Power grids are transforming into flexible, smart, and cooperative systems with greater dissemination of distributed energy resources, advanced metering infrastructure, and advanced communication technologies. Short-term electric load forecasting for individual residential customers plays a progressively crucial role in the operation and planning of future grids. Compared to the aggregated electrical load at the community level, the prediction of individual household electric loads is legitimately challenging because of the high uncertainty and volatility involved. Results from previous studies show that prediction using machine learning and deep learning models is far from accurate, and there is still room for improvement. We herein propose a deep learning framework based on a combination of a convolutional neural network (CNN) and long short-term memory (LSTM). The proposed hybrid CNN-LSTM model uses CNN layers for feature extraction from the input data with LSTM layers for sequence learning. The performance of our developed framework is comprehensively compared to state-of-the-art systems currently in use for short-term individual household electric load forecasting. The proposed model achieved significantly better results compared to other competing techniques. We evaluated our proposed model with the recently explored LSTM-based deep learning model on a publicly available electrical load data of individual household customers from the Smart Grid Smart City (SGSC) project. We obtained an average mean absolute percentage error (MAPE) of 40.38% for individual household electric load forecasts in comparison with the LSTM-based model that obtained an average MAPE of 44.06%. Furthermore, we evaluated the effectiveness of the proposed model on different time horizons (up to 3 h ahead). Compared to the recently developed LSTM-based model tested on the same dataset, we obtained 4.01%, 4.76%, and 5.98% improvement for one, two, and six look-forward time steps, respectively (with 2 lookback time steps). Additionally, we have performed clustering analysis based on the power consumption behavior of the energy users, which indicate that prediction accuracy could be improved by grouping and training the representative model using large amount of data. The results indicated that the proposed model outperforms the LSTM-based model for both 1 h ahead and 3 h ahead in forecasting individual household electric loads.

Agriculture is a major part of the world economy as it provides food safety. However, in recent years, it has been noted that plants are extensively infected by different diseases. This causes enormous economic losses in agriculture industry around the world. The manual inspection of fruit diseases is a difficult process which can be minimized by using automated methods for detection of plant diseases at the earlier stage. In this paper, a new method is implemented for apple diseases identification and recognition. Three pipeline procedures are followed by preprocessing, spot segmentation, and features extraction, and classification. In the first step, the apple leaf spots are enhanced by a hybrid method which is the conjunction of 3D box filtering, de-correlation, 3D-Gaussian filter, and 3D-median filter. After that, the lesion spots are segmented by the strong correlation-based method and optimized their results by fusion of expectation maximization (EM) segmentation. Finally, the color, color histogram, and local binary pattern (LBP) features are fused by comparison-based parallel fusion. The extracted features are optimized by genetic algorithm and classified by One-vs-All M-SVM. The experimental results are performed on plant village dataset. The proposed methodology is tested for four types of apple disease classes including healthy leaves, Blackrot, Rust, and Scab. The classification accuracy shows the improvement of our method on selected apple diseases. Moreover, the good preprocessing step always produced prominent features which later achieved significant classification accuracy.

In this paper, we propose a home energy management system that employs load shifting strategy of demand side management to optimize the energy consumption patterns of a smart home. It aims to manage the load demand in an efficient way to minimize electricity cost and peak to average ratio while maintaining user comfort through coordination among home appliances. In order to meet the load demand of electricity consumers, we schedule the load in day-ahead and real-time basis. We propose a fitness criterion for proposed hybrid technique, which helps in balancing the load during ON-peak and OFF-peak hours. Moreover, for realtime rescheduling, we present the concept of coordination among home appliances. This helps the scheduler to optimally decide the ON/OFF status of appliances in order to reduce the waiting time of appliance. For this purpose, we formulate our real-time rescheduling problem as knapsack problem and solve it through dynamic programming. This paper also evaluates the behavior of the proposed technique for three pricing schemes including: time of use, real-time pricing, and critical peak pricing. Simulation results illustrate the significance of the proposed optimization technique with 95% confidence interval.

The traditional power grid is inadequate to overcome modern day challenges. As the modern era demands the traditional power grid to be more reliable, resilient, and cost-effective, the concept of smart grid evolves and various methods have been developed to overcome these demands which make the smart grid superior over the traditional power grid. One of the essential components of the smart grid, home energy management system (HEMS) enhances the energy efficiency of electricity infrastructure in a residential area. In this aspect, we propose an efficient home energy management controller (EHEMC) based on genetic harmony search algorithm (GHSA) to reduce electricity expense, peak to average ratio (PAR), and maximize user comfort. We consider EHEMC for a single home and multiple homes with real-time electricity pricing (RTEP) and critical peak pricing (CPP) tariffs. In particular, for multiple homes, we classify modes of operation for the appliances according to their energy consumption with varying operation time slots. The constrained optimization problem is solved using heuristic algorithms: wind-driven optimization (WDO), harmony search algorithm (HSA), genetic algorithm (GA), and proposed algorithm GHSA. The proposed algorithm GHSA shows higher search efficiency and dynamic capability to attain optimal solutions as compared to existing algorithms. Simulation results also show that the proposed algorithm GHSA outperforms the existing algorithms in terms of reduction in electricity cost, PAR, and maximize user comfort.

The smart grid plays a vital role in decreasing electricity cost through Demand Side Management (DSM). Smart homes, a part of the smart grid, contribute greatly to minimizing electricity consumption cost via scheduling home appliances. However, user waiting time increases due to the scheduling of home appliances. This scheduling problem is the motivation to find an optimal solution that could minimize the electricity cost and Peak to Average Ratio (PAR) with minimum user waiting time. There are many studies on Home Energy Management (HEM) for cost minimization and peak load reduction. However, none of the systems gave sufficient attention to tackle multiple parameters (i.e., electricity cost and peak load reduction) at the same time as user waiting time was minimum for residential consumers with multiple homes. Hence, in this work, we propose an efficient HEM scheme using the well-known meta-heuristic Genetic Algorithm (GA), the recently developed Cuckoo Search Optimization Algorithm (CSOA) and the Crow Search Algorithm (CSA), which can be used for electricity cost and peak load alleviation with minimum user waiting time. The integration of a smart Electricity Storage System (ESS) is also taken into account for more efficient operation of the Home Energy Management System (HEMS). Furthermore, we took the real-time electricity consumption pattern for every residence, i.e., every home has its own living pattern. The proposed scheme is implemented in a smart building; comprised of thirty smart homes (apartments), Real-Time Pricing (RTP) and Critical Peak Pricing (CPP) signals are examined in terms of electricity cost estimation for both a single smart home and a smart building. In addition, feasible regions are presented for single and multiple smart homes, which show the relationship among the electricity cost, electricity consumption and user waiting time. Experimental results demonstrate the effectiveness of our proposed scheme for single and multiple smart homes in terms of electricity cost and PAR minimization. Moreover, there exists a tradeoff between electricity cost and user waiting.

Melanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in its early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for the highly equipped environment. The recent advancements in computerized solutions for this diagnosis are highly promising with improved accuracy and efficiency. In this article, a method for the identification and classification of the lesion based on probabilistic distribution and best features selection is proposed. The probabilistic distribution such as normal distribution and uniform distribution are implemented for segmentation of lesion in the dermoscopic images. Then multi-level features are extracted and parallel strategy is performed for fusion. A novel entropy-based method with the combination of Bhattacharyya distance and variance are calculated for the selection of best features. Only selected features are classified using multi-class support vector machine, which is selected as a base classifier. The proposed method is validated on three publicly available datasets such as PH2, ISIC (i.e. ISIC MSK-2 and ISIC UDA), and Combined (ISBI 2016 and ISBI 2017), including multi-resolution RGB images and achieved accuracy of 97.5%, 97.75%, and 93.2%, respectively. The base classifier performs significantly better on proposed features fusion and selection method as compared to other methods in terms of sensitivity, specificity, and accuracy. Furthermore, the presented method achieved satisfactory segmentation results on selected datasets.