# Management of Distributed Renewable Energy Resources with the Help of a Wireless Sensor Network
## 1 Introduction
Wireless sensor networks have been widely employed to gather mission-critical information from various environments. Applications of wireless sensor networks (WSNs) are diverse, including spectrum sensing [24] and broadband data transmission [25]. A study in [26] proposed ferrite position identification technology that offers precise train location information and is unaffected by the wireless power transfer of electromagnetic interference. For data collection in PV and wind applications, the Internet of Things (IoT) and WSN, such as the IPv6-centric time-slotted channel hopping (6 TiSCH) wireless network, can play a key role. Link scheduling strategies for 6 TiSCH wireless networks have been examined in different studies. Despite the risk of a single point of failure at the sink node, a study on 6 TiSCH wireless networks uses tree topology as it fits well in dense networks. Aloha-based scheduling and reservation-based scheduling algorithms were presented in [27]. The first one allocated a frequency channel for broadcasting advertisements for new neighbors and the latter one augmented Aloha-based scheduling with a dedicated slot for targeted advertisements based on gossip information. A decentralized adaptive multi-hop scheduling protocol for 6 TiSCH wireless networks (DeAMON) was examined in [28]. When compared with earlier studies, the DeAMON guaranteed allocated scheduling with the least communication overhead between adjacent nodes. The relevance of a real-time weather monitoring system is expanding, and precise weather prediction has a crucial function for a PV or wind power plant. The IoT technologies are fast evolving, gaining popularity, and being mostly utilized in remote locations to collect data from the outside world and transfer it to a cloud server on a regular basis. The microcontroller, communication and sensing device, memory, and constrained battery capacity are all included in the sensor node. The sensor network is used in a variety of applications, including target tracking, event detection, and weather forecasting for renewable energy systems [29]. Accurate PV and wind power prediction are vital for ensuring safe operation and cost-effectiveness in integrating electrical power networks with PV and wind energy systems. The optimal allotment of these PV and wind power frameworks is necessary during the planning stage, which necessitates the precise speculation of natural circumstances at their suggested positions [30]. By predicting the power output from PV and wind systems, the operators can keep track of the performance, take control actions, dispatch various distributed grids optimally, and monitor voltage control devices. The precise determination of PV and wind power production is often a perplexing task because of the fluctuating nature of solar intensity and weather. Field testing in [31] reveals that due to multi-path, fading, and noise, the connection quality of wireless networks in various smart grid locations, such as outdoor substations, power control rooms, and subterranean network transformer vaults, varies substantially with location and time. In addition to these concerns, the capacity of the WSN is limited in smart grid scenarios and other applications due to battery drain [32], wireless medium contention, and RF interference caused by concurrent transmissions. The above factors motivated us to investigate the performance and implications of link scheduling in WSNs. To address this issue, a link scheduling technique based on node attributes to execute the 6 TiSCH wireless network with collision-free link transmission is adopted. The performance/error indices of different studies for renewable energy forecasting are shown in Table 1.  
Until now, various strategies for improving the accuracy of PV and wind power prediction have been developed [10; 37]. PV power prediction models, proposed in [35; 36; 38] are only useful when the data at a specific time is ready ahead of assessing the PV power output.