# Management of Distributed Renewable Energy Resources with the Help of a Wireless Sensor Network
## 5 Conclusions  
Many governments and experts throughout the world are focusing their efforts on developing new alternative energy sources that are eco-friendly. One of the most popular solutions is to use renewable energy sources, which has been proven to be sustainable, cost-effective, abundant, non-polluting, and renewable. However, the nature of energy generated by renewable energy sources is sporadic and has created various obstacles. This work proposes a forecasting model for PV and wind power production, utilizing the PV and wind data collected by the cloud data center. The forecasting model was evaluated and validated using real-time data collected by the cloud data center. Moreover, a link scheduling algorithm for the 6 TiSCH wireless network was proposed for efficient cell usage of channel offsets in WSNs. The real data was used by the KRLS algorithm and other regression techniques. The proposed KRLS forecasting model was validated by calculating and analyzing different types of error/performance indices, including RMSE, MAE, and R\({}^{2}\). Using the real-world dataset, the forecasting performance of the proposed KRLS forecasting model was compared with other ML and regression models. All the different regression models underperform relative to the simulated results, however, the proposed KRLS forecasting model shows an impressively executed performance with high accuracy in terms of RMSE, MAE, and R\({}^{2}\) values. For the majority of the experimental parameters, the SVM-based model underperforms. The proposed KRLS forecasting model outperformed other regression models in all assessment events, with an RMSE value of 0.0146, MAE value of 0.00021, and R\({}^{2}\) of 99.7% for PV energy, and RMSE value of 0.0421, MAE value of 0.0018, and R\({}^{2}\) of 88.17% for wind energy. The proposed link scheduling algorithm for 6 TiSCH WSN adapts to topology changes in the network and reconfigures the scheduling, providing high reliability in dynamic environments. The proposed link scheduling algorithm incurs 22% lower latency and 38% higher resource utilization through the efficient scheduling of time slots. The link scheduling algorithm can provide a viable solution for a variety of industrial control and monitoring applications. Integration of the ESS with the proposed KRLS forecasting scheme will be a focus of future work as well as considering different environmental factors for PV and wind power forecasting. In addition  
Figure 23: A comparison of the forecasting performance (wind power) based on proposed link scheduling according to LSP having 16 available channel offsets for the 6 TiSCH network shown in Figure 2.  
to this, we hope to further improve the deep learning process and wireless sensor network communication dependability.  
Conceptualization, S.H.N.; methodology, S.H.N.; software, S.H.N. and S.L.; validation, S.H.N. and S.L.; formal analysis, S.H.N., H.J. and S.L.; investigation, S.H.N. and S.L.; resources, S.H.N., H.J. and S.L.; data curation, S.H.N., H.J. and S.L.; writing--S.H.N.; writing--review and editing, S.H.N. and H.J.; visualization, S.H.N.; supervision, S.H.N. and S.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This work was supported by the Institute for Information communications Technology Promotion (IITP) grant funded by the Korean government (MSIT) (No. 2020-0-00440, Development of Artificial Intelligence Technology that continuously improves itself as the situation changes in the real world). This work was also supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20192010107290).  
The authors