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Nowadays, electric load forecasting through a data analytic approach has become one of the most active and emerging research areas. It provides future consumption patterns of electric load. Since there are large fluctuations in both electricity production and use, it is a difficult task to achieve a balance between electric load and demand. By analyzing past electric consumption records to estimate the upcoming electricity load, the issue of fluctuating behavior can be resolved. In this study, a framework for feature selection, extraction, and regression is put forward to carry out the electric load prediction. The feature selection phase uses a combination of extreme gradient boosting (XGB) and random forest (RF) to determine the significance of each feature. Redundant features in the feature extraction approach are removed by applying recursive feature elimination (RFE). We propose an enhanced support vector machine (ESVM) and an enhanced convolutional neural network (ECNN) for the regression component. Hyperparameters of both the proposed approaches are set using the random search (RS) technique. To illustrate the effectiveness of our proposed strategies, a comparison is also performed between the state-of-the-art approaches and our proposed techniques. In addition, we perform statistical analyses to prove the significance of our proposed approaches. Simulation findings illustrate that our proposed approaches ECNN and ESVM achieve higher accuracies of 98.83% and 98.7%, respectively. [ABSTRACT FROM AUTHOR]

Energy-efficient management and highly reliable communication and transmission mechanisms are major issues in Underwater Wireless Sensor Networks (UWSN) due to the limited battery power of UWSN nodes within an harsh underwater environment. In this paper, we integrate the three main techniques that have been used for managing Transmission Power-based Sparsity-conscious Energy-Efficient Clustering (CTP-SEEC) in UWSNs. These incorporate the adaptive power control mechanism that converts to a suitable Transmission Power Level (TPL), and deploys collaboration mobile sinks or Autonomous Underwater Vehicles (AUVs) to gather information locally to achieve energy and data management efficiency (Security) in the WSN. The proposed protocol is rigorously evaluated through extensive simulations and is validated by comparing it with state-of-the-art UWSN protocols. The simulation results are based on the static environmental condition, which shows that the proposed protocol performs well in terms of network lifetime, packet delivery, and throughput. [ABSTRACT FROM AUTHOR]