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학술지 Management of Distributed Renewable Energy Resources with the Help of a Wireless Sensor Network
Cited 12 time in scopus Download 96 time Share share facebook twitter linkedin kakaostory
저자
Sarvar Hussain Nengroo, 진호준, 이상금
발행일
202207
출처
Applied Sciences, v.12 no.14, pp.1-25
ISSN
2076-3417
출판사
MDPI
DOI
https://dx.doi.org/10.3390/app12146908
협약과제
21PR6100, 가정 에너지 사용량 실시간 진단 및 지능형 자율제어/관리 시스템 원천기술 개발, 도윤미
초록
Photovoltaic (PV) and wind energy are widely considered eco-friendly renewable energy resources. However, due to the unpredictable oscillations in solar and wind power production, efficient management to meet load demands is often hard to achieve. As a result, precise forecasting of PV and wind energy production is critical for grid managers to limit the impact of random fluctuations. In this study, the kernel recursive least-squares (KRLS) algorithm is proposed for the prediction of PV and wind energy. The wireless sensor network (WSN) typically adopted for data collection with a flexible configuration of sensor nodes is used to transport PV and wind production data to the monitoring center. For efficient transmission of the data production, a link scheduling technique based on sensor node attributes is proposed. Different statistical and machine learning (ML) techniques are examined with respect to the proposed KRLS algorithm for performance analysis. The comparison results show that the KRLS algorithm surpasses all other regression approaches. For both PV and wind power feed-in forecasts, the proposed KRLS algorithm demonstrates high forecasting accuracy. In addition, the link scheduling proposed for the transmission of data for the management of distributed renewable energy resources is compared with a reference technique to show its comparable performance. The efficacy of the proposed KRLS model is better than other regression models in all assessment events in terms of an RMSE value of 0.0146, MAE value of 0.00021, and R2 of 99.7% for PV power, and RMSE value of 0.0421, MAE value of 0.0018, and R2 of 88.17% for wind power. In addition to this, the proposed link scheduling approach results in 22% lower latency and 38% higher resource utilization through the efficient scheduling of time slots.
KSP 제안 키워드
Data Collection, Data production, Distributed renewable energy resources, Eco-friendly, Least Squares(LS), Machine learning (ml), Monitoring center, PV power, Performance analysis, Random fluctuations, Recursive least squares
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