Electricity load forecasting is an essential part of power system planning and operation, and it is crucial to make accurate predictions. The smart grid paradigm and the new energy market necessitate better demand-side management (DSM) and more reliable end-user forecasts to system scale. This paper proposes a time-series clustering-based probabilistic electricity future prediction for short-term load forecasting (STLF), which makes forecasts more accurate and intelligent. The weather and data noise uncertainties are considered, with the load probabilistic interval as the model’s output for individual and aggregated household energy consumption. This paper adapts the logarithm of the hyperbolic cosine (log-cosh) of the error value as quantile loss and time feature with long short-term memory (LSTM) to bridge the gap. This paper presents a framework for probabilistic electric load forecasting that incorporates clustering-based quantile-LSTM learning to improve the accuracy and robustness of short-term electricity prediction. The proposed model is primarily applied to energy demand information on 15-minute and 1-hour time horizons for day-ahead prediction tasks, which are the key concerns for electricity utilities. Various state-of-the-art regression learning techniques, i.e., quantile regression forest (QRF), quantile regression neural network (QRNN), quantile gradient boosting regression tree (QGBRT), and quantile LSTM (Q-LSTM), were utilized and evaluated. The findings indicate that the proposed cluster-based probabilistic forecasting approach outperforms the existing benchmark models in terms of prediction interval coverage probability (PICP) evaluation metric.

The deep learning (DL) approaches in smart grid (SG) describes the possibility of shifting the energy industry into a modern era of reliable and sustainable energy networks. This paper proposes a time-series clustering framework with multi-step time-series sequence to sequence (Seq2Seq) long short-term memory (LSTM) load forecasting strategy for households. Specifically, we investigate a clustering-based Seq2Seq LSTM electricity load forecasting model to undertake an energy load forecasting problem, where information input to the model contains individual appliances and aggregate energy as historical data of households. The original dataset is preprocessed, and forwarded to a multi-step time-series learning model which reduces the training time and guarantees convergence for energy forecasting. Furthermore, simulation results show the accuracy performance of the proposed model by validation and testing cluster data, which shows a promising potential of the proposed predictive model.

One of the most severe types of cancer caused by the uncontrollable proliferation of brain cells inside the skull is brain tumors. Hence, a fast and accurate tumor detection method is critical for the patient’s health. Many automated artificial intelligence (AI) methods have recently been developed to diagnose tumors. These approaches, however, result in poor performance; hence, there is a need for an efficient technique to perform precise diagnoses. This paper suggests a novel approach for brain tumor detection via an ensemble of deep and hand-crafted feature vectors (FV). The novel FV is an ensemble of hand-crafted features based on the GLCM (gray level co-occurrence matrix) and in-depth features based on VGG16. The novel FV contains robust features compared to independent vectors, which improve the suggested method’s discriminating capabilities. The proposed FV is then classified using SVM or support vector machines and the k-nearest neighbor classifier (KNN). The framework achieved the highest accuracy of 99% on the ensemble FV. The results indicate the reliability and efficacy of the proposed methodology; hence, radiologists can use it to detect brain tumors through MRI (magnetic resonance imaging). The results show the robustness of the proposed method and can be deployed in the real environment to detect brain tumors from MRI images accurately. In addition, the performance of our model was validated via cross-tabulated data.

This paper poses new challenges, especially when designing routing protocols to improve the quality of service (QoS) criteria and the lifetime of large-scale wireless sensor networks (LS-WSNs) and high sensor node density WSNs (HSND-WSN). Some of these challenging problems for routing protocols are minimizing the distance between a base station (BS) and the cluster head (CH), latency in the transmission phase, power consumption in the clustering phase, and determining the precise SN location without error. Thus, for a dynamic WSN, it is important to support an intelligent mobile data collector (MDC) to continue data propagation despite the inevitable changes in the WSN topology. Considering these challenges, we propose a novel intelligent routing protocol based on the improved recursive distance vector-hop (IR-DV-Hop) localization algorithm to determine the accurate location of SNs without errors in LS-WSNs and determine the CHs and optimal route traversed by the MDC for energy efficiency, latency, and reliability. Specifically, the proposed mobile data collector-improved recursive distance vector-hop (MDC-IR-DV-Hop) protocol uses the MDC as an intermediate between the CH and BS to enhance the QoS of WSNs, reduce delays while collecting data, and improve the transmission phase of the routing protocol. Simulation results prove the performance of the proposed protocol compared with the energy aware cluster based multi-hop (EACBM), tree clustering algorithm with mobile data collector (TCMDC), mobile data collector maximum residual energy low energy adaptive clustering hierarchy (MDC maximum residual energy leach), MDC minimum distance leach, mobile data collector-K-means (MDC-K), mobile data collector-traveling salesman problem-low energy adaptive clustering hierarchy-K-means (MDC-TSP-LEACH-K), a three-layer hierarchical architecture for optimized routing protocol based on clustering and the Khalimsky topology for mobile WSN (AMWSN), and node density-based clustering and mobile collection (NDCM) protocols. In addition, this article proposes a comparative analysis of the best clustering, CH election, localization and MDC by discussing and describing their functionalities, advantages, disadvantages and their main performance parameters.

A huge increase in the percentage of the world’s urban population poses resource management especially energy management challenges in smart cities. In this paper, the growing challenges of energy management in smart cities have been explored and significance of elimination of energy holes in converge cast communication has been discussed. The impact of mitigation of energy holes on the network lifetime and energy efficiency has been thoroughly covered. The particular focus of this work has been on energy-efficient practices in two major key enablers of smart cities namely, the Internet of Things (IoT) and Wireless Sensor Networks (WSNs). In addition, this paper presents a robust survey of state-of-the-art energy-efficient routing and clustering methods in WSNs. A niche energy efficiency issue in WSNs routing has been identified as energy holes and a detailed survey and evaluation of various techniques that mitigate the formation of energy holes and achieve balanced energy-efficient routing has been covered.

The involvement of wireless sensor networks in large-scale real-time applications is exponentially growing. These applications can range from hazardous area supervision to military applications. In such critical contexts, the simultaneous improvement of the quality of service and the network lifetime represents a big challenge. To meet these requirements, using multiple mobile sinks can be a key solution to accommodate the variations that may affect the network. Recent studies were based on predefined mobility models for sinks and relied on multi-hop routing techniques. Besides, most of these studies focused only on improving energy consumption without considering QoS metrics. In this paper, multiple mobile sinks with random mobile models are used to establish a tradeoff between power consumption and the quality of service. The simulation results show that using hierarchical data routing with random mobile sinks represents an efficient method to balance the distribution of the energy levels of nodes and to reduce the overall power consumption. Moreover, it is proven that the proposed routing methods allow for minimizing the latency of the transmitted data, increasing the reliability, and improving the throughput of the received data compared to recent works, which are based on predefined trajectories of mobile sinks and multi-hop architectures.