Electricity theft remains a significant challenge for modern power systems causing major financial damage to utility providers. With increasing adoption of advanced metering infrastructure in smart grids, energy efficiency and real-time monitoring have improved but the vulnerability of smart meters to tampering and cyber manipulation introduces new threats. To address these concerns at scale, this study proposes a cloud-based machine learning framework for intelligent electricity theft detection in residential sectors. The cloud-based setup, utilizing a Google Colab environment, facilitates centralized data processing, streamlined deployment, and real-time decision support. This study systematically resolves several critical limitations in existing approaches. Firstly, it addresses the severe class imbalance in real-world datasets, which significantly underrepresent theft cases compared to honest consumption. The synthetic minority oversampling technique is used to equalize representation and enable fair learning. Second, recognizing that synthetic oversampling can introduce noise and class overlap, a post-balancing noise reduction mechanism is integrated to refine the dataset and eliminate ambiguous samples. Third, by combining the interpretability of Decision Trees (DT) and the robustness of Random Forests (RF), an ensemble model named DTRF is proposed to enhance variance control and generalization. To improve the predictive performance of DTRF, the Firefly Algorithm (FA) is used to smartly adjust the model’s settings based on the specific features of the dataset. The proposed models are tested on two publicly available real-world datasets, Pakistan Residential Electricity Consumption (PRECON) and State Grid Corporation of China (SGCC), showing consistent improvements over standard models in various measures. On the PRECON dataset, the DTRF model outperforms baseline models and achieves a 25.68% reduction in log loss, a 31.23% decrease in Hamming loss, a 3.52% improvement in Cohen’s Kappa (CK), and a 4.10% increase in Jaccard score. The proposed DTRF-FA model further achieves a 40.97% reduction in log loss, a 36.38% decrease in Hamming loss, a 2.71% improvement in CK, and a 3.17% increase in the Jaccard score relative to the DTRF model. On the SGCC dataset, the DTRF model outperforms baselines with a 33.34% drop in Hamming loss, a 3.86% increase in CK, a 3.70% rise in the Jaccard score, and a 1.92% gain in the F1-score. The DTRF-FA model achieves a 75.00% reduction in Hamming loss, a 5.63% gain in CK, a 5.82% rise in Jaccard score, a 2.95% improvement in F1-score, and a 5.39% increase in Matthews correlation coefficient over the DTRF baseline. These improvements affirm the models’ ability to balance sensitivity and specificity while maintaining low false positive and false negative rates. Finally, to promote interpretability and transparency essential for stakeholder trust and regulatory compliance, the framework integrates two powerful explainable artificial intelligence techniques. Local Interpretable Model-agnostic Explanations (LIME) is used to generate local, instance-level explanations of individual predictions, while SHapley Additive exPlanations (SHAP) provides a global view of feature importance using cooperative game theory. By combining LIME and SHAP, the framework helps practitioners understand models’ decisions in detail and as a whole ensuring that it is accurate, clear, adaptable, and suitable for use in the smart grid settings.

A micro-grid (MG) is a localized organization of generation with a few loads, and they are becoming a more and more popular concept in the research community. With the passage of time, it is increasing the efficiency and reliability of the power systems. Both islanded mode and grid-connected mode (GCM) are used by MGs. MGs use the software application to replace their strength in order to meet the standards of GCM. However, line losses are considerable since MG and the macro station (MS) are located farther apart. Therefore, a hierarchical coordination model (HCM) is proposed for successfully replacing the power among MGs. Since some of the distances among the MGs are shorter than the distance between the MG and MS, HCM seeks to limit the power line losses through making hierarchical coalitions. In addition, a pricing scheme is proposed for encouraging the MGs to participate in the HCM. This pricing scheme is implemented along with the HCM, and both purchasing and selling costs of each MG are compared before and after coordination. Numerical simulations demonstrated that the recommended pricing scheme succeeds in lowering the cost of energy exchange and optimum energy distribution, thereby reducing the overall system cost. In addition, every MG has an energy storage system (ESS), and an analysis has been conducted to determine whether it affects line losses as well as the costs associated with buying and selling energy for each MG. The proposed HCM and the conventional non-coordination model (NCM) are compared. Effects are assessed, and a comparison indicates how successful the suggested HCM is. Results suggest that, in comparison to NCM, HCM has decreased electrical line losses. The results demonstrate that, in comparison to NCM, the proposed HCM model is a more economical means of facilitating energy exchange between microgrids; consequently, the model would significantly improve system efficiency. Power line losses were reduced by 69.7% because of the ESS integration, compared to conventional NCM. As a result of the enhanced energy exchange between MGs, HCM caused the energy purchasing costs to drop by 17.7%. Additionally, the pricing mechanism has been performing well and has improved the power exchange between MGs, which has helped to lower the load on the MG.

In the proposed work, blockchain is implemented on the Base Stations (BSs) and Cluster Heads (CHs) to register the nodes using their credentials and also to tackle various security issues. Moreover, a Machine Learning (ML) classifier, termed as Histogram Gradient Boost (HGB), is employed on the BSs to classify the nodes as malicious or legitimate. In case, the node is found to be malicious, its registration is revoked from the network. Whereas, if a node is found to be legitimate, then its data is stored in an Interplanetary File System (IPFS). IPFS stores the data in the form of chunks and generates hash for the data, which is then stored in blockchain. In addition, Verifiable Byzantine Fault Tolerance (VBFT) is used instead of Proof of Work (PoW) to perform consensus and validate transactions. Also, extensive simulations are performed using the Wireless Sensor Network (WSN) dataset, referred as WSN-DS. The proposed model is evaluated both on the original dataset and the balanced dataset. Furthermore, HGB is compared with other existing classifiers, Adaptive Boost (AdaBoost), Gradient Boost (GB), Linear Discriminant Analysis (LDA), Extreme Gradient Boost (XGB) and ridge, using different performance metrics like accuracy, precision, recall, micro-F1 score and macro-F1 score. The performance evaluation of HGB shows that it outperforms GB, AdaBoost, LDA, XGB and Ridge by 2-4%, 8-10%, 12-14%, 3-5% and 14-16%, respectively. Moreover, the results with balanced dataset are better than those with original dataset. Also, VBFT performs 20-30% better than PoW. Overall, the proposed model performs efficiently in terms of malicious node detection and secure data storage.

The current study uses a data-driven method for Nontechnical Loss (NTL) detection using smart meter data. Data augmentation is performed using six distinct theft attacks on benign users’ samples to balance the data from honest and theft samples. The theft attacks help to generate synthetic patterns that mimic real-world electricity theft patterns. Moreover, we propose a hybrid model including the Multi-Layer Perceptron and Gated Recurrent Unit (MLP-GRU) networks for detecting electricity theft. In the model, the MLP network examines the auxiliary data to analyze nonmalicious factors in daily consumption data, whereas the GRU network uses smart meter data acquired from the Pakistan Residential Electricity Consumption (PRECON) dataset as the input. Additionally, a random search algorithm is used for tuning the hyperparameters of the proposed deep learning model. In the simulations, the proposed model is compared with the MLP-Long Term Short Memory (LSTM) scheme and other traditional schemes. The results show that the proposed model has scores of 0.93 and 0.96 for the area under the precision–recall curve and the area under the receiver operating characteristic curve, respectively. The precision–recall curve and the area under the receiver operating characteristic curve scores for the MLP-LSTM are 0.93 and 0.89, respectively.

The enhancement of Robustness (R) has gained significant importance in Scale-Free Networks (SFNs) over the past few years. SFNs are resilient to Random Attacks (RAs). However, these networks are prone to Malicious Attacks (MAs). This study aims to construct a robust network against MAs. An Intelligent Rewiring (INTR) mechanism is proposed to optimize the network R against MAs. In this mechanism, edge rewiring is performed between the high and low degree nodes to make a robust network. The Closeness Centrality (CC) measure is utilized to determine the central nodes in the network. Based on the measure, MAs are performed on nodes to damage the network. Therefore, the connections of the neighboring nodes in the network are greatly affected by removing the central nodes. To analyze the network connectivity against the removal of nodes, the performance of CC is found to be more efficient in terms of computational time as compared to Betweenness Centrality (BC) and Eigenvector Centrality (EC). In addition, the Recalculated High Degree based Link Attacks (RHDLA) and the High Degree based Link Attacks (HDLA) are performed to affect the network connectivity. Using the local information of SFN, these attacks damage the vital portion of the network. The INTR outperforms Simulated Annealing (SA) and ROSE in terms of R by 17.8% and 10.7%, respectively. During the rewiring mechanism, the distribution of nodes' degrees remains constant.

Electricity theft is considered one of the most significant reasons of the non technical losses (NTL). It negatively influences the utilities in terms of the power supply quality, grid's safety, and economic loss. Therefore, it is necessary to effectively deal with the electricity theft problem. For detecting electricity theft in smart grids (SGs), an efficient and state-of-the-art approach is designed in the underlying work based on autoencoder and bidirectional gated recurrent unit (AE-BiGRU). The proposed approach consists of six components: (1) data collection, (2) data preparation, (3) data balancing, (4) feature extraction, (5) classification and (6) performance evaluation. Moreover, bidirectional gated recurrent unit (BiGRU) is used for the identification of the anomalies in electricity consumption (EC) patterns caused due to factors like family formation changes, holidays, parties, and so on, which are referred as non-theft factors. The proposed autoencoder-bidirectional gated recurrent unit (AE-BiGRU) model employs the EC data acquired from state grid corporation of China (SGCC) for simulations. Furthermore, it is visualized from the simulation results that 90.1% accuracy and 10.2% false positive rate (FPR) are obtained by the proposed model. The results are better than different existing classifiers, i.e., logistic regression (LR), decision tree (DT), extreme gradient boosting (XGBoost), gated recurrent unit (GRU), etc.

In this paper a secure energy system is proposed that consists of private and public blockchains for vehicles in sustainable cities and society. The former protects vehicle owners from spatial and temporal information based attacks while the latter provides efficient energy trading in sustainable cities and society. In the proposed system, the dynamic demand based pricing policy for the vehicle owners is proposed using types of vehicles, time of demand and geographical locations. The vehicles' social welfare and utility are maximized using an optimal scheduling method along with the proposed pricing policy. Also, the vehicle owners' privacy is protected by applying differential privacy in the proposed consensus energy management algorithm. The numerical analyses show that 89.23% reduction in energy price is achieved as compared to 83.46%, 73.86% and 53.07% for multi-parameter pricing scheme (MPPS), fixed pricing scheme and time-of-use pricing scheme (ToU), respectively. Applying the proposed scheme, the owners can achieve about 81.46% reduction in their operating cost as compared to 80.48%, 69.75% and 68.29% for MPPS, fixed pricing scheme and ToU, respectively. Moreover, the proposed system is 60.32% secure as compared to 39.67% for MPPS system. Furthermore, using less information loss against considerable background knowledge of an attacker, higher privacy protection of vehicles is attained.

Electricity theft has emerged as one of the major reasons of Non-Technical Losses (NTLs) in the power distribution systems and has become a global issue.Therefore, power utilities are concerned about resolving the issue of electricity theft.In this regard, the data collected by Advanced Metering Infrastructure (AMI) can be used to devise data-driven machine learning-based Electricity Theft Detection (ETD) methods.In this paper, a novel data-driven ETD method is proposed that firstly labels the electricity consumers as fair or malicious based on three analyses: intra-consumer, inter-consumer, and temperature-electricity consumption relation.After assigning labels to the data, significant features are extraction from data by introducing a new feature extractor that is based on Reconstruction Independent Component Analysis (RICA) and sparse auto-encoder.Finally, classification is performed using two newly proposed enhanced classifiers, named as Differential Evolution (DE) Random Undersampling Boosting (DE-RUSBoost) and Jaya-RUSBoost.The performance of RUSBoost is enhanced using two nature-inspired swarm intelligence-based optimization algorithms, namely DE and Jaya optimization.The performance evaluation of the proposed classifiers is performed by conducting comprehensive simulations on real-world data taken from the UMass * smart homes electricity consumption dataset and the State Grid Corporation of China (SGCC) electricity theft dataset.DE-RUSBoost achieves an Area Under the Curve (AUC) of 0.89 and Jaya-RUSBoost achieves AUC of 0.95.The proposed classifiers have superior performance compared to two state-of-the-art benchmarks, i.e., Wide And Deep Convolution Neural Network (WADCNN) and grid search-based RUSBoost.

The electrical losses in power systems are divided into non-technical losses (NTLs) and technical losses (TLs). NTL is more harmful than TL because it includes electricity theft, faulty meters and billing errors. It is one of the major concerns in the power system worldwide and incurs a huge revenue loss for utility companies. Electricity theft detection (ETD) is the mechanism used by industry and academia to detect electricity theft. However, due to imbalanced data, overfitting issues and the handling of high-dimensional data, the ETD cannot be applied efficiently. Therefore, this paper proposes a solution to address the above limitations. A long short-term memory (LSTM) technique is applied to detect abnormal patterns in electricity consumption data along with the bat-based random under-sampling boosting (RUSBoost) technique for parameter optimization. Our proposed system model uses the normalization and interpolation methods to pre-process the electricity data. Afterwards, the pre-processed data are fed into the LSTM module for feature extraction. Finally, the selected features are passed to the RUSBoost module for classification. The simulation results show that the proposed solution resolves the issues of data imbalancing, overfitting and the handling of massive time series data. Additionally, the proposed method outperforms the state-of-the-art techniques; i.e., support vector machine (SVM), convolutional neural network (CNN) and logistic regression (LR). Moreover, the F1-score, precision, recall and receiver operating characteristics (ROC) curve metrics are used for the comparative analysis.

The energy demand is increasing day by day due to the huge amount of residential appliance's energy consumption, which creates more shortage of electricity. Industrial and commercial areas are also consuming large amount of energy, but residential energy demand is more flexible as compared to other two. Nowadays, many of the techniques are presented for scheduling of Smart Appliances to reduce the peak to average ratio (PAR) and consumer delay time. However, they didn't consider the total electricity cost and consumer waiting time. In this paper, we reduce the cost through load shifting techniques. In order to consider above objective, we employed some feature of the Jaya algorithm (JA) on a bat algorithm (BA) to develop a candidate solution updation algorithm (CSUA). Simulation was conducted to compare the result of existing BA and Jaya for single smart home with 15 smart appliances. We used time of use (ToU) and critical peak price (CPP). The result depicts that successful achievement of load shifting from higher price time slot to lower price time slot, which basically bring out the reduction in electricity bills.

Nowadays, the Internet of Things enabled Underwater Wireless Sensor Network (IoT-UWSN) is suffering from serious performance restrictions, i.e., high End to End (E2E) delay, low energy efficiency, low data reliability, etc. The necessity of efficient, reliable, collision and interference-free communication has become a challenging task for the researchers. However, the minimum Energy Consumption (EC) and low E2E delay increase the performance of the IoT-UWSN. Therefore, in the current work, two proactive routing protocols are presented, namely: Bellman–Ford Shortest Path-based Routing (BF-SPR-Three) and Energy-efficient Path-based Void hole and Interference-free Routing (EP-VIR-Three). Then we formalized the aforementioned problems to accomplish the reliable data transmission in Underwater Wireless Sensor Network (UWSN). The main objectives of this paper include minimum EC, interference-free transmission, void hole avoidance and high Packet Delivery Ratio (PDR). Furthermore, the algorithms for the proposed routing protocols are presented. Feasible regions using linear programming are also computed for optimal EC and to enhance the network lifespan. Comparative analysis is also performed with state-of-the-art proactive routing protocols. In the end, extensive simulations have been performed to authenticate the performance of the proposed routing protocols. Results and discussion disclose that the proposed routing protocols outperformed the counterparts significantly.

Electricity is the basic demand of consumers. With the passage of time this demand is increasing day by day. Smart grid (SG) trying to fulfill the demand of customers. When demand increases then load is also high. To maintain load from on peak hours to off peak hours, consumer needs to manage their appliances by home energy management system (HEMS). HEMS schedule the appliances according to customer’s needs. In this paper, scheme is proposed which is used to minimize the electricity cost and also maximize the user comfort. The proposed scheme is performed better than existing meta heuristic techniques. The proposed scheme is used real time price (RTP) price signal. Simulation results shows that the algorithm has met the objective of DSM. Moreover, the proposed algorithm outperforms earth worm algorithm (EWA) and single swam optimization (SSO) in terms of electricity cost and user comfort.

With the advent of Smart Grid (SG), it provides the consumers with the opportunity to schedule their power consumption load efficiently in such a way that it reduces their energy cost while also minimizing their Peak to Average Ratio (PAR) in the process. We in this paper target the appliances to schedule in such a way that it increases User Comfort (UC) and decreases electricity consumption load which benefits both consumer and utility. In this paper, we proposed hybrid of Bacterial Forging Algorithm (BFA) and Tabu Search (TS) Algorithm using different Operational time Interval (OTI) to schedule appliances while balancing User Comfort which is the main objective of the Demand Side Management (DSM). This paper tries to reduce both waiting time and electricity cost simultaneously in the new hybrid Bacterial Foraging Tabu Search (BFTS) technique. Real time pricing (RTP) scheme was used to get the total cost of electricity consumed. We compared the results of proposed hybrid scheme with Bacterial Forging (BFA) and Tabu Search (TS) Algorithm using different Operational time Interval (OTI). The result shows effectiveness of using hybrid Bacterial Foraging Tabu Search (BFTS) technique for Demand Side Management (DSM).

Network Simulator (NS) is a discrete event simulator targeted at networking research that provides substantial support for simulation of various networks. Performance evaluation in effective manner is the main concern of this paper. This paper, presents a mathematical model to work with the pre-simulation TCL file and post-simulation trace file evaluation for the 802.15.4 networks. The impact of BO and SO on performance of 802.15.4 with varying duty cycle is analyzed considering various parameters like packet delivery ratio, average end-to-end delay and energy consumption in different state: receiving, transmitting and idle mode.

Smart grid (SG) is one of the most advanced technologies, which plays a key role in maintaining balance between demand and supply by implementing demand response (DR). In SG the main focus of the researchers is on home energy management (HEM) system, that is also called demand side management (DSM). DSM includes all responses, which adjust the consumer's electricity consumption pattern, and make it match with the supply. If the main grid cannot provide the users with sufficient energy, then the smart scheduler (SS) integrates renewable energy source (RES) with the HEM system. This alters the peak formation as well as minimizes the cost. Residential users basically effect the overall performance of traditional grid due to maximum requirement of their energy demand. HEM benefits the end users by monitoring, managing and controlling their energy consumption. Appliance scheduling is integral part of HEM system as it manages energy demand according to supply, by automatically controlling the appliances or shifting the load from peak to off peak hours. Recently different techniques based on artificial intelligence (AI) are being used to meet aforementioned objectives. In this paper, three different types of heuristic algorithms are evaluated on the basis of their performance against cost saving, user comfort and peak to average ratio (PAR) reduction. Two techniques are already existing heuristic techniques i.e. harmony search (HS) algorithm and enhanced differential evolution (EDE) algorithm. On the basis of aforementioned two algorithms a hybrid approach is developed i.e. harmony search differential evolution (HSDE). We have done our problem formulation through multiple knapsack problem (MKP), that the maximum consumption of electricity of consumer must be in the range which is bearable for utility and also for consumer in sense of electricity bill. Finally simulation of the proposed techniques will be conducted in MATLAB to validate the performance of proposed scheduling algorithms in terms of minimum cost, reduced peak to average ratio (PAR), waiting time and equally distributed energy consumption pattern in each hour of a day to benefit both utility and end users.

In this paper, our goal is to minimize the electricity cost, electricity consumption at minimum user discomfort while considering the peak electricity consumption. Electricity consumption may not be the same in residential, commercial and industrial areas. It may vary from each and every area. It is a challenging task to maintain the balance between the conflicting objectives: electricity consumption and user comfort. To meet the rising electricity demand in residential area, scheduleable devices can be equally distributed to the available time slots on the basis of average power consumption. The main objective is to minimize the electricity usage during the electricity peak hours by distributing the electricity load during the off-peak hours. In this regard, Genetic Algorithm (GA), Pigeon Inspired Optimization (PIO) and our proposed hybridization of GA and PIO (HGP) in Demand Side Management(DSM) are applied for residential load management to optimize the fitness function. GA, PIO and HGP are evaluated on the basis of real time pricing scheme (RTP) for single home with three different operational time interval (OTI) and for multiple homes with a single OTI. Simulations results shows that GA, PIO and HGP are able to minimize electricity bill and electricity consumption while minimizing the user discomfort. The performance of HGP is better than GA, PIO with respect to PAR, electricity load and electricity cost for both single home and multiple homes scenario. The feasible region between electricity cost and electricity consumption is also represented. Moreover, the desired trade-off between electricity cost and user comfort is also achieved in both techniques.

The distinctive features of acoustic communication channel-like high propagation delay, multi-path fading, quick attenuation of acoustic signal, etc. limit the utilization of underwater wireless sensor networks (UWSNs). The immutable selection of forwarder node leads to dramatic death of node resulting in imbalanced energy depletion and void hole creation. To reduce the probability of void occurrence and imbalance energy dissipation, in this paper, we propose mobility assisted geo-opportunistic routing paradigm based on interference avoidance for UWSNs. The network volume is divided into logical small cubes to reduce the interference and to make more informed routing decisions for efficient energy consumption. Additionally, an optimal number of forwarder nodes is elected from each cube based on its proximity with respect to the destination to avoid void occurrence. Moreover, the data packets are recovered from void regions with the help of mobile sinks which also reduce the data traffic on intermediate nodes. Extensive simulations are performed to verify that our proposed work maximizes the network lifetime and packet delivery ratio.

Smart grid is an emerging technology which is considered as an ultimate solution to meet the increasing power demand challenges. Modern communication technologies has enabled the successful implementation of smart grid, which aims at provision of demand side management mechanisms, such as demand response. In this paper, we propose residential load scheduling model for demand side management. It is assumed that electric prices are announced on day-ahead basis. The major focus of this work is to minimize consumer electricity bill at minimum user discomfort. Load scheduling is formulated as an optimization problem, and an optimal schedule is achieved by solving the minimization problem. Simulation results validate that teacher learning based optimization performs better as compared to genetic algorithm, showing comparable results with linear programming with less computational efforts. TLBO is able to obtain the desired trade-off between consumer electric bill and user discomfort.

Increasing demand of power and emergence of smart grid has gain maximum attention of researchers which has further opened new opportunities for Home Energy Management System (HEMS). HEMS under Demand Response (DR) helps to reduce the On-peak hour load by shifting the load toward the Off-peak hours. This load shifting strategy effects the user comfort, however in return DR gives them incentives in term of electricity bill reduction. Consumer electricity cost and peak load have a tradeoff, to sort out this situation an efficient system is required. In this paper, we present a multi-objective HEMS to schedule home appliances using Cuckoo Search Algorithm (CSA) while considering the objective load fitness criteria. This proposed load fitness criteria effectively reduces the cost and peak load. Simulations are performed to verify the generic behavior i.e., system performance on any price tariffs. For this purpose, results are validated for three price signals: day-ahead Real Time Peak Price (RTP), Time of Use (TOU) and Critical Peak Price (CPP).

Wireless body area networks are captivating growing interest because of their suitability for wide range of applications. However, network lifetime is one of the most prominent barriers in deploying these networks for most applications. Moreover, most of these applications have stringent QoS requirements such as delay and throughput. In this paper, the modified superframe structure of IEEE 802.15.4 based MAC protocol is proposed which addresses the aforementioned problems and improves the energy consumption efficiency. Moreover, priority guaranteed CSMA/CA mechanism is used where different priorities are assigned to body nodes by adjusting the data type and size. In order to save energy, a wake-up radio based mechanism to control sleep and active modes of body sensors are used. Furthermore, a discrete time finite state Markov model to find the node states is used. Analytical expressions are derived to model and analyze the behavior of average energy consumption, throughput, packet drop probability, and average delay during normal and emergency data. Extensive simulations are conducted for analysis and validation of the proposed mechanism. Results show that the average energy consumption and delay are relatively higher during emergency data transmission with acknowledgment mode due to data collision and retransmission.