Medium-term electricity consumption and load forecasting in smart grids is an attractive topic of study, especially using innovative data analysis approaches for future energy consumption trends. Loss of electricity during generation and use is also a problem to be addressed. Both consumers and utilities can benefit from a predictive study of electricity demand and pricing. In this study, we used a new machine learning approach called AdaBoost to identify key features from an ISO-NE dataset that includes daily consumption data over eight years. Moreover, the DT classifier and RF are widely used to extract the best features from the dataset. Moreover, we predicted the electricity load and price using machine learning techniques including support vector machine (SVM) and deep learning techniques such as a convolutional neural network (CNN). Coronavirus herd immunity optimization (CHIO), a novel optimization approach, was used to modify the hyperparameters to increase efficiency, and it used classifiers to improve the performance of our classifier. By adding additional layers to the CNN and fine-tuning its parameters, the probability of overfitting the classifier was reduced. For method validation, we compared our proposed models with several benchmarks. MAE, MAPE, MSE, RMSE, the f1 score, recall, precision, and accuracy were the measures used for performance evaluation. Moreover, seven different forms of statistical analysis were given to show why our proposed approaches are preferable. The proposed CNN-CHIO and SVM techniques had the lowest MAPE error rates of 6% and 8%, respectively, and the highest accuracy rates of 95% and 92%, respectively.

Mental illness prediction through text involves employing natural language processing (NLP) techniques and deep learning algorithms to analyze textual data for the identification of mental disorders. Therefore, machine learning and deep learning algorithms have been utilized in the existing literature for the detection of mental illness. However, current systems exhibit suboptimal performance primarily due to their reliance on traditional embedding techniques and generic language models to generate text embeddings. To address this limitation, there is a requirement for domain-specific pretrained language models that comprehensively understand the context found in posts of mentally ill patients. Posts from individuals with mental illness often contain metaphorical expressions, posing a challenge for existing models in understanding such figurative language. In this study, we propose a hybrid transformer architecture, comprising MentalBERT and MelBERT pretrained language models, cascaded with CNN models to generate and concatenate deep features. MentalBERT is pretrained on an extensive corpus of text data specifically related to the mental health domain, while MelBERT is trained on a large corpus of metaphorical data for improved understanding of metaphorical expressions. The results reveal outstanding performance of the proposed architecture with an overall accuracy of 92% and an F1-score of 92%, surpassing state-of-the-art models in comparison. This study underscores the necessity for further research in this field and illustrates the potential of advanced technologies to address mental health issues in contemporary society.

Electricity theft harms smart grids and results in huge revenue losses for electric companies. Deep learning (DL), machine learning (ML), and statistical methods have been used in recent research studies to detect anomalies and illegal patterns in electricity consumption (EC) data collected by smart meters. In this paper, we propose a hybrid DL model for detecting theft activity in EC data. The model combines both a gated recurrent unit (GRU) and a convolutional neural network (CNN). The model distinguishes between legitimate and malicious EC patterns. GRU layers are used to extract temporal patterns, while the CNN is used to retrieve optimal abstract or latent patterns from EC data. Moreover, imbalance of data classes negatively affects the consistency of ML and DL. In this paper, an adaptive synthetic (ADASYN) method and TomekLinks are used to deal with the imbalance of data classes. In addition, the performance of the hybrid model is evaluated using a real-time EC dataset from the State Grid Corporation of China (SGCC). The proposed algorithm is computationally expensive, but on the other hand, it provides higher accuracy than the other algorithms used for comparison. With more and more computational resources available nowadays, researchers are focusing on algorithms that provide better efficiency in the face of widespread data. Various performance metrics such as F1-score, precision, recall, accuracy, and false positive rate are used to investigate the effectiveness of the hybrid DL model. The proposed model outperforms its counterparts with 0.985 Precision–Recall Area Under Curve (PR-AUC) and 0.987 Receiver Operating Characteristic Area Under Curve (ROC-AUC) for the data of EC.

The integration of the smart grid with the cloud computing environment promises to develop an improved energy-management system for utility and consumers. New applications and services are being developed which generate huge requests to be processed in the cloud. As smart grids can dynamically be operated according to consumer requests (data), so, they can be called Data-Driven Smart Grids. Fog computing as an extension of cloud computing helps to mitigate the load on cloud data centers. This paper presents a cloud–fog-based system model to reduce Response Time (RT) and Processing Time (PT). The load of requests from end devices is processed in fog data centers. The selection of potential data centers and efficient allocation of requests on Virtual Machines (VMs) optimize the RT and PT. A New Service Broker Policy (NSBP) is proposed for the selection of a potential data center. The load-balancing algorithm, a hybrid of Particle Swarm Optimization and Simulated Annealing (PSO-SA), is proposed for the efficient allocation of requests on VMs in the potential data center. In the proposed system model, Micro-Grids (MGs) are placed near the fogs for uninterrupted and cheap power supply to clusters of residential buildings. The simulation results show the supremacy of NSBP and PSO-SA over their counterparts.

The influence of Information Communication and Technology (ICT) in power systems necessitates Smart Grid (SG) with monitoring and real-time control of electricity consumption. In SG, huge requests are generated from the smart homes in residential sector. Thus, researchers have proposed cloud based centralized and fog based semi-centralized computing systems for such requests. The cloud, unlike the fog system, has virtually infinite computing resources; however, in the cloud, system delay is the challenge for real-time applications. The prominent features of fog are; awareness of location, low latency, wired and wireless connectivity. In this paper, the impact of longer delay of cloud in SG applications is addressed. We proposed a cloud-fog based system for efficient processing of requests coming from the smart homes, their quick response and ultimately reduced cost. Each smart home is provided with a 5G based Home Energy Management Controller (HEMC). Then, the 5G-HEMC communicates with the High Performance Fog (HPF). The HPFs are capable of processing energy consumers’ huge requests. Virtual Machines (VMs) are installed on physical systems (HPFs) to entertain the requests using First Come First Service (FCFS) and Ant Colony Optimization (ACO) algorithms along with Optimized Response Time Policy (ORTP) for the selection of potential HPF for efficient processing of the requests with maximum resource utilization. It is analysed that size and number of virtual resources affect the performance of the computing system. In the proposed system model, micro grids are introduced in the vicinity of energy consumers for uninterrupted and cost optimized power supply. The impact of the number of VMs on the performance of HPFs is analysed with extensive simulations with three scenarios.

The exponential growth of the edge-based Internet-of-Things (IoT) services and its ecosystems has recently led to a new type of communication network, the Low Power Wide Area Network (LPWAN). This standard enables low-power, long-range, and low-data-rate communications. Long Range Wide Area Network (LoRaWAN) is a recent standard of LPWAN that incorporates LoRa wireless into a networked infrastructure. Consequently, the consumption of smart End Devices (EDs) is a major challenge due to the highly dense network environment characterised by limited battery life, spectrum coverage, and data collisions. Intelligent and efficient service provisioning is an urgent need of a network to streamline the networks and solve these problems. This paper proposes a Dynamic Reinforcement Learning Resource Allocation (DRLRA) approach to allocate efficient resources such as channel, Spreading Factor (SF), and Transmit Power (Tp) to EDs that ultimately improve the performance in terms of consumption and reliability. The proposed model is extensively simulated and evaluated with the currently implemented algorithms such as Adaptive Data Rate (ADR) and Adaptive Priority-aware Resource Allocation (APRA) using standard and advanced evaluation metrics. The proposed work is properly cross validated to show completely unbiased results.