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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.

Electricity load forecasting plays a vital role in improving the usage of energy through customers to make decisions efficiently. The accuracy of load prediction is a challenging task because of randomness and noise disturbance. An extreme deep learning model is applied in proposed system model to achieve better load prediction accuracy. The proposed model used to extract features by combining the mutual information (RF) and recursive feature elimination (RFE). Furthermore, extreme learning machine (ELM) and enhance CNN are used for load forecasting based on extracted features from MI and RFE. Additionally, to check the performance of our proposed scheme, we compared it with some benchmark schemes e.g. CNN, SVR and MLR. Simulation results reveal that our proposed approach outperformed in prediction performance.

Images can be a very good evidence during investigation of a crime scene. At the same time they can also contain very personal information which should not be exposed without the consent of the involved people. In this paper, We have presented here a practical approach to protect privacy of under investigation images with the use of Arnold’s Transform (AT) scrambling and Support Vector Machine, We also provide a new approach towards the whole forensics service provided by the designated agencies with the help of implementation of our approach. We enhanced the security of AT and provided privacy preserving mechanism to ensure protection of privacy. In literature only policies are defined to protect the privacy and lack of a solid approach which we have tried to resolve with a proof of concept implementation. In short, we have provided a full image forensics framework for illegal image detection while preserving the privacy.

In electricity market, electricity price has some complicated features like high volatility, non-linearity and non-stationarity that make very difficult to predict the accurate price. However, it is necessary for markets and companies to predict accurate electricity price. In this paper, we enhanced the forecasting accuracy by combined approaches of Kernel Extreme Learning Machine (KELM) and Autoregression Moving Average (ARMA) along with unique and enhanced features of both models. Wavelet transform is applied on prices series to decompose them, afterward test has performed on decomposed series for providing stationary series to AMRA-model and non-stationary series to KELM-model. At the end series are tuned with our combine approach of enhanced price prediction. The performance of our enhanced combined method is evaluated by electricity price dataset of New South Wales (NSW), Australian market. The simulation results show that combined method has more accurate prediction than individual methods.

Recently a massive increase in the demand of energy has been reported in residential, industrial and commercial sectors. Traditional Grid (TG) with the aging infrastructure is unable to address the increasing demand problem. Smart Grid (SG) enhanced the TG by adopting information and communication based technological solutions to address the increasing electricity demand. Smart Home Energy Management System (SHEMS) plays an important role in the efficacy of SG. In this paper, an Improved Algorithm for Peak to average ratio Reduction (IAPR) in SHEMS is developed. To validate the effectiveness of the IAPR, comparison is made with the renowned meta-heuristic optimization approaches namely Strawberry Algorithm (SA) and Salp Swarm Algorithm (SSA) using two different pricing scheme. It is illustrated by simulations results that the IAPR reduced the PAR to a greater degree as compare to SA and SSA.

As the energy demand for consumption is comparably higher than the generation of energy, which produce the shortage of energy. Many new schemes are being developed to fulfill the energy consumer demand. In this paper, we proposed our meta-heuristic algorithm Runner Updation Optimization Algorithm (RUOA) to schedule the consumption pattern of residential appliances. We compared the results of our scheme with other meta-heuristic algorithm Strawberry Algorithm (SBA) and Firefly Algorithm (FA). Critical Peak Price (CPP) and Real Time Price (RTP) are the two electricity pricing scheme that we used in this paper for calculation of electricity cost. The main objective of this paper is to minimize the electricity cost and Peak to Average Ratio (PAR). However, consumer comfort is not satisfied.