# Management of Distributed Renewable Energy Resources with the Help of a Wireless Sensor Network
## 4 Results and Discussion  
This section presents the efficacy of the proposed KRLS forecasting algorithm and the proposed link scheduling model. Generally, the WSN has sensor nodes and a sink node. The data collected by the cloud data center are used for regression techniques: GPR, LRM, SVM, TR, NN, and LSTM network. Data on PV and wind power on a half-hourly basis was acquired from the Korea Meteorological Administration in South Korea (from 1 January 2019 to 31 December 2019). In total, 70% of the data for one day was selected to be utilized as training and validation data in the proposed forecasting models' training phase, while the remaining 30% was used to evaluate the prediction performances. MATLAB R2022a was used for WSN and the proposed link scheduling technique. In this section, the proposed PV and wind forecasting model is compared with prediction methods by using regression techniques. To achieve effective power prediction performance, the proposed forecasting model for PV and wind power production utilizes a large training dataset. The prediction result for different regression learning models for both PV and wind systems, viz., GPR, LR, SVM, TR, NN, LSTM, and the proposed forecasting model are shown in Tables 2 and 3. Figures 6-11 represent the actual, training, and forecasting of PV energy using GPR, LR, SVM, TR, NN, and LSTM techniques. Figures 12-17 represent the actual, training, and forecasting of wind energy using GPR, LR, SVM, TR, NN, and LSTM techniques. The plots in Figures 18 and 19 show the results obtained by using the proposed KRLS model. As shown in Table 2, the linear regression (LR) model for the PV energy prediction underperforms in terms of the MAE and RMSE when compared with the proposed forecasting model. Among all the models in this study, the SVM-based model, as shown in Figure 8, shows poor performance for PV power prediction, the TR model, shown in Figure 14, has poor performance for wind power prediction; while the proposed forecasting models shown in Figures 18 and 19 demonstrate the best performance in both cases. In terms of MAE, the proposed forecasting model for the wind system shown in Table 3 outperforms LR, TR, GPR, SVM, LSTM, and NN by 6.07%, 20.28%, 7.49%, 20.4%,  
The actual and trained curves shown in Figures 18 and 19 marked with blue and red lines follow the same trajectory, indicating the accuracy of the proposed KRLS model. Figure 19: Actual, training, and prediction of wind power using the proposed KRLS algorithm. Figure 17: Actual, training, and prediction of wind power using LSTM. Figure 18: Actual, training, and prediction of PV power using the proposed KRLS algorithm. Green lines in Figures 18 and 19 indicate the prediction curve and the accuracy of the prediction is verified by the different error indices shown in the Tables 2 and 3. In terms of MAE, the proposed KRLS model for the PV system outperforms LR, TR, GPR, SVM, LSTM, and NN by 99.75%, 98.73%, 98.76%, 99.45%, 99.75%, 97.58%, and 98.61%, respectively, while the improvement ratios in terms of RMSE are 86.15%, 53.48%, 63.54%, 65.93%, 19.38%, and 30.84, respectively. The error indices are assessed and reported in Tables 2 and 3 to validate the performance of the proposed forecasting model for PV and wind power. Moreover, in Tables 2 and 3, the hourly prediction dataset of PV power produces an RMSE value of 0.0146, MAE of 0.000211, and R\({}^{2}\) score of 99.7%. The results demonstrate that the proposed forecasting model of PV and wind power outperforms other statistical and ML techniques. Simulations for cloud data consumption in the WSN are implemented to verify the performance of the proposed link scheduling. In Figure 20, cell usage of the proposed algorithm, DeAMON with priority, and DeAMON are shown. The proposed algorithm requires only 67 time slots including the first 11 time slots of the slot frame, whereas the DeAMON needs 86 time slots. The simulation result shows that by the proposed technique the cell utilization or usage in each timeslot is generally higher than that of the DeAMON.