Multi-Objective Optimization and Hybrid HHO-RF for Data Prediction and Performance Improvement in WSN

An enhancement of the data prediction for photovoltaic (PV) applications using Wireless Sensor Networks is an area of study in the study. Some threats affecting environmental meteorological data collected by WSNs in PV plants include; the high-power consumptions, presence of data noise or missing and problems in network. To this end, this chapter proposes an enhanced HHO-RF hybrid prediction approach that minimizes coverage error while maximizing prediction gains. AT, the ST, and SI associated with the photovoltaic effectiveness of PV systems can be estimated using the HHO-RF approach. HHO in the model enhances optimised power usage so that accuracy in data is achieved without compromising on the RF parameters. Model performance is evaluated using several statistical measures include the RMSE and the MAE. Compared with the conventional ways of LR, SVM, and RF techniques, this model is better. By analysing the results, the author can claim that the suggested method is not only successful in increasing the lifetime of the WSN nodes, but also handling the balance between recognizing WSN credibility in forecasting and its energy consumption, making the method practical for real-time weather monitoring.