Management of Distributed Renewable Energy Resources with the Help of a Wireless Sensor Network

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.

Energy Prediction for Energy-Harvesting Wireless Sensor: A Systematic Mapping Study

“…Nengroo et al [22] employed the kernel recursive least squares (KRLS), a prediction model, to address distributed renewable energy-prediction problems. The KRLS model, based on the recursive least squares method with a kernel function, exhibits remarkable performance in regression problems and finds extensive application in smart grid scenarios.…”

Mathematical approach of fiber optics for renewable energy sources using general adversarial networks

“…Additionally, some mathematical approaches-which are based solely on data already present for fiber optic communicationsare created to ensure steady functioning during the entire process (Aslian et al, 2016;Nengroo et al, 2022). All fiber optic and energy source integration cannot be carried out in real time by using related datasets alone because various optimization techniques are required.…”

Special Issue on Advanced Wireless Sensor Networks for Emerging Applications

“…In [29], a combined prediction model, which combines a back propagation neural network with a genetic algorithm and particle swarm optimization, respectively, is used for wind farm energy harvesting based on the weather data gathered by the WSN. Additionally, for efficient data transmission of PV and wind power production data predicted by kernel recursive least-square (KRLS) algorithm, a link scheduling method based on sensor node attributes is presented in [30].…”