Mahnoor Khan

SIMPLE: Stable Increased-Throughput Multi-hop Protocol for Link Efficiency in Wireless Body Area Networks

In this work, we propose a reliable, power efficient and high throughput routing protocol for Wireless Body Area Networks (WBANs). We use multi-hop topology to achieve minimum energy consumption and longer network lifetime. We propose a cost function to select parent node or forwarder. Proposed cost function selects a parent node which has high residual energy and minimum distance to sink. Residual energy parameter balances the energy consumption among the sensor nodes while distance parameter ensures successful packet delivery to sink. Simulation results show that our proposed protocol maximize the network stability period and nodes stay alive for longer period. Longer stability period contributes high packet delivery to sink which is major interest for continuous patient monitoring.

Demand Side Management in Smart Grid by Using Flower Pollination Algorithm and Genetic Algorithm

The introduction of Smart Grid (SG) in recent years provide the opportunity to the consumer to schedule their load in such an efficient manner that reduces the bill and also minimizes the Peak to Average Ratio. This paper focuses on scheduling the appliances in a more feasible and energy conservative way to satisfy both consumer and utility. In this paper, Flower Pollination Algorithm (FPA) is proposed to schedule the appliances in order to balance the time varying demand of consumer that is the basic aim of Demand Side Management (DSM). This paper emphasis on reducing the cost and Peak to Average Ratio (PAR) at same time. We used Real Time Pricing (RTP) tariff to calculate the consumer bill on the bases of real time energy consumption information. The results of proposed algorithm are compared with the results of Genetic Algorithm (GA), an existing technique to schedule the load consumption. The compared results show the significance of using this novel algorithm for DSM.

Short Term Load Forecasting based on Deep Learning for Smart Grid Applications

Short term load forecasting is indispensable for industrial, commercial, and residential smart grid (SG) applications. In this regard, a large variety of short term load forecasting models have been proposed in literature spaning from legacy time series models to contemporary data analytic models. Some of these models have either better performance in terms of accuracy while others perform well in convergence rate. In this paper, a fast and accurate short term load forecasting framework based on stacked factored conditional restricted boltzmann machine (FCRBM) and conditional restricted boltzmann machine (CRBM) is presented. The stacked FCRBM and CRBM are trained using rectified linear unit (RelU) and sigmoid functions, respectively. The proposed framework is applied to offline demand side load data of US utility. Load forecasts decide weather to increase or decrease the generation of an already running generator or to add extra units or exchange power with neighboring systems. Three performance metrics i.e., mean absolute percentage error (MAPE), normalized root mean square (NRMSE), and correlation coefficient are used to validate the proposed framework. The results show that stacked FCRBM and CRBM are accurate and robust as compared to artificial neural network (ANN) and convolutional neural network (CNN).

Load Prediction Based on Multivariate Time Series Forecasting for Energy Consumption and Behavioral Analytics

Liable, proficient and ecological cognizant electrical energy consumption activities are becoming a basic need for the consistent smart grid. This paper put forwards the concept of a data mining and intelligent proposed model to scrutinize including predict electrical energy time sequences to discover a number of time-based power consumption patterns. Support Vector Regression (SVR) have been productively employed to resolve non-linear regression and time sequences complications associated with prediction of residential electric energy consumption. Jaya algorithm is used in this paper as the implementation of SVR is greatly reliant on the collection of its constraints. The predicting model is technologically advanced by means of weighted SVR configurations (\(\nu \)-SVR and \(\epsilon \)-SVR). Besides, the Jaya algorithm is deployed to decide the weights resultant to every configuration. An instance of time sequential power consumption information from a residential edifice in Denmark is employed to explicate the execution of the presented configuration. Furthermore, the anticipated model is able to estimate power consumption for half hour and daily time successions data for the similar building. The consequences depict that the proposed model demonstrates developed weight for \(\nu \)-SVR for half hour data. Nevertheless, a sophisticated weight for \(\epsilon \)-SVR is perceived for diurnal data. The Mean Absolute Percentage Error (MAPE) for everyday power expenditure data is 5.521 while for half-hour power utilization is 3.769 correspondingly. Also, a thorough evaluation with different algorithms indicate that the presented configuration produces greater exactness for residential power exhaustion prediction.

Big Data Analytics for Price and Load Forecasting in Smart Grids

This paper focuses on analytics of an extremely large dataset of smart grid electricity price and load that is difficult to process with conventional computational models, referred as big data. The processing and analyzing of big data divulges the deeper insights that help experts in improvement of smart grid operations. Processing and extracting of the meaningful information from data is a challenging task. Electricity load and price are the most influential factors in the electricity market. For improving reliability, control and management of electricity market operations, an exact estimate of the day ahead load is a substantial requirement. Energy market trade is based on price. Accurate price forecast enables energy market participants to make effective and most profitable bidding strategies. This paper proposes a deep learning-based model for the forecast of price and demand on big data using deeper Long Short-Term Memory (LSTM). Due to adaptive and automatic feature learning of DNNs, processing of big data is easier with LSTM as compared to purely data driven methods. The proposed model is evaluated using a well-known real electricity markets’ data.

Inter-process communication, MPI and MPICH in microkernel environment: A comparative analysis

Inter-process communication (IPC) is one of the crucial aspects of every microkernel. The message-passing interface (MPI) is a specification between different processes, which is used for communication amongst processes. Message Passing Interface Chameleon (MPICH) is the portable implementation of message passing interface. This paper delineates the comparison between IPC, MPI and MPICH in terms of efficiency and computational cost of the processor. Different experimentations are performed to check the efficiency of each approach. Furthermore, the paper considers the latest research carried out since 2013 to deliberate the feasibility to swap IPC with MPICH in a microkernel environment.

A Novel Meta-heuristic Technique for Energy Optimization in Smart Grid

The increase in the energy consumption causes a serious crisis, especially during on-peak hours when the demand of energy consumption is high. Consequently, the peak to average ratio and electricity cost will be increased. This issue can be overcome by integrating Demand side management (DSM) with traditional Smart grid (SG), so that electricity utilization can be minimized during on-peak hours by efficiently distributing them into off-peak hours. In this paper, Crow search algorithm (CSA) is proposed to schedule the appliances for DSM and the performance of Home energy management system (HEMS) is assessed by two meta-heuristic techniques; Enhanced differential evolution and CSA. The reduction in cost and peak to average ratio along with increase in user comfort is mainly focused in this paper. Moreover, the electricity cost is based on real time pricing scheme. The main objective is to provide a comparative analysis of the aforementioned techniques for energy optimization using simulations in HEMS. The simulations results show that our proposed technique outperformed as compared to the existing meta-heuristic technique.

Demand Side Management Using Meta-Heuristic Optimization Techniques

In this paper, we present a Home Energy Management System (HEMS) using two meta-heuristic optimization techniques: Bacterial Foraging Optimization Algorithm (BFOA) and Bat Algorithm (BA). HEMS will provide different services to end user to manage and control their energy usage with time of use. The proposed model used for load scheduling between peak hour and off-peak hour. In this regard, we perform appliances scheduling to manage the frequent demand from the consumer. The aim of the proposed scheduling is to minimize peak to average ratio and the cost while having some trade-off in user comfort to achieve an optimal management of load. Simulation results show that the BA outperform than BFOA in selected performance parameters.

Residential Demand Side Management in Smart Grid Using Meta-Heuristic Techniques

The rise of energy demand is an alarming situation for mankind as it can lead towards a crisis. This problem can be easily tackled by assimilating Demand Side Management (DSM) with traditional grid by means of bi-directional communication between utility companies and consumers. This study evaluates the performance of Home Energy Management System (HEMS) using meta-heuristic optimization techniques: Genetic Algorithm (GA) and Crow Search Algorithm (CSA). The appliances are classified in three sets on the basis of their electrical energy consumption pattern. Moreover, the Real Time Pricing (RTP) scheme is used for power bill control. The core aims of this paper are to minimize electrical energy cost and consumption by scheduling of appliances, decline in peak to average ratio, while getting the best out of user comfort. Besides, simulation results illustrate that there is a trade-off between waiting time and electricity cost. The outcomes also indicate that CSA perform better as compared to GA in relation to cost.

Load Scheduling in Home Energy Management System Using Meta-Heuristic Techniques and Critical Peak Pricing Tariff

In this modern world, the demand of energy rises exponentially, that makes it a valuable resource. New techniques and methods are being developed to solve the problem of energy crisis in residential areas. The strategy to handle this problem is by integrating the demand side management (DSM) with smart grid (SG). DSM enables the consumer to schedule their load profile effectively in order to reduce electricity cost and power peak creation, referred as peak-to-average ratio (PAR). This paper evaluates the performance of home energy management system (HEMS) using meta-heuristic techniques; harmony search algorithm (HSA) and flower pollination algorithm (FPA). In this regard, a single home is considered with smart appliances classified as automatically operated appliances (AOAs) and manually operated appliances (MOAs). Moreover, critical peak pricing (CPP) is used as a price signal. In this paper, emphasis is placed on the cost minimization and load scheduling by shifting the load between off-peak and on-peak hours, while considering the user comfort. Simulation results shows that the performance of FPA is better in terms of cost and PAR reduction, whereas there exists a trade-offs between electricity cost and user comfort level.