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Journal Article
Management of Distributed Renewable Energy Resources with the Help of a Wireless Sensor Network
- Authors
- Sarvar Hussain Nengroo, Hojun Jin, Sangkeum Lee
- Issue Date
- 2022-07
- Citation
- Applied Sciences, v.12, no.14, pp.1-25
- ISSN
- 2076-3417
- Publisher
- MDPI
- Language
- English
- Type
- Journal Article
- Abstract
- 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 Keywords
- 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
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
