic Power Estimation and Forecast Using Satellite and Numerical Weather Prediction Data, Solar Energy, Vol. 158, pp. 1026-1038, 2017.
Lloyd, S. P., Least Square Quantization in PCM, IEEE Transactions on Information Theory, Vol. 28, No. 2, pp. 129-137, 1982.
Moon, H. and Ahn, J., Forecasting Residential Solar Photovoltaic (PV) Capacity for Net Metering in South Korea, Journal of Climate Change Research, Vol. 14, No. 2, pp. 181-188, 2023.
KEA, Renewable EnergyMonitoring System. https://rems.energy.or.kr/admin/monitor/view/monitorBld. last accessed on 22th May 2024.
Pierro, M., Data-driven Upscaling Methods for Regional Photovoltaic Power Estimation and Forecast Using Satellite and Numerical Weather Prediction Data, Solar Energy, Vol. 158, pp. 1026-1038, 2017.
Lloyd, S. P., Least Square Quantization in PCM, IEEE Transactions on Information Theory, Vol. 28, No. 2, pp. 129-137, 1982.
이 논문과 함께 이용한 논문
최근 본 자료
댓글(0)
0
UCI(KEPA) : I410-151-25-02-091246382