Big data analytics based short term electricity load forecasting model for residential buildings in smart grids

Electricity load forecasting has always been a significant part of the smart grid. It ensures sustainability and helps utilities to take cost-efficient measures for power system planning and operation. Conventional methods for load forecasting cannot handle huge data that has a nonlinear relationship with load power. Hence an integrated approach is needed that adopts a coordinating procedure between different modules of electricity load forecasting. We develop a novel electricity load forecasting architecture that integrates three modules, namely data selection, extraction, and classification into a single model. First, essential features are selected with the help of random forest and recursive feature elimination methods. This helps reduce feature redundancy and hence computational overhead for the next two modules. Second, dimensionality reduction is realized with the help of a t-stochastic neighbourhood embedding algorithm for the best feature extraction. Finally, the electricity load is forecasted with the help of a deep neural network (DNN). To improve the learning trend and computational efficiency, we employ a grid search algorithm for tuning the critical parameters of the DNN. Simulation results confirm that the proposed model achieves higher accuracy when compared to the standard DNN

A comparative study on different physicochemical properties of fresh and frozen lamb meat

This study described the comparison of different physicochemical parameters between fresh and frozen lamb meat. The pH measurement for fresh and frozen lamb meat did not show a significant difference. CIE L*a*b* (Commission Internationale de l'éclairage) color measurement technique was used and ∆E (distance between 2 colors) was found 5.32. On shrinkage measurement, there were significant differences (p<0.05) between the fresh and frozen meat. Frozen lamb sample showed 26.99% shrinkage compared to the fresh lamb which showed 18.09% shrinkage. The thawing loss did not show any significant difference. For texture analysis force and work were evaluated together for both fresh and frozen samples through Warner Bratzler texture analysis. The values did not show any significant difference. The absolute values of force and work were significantly different (p<0.05). Water binding capacity of the frozen and fresh sample were 56.57% and 59.27%, respectively. The moisture contents of fresh and frozen sample were 73.64% and 72.85%, respectively. Fat contents of fresh and frozen sample were 5.08% and 6.09% respectively. The study concludes that while comparing fresh and frozen lamb, only shrinkage and texture analysis showed significant difference whereas other physicochemical properties showed minor differences

Data Driven Analysis for Electricity Theft Attack-Resilient Power Grid

The role of electricity theft detection (ETD) is critical to maintain cost-efficiency in smart grids. However, existing ETD methods cannot efficiently handle the sheer volume of data now available, being limited by issues such as missing values, high variance and non-linearity. An integrated infrastructure is also required for synchronizing diverse procedures in electricity theft classification. To help address such problems, a novel ETD framework is proposed that combines three distinct modules. The first module handles missing values, outliers, and unstandardized electricity consumption data. The second module employs a newly proposed hybrid class balancing approach to deal with highly imbalanced datasets. The third module utilises an improved artificial neural network (iANN) based classification engine, to predict electricity theft cases accurately and efficiently. We propose three distinctive mechanisms, including hyper-parameters tuning, regularization and skip connections, to improve the performance of standard ANN to handle more complex classification tasks using smart meter (SM) data. Furthermore, various structures of iANN are investigated to improve the generalization and function fitting capabilities of the final classification. Numerical results from real-world energy usage datasets confirm that the proposed ETD model has superior performance compared to existing machine learning and deep learning methods, and can effectively be applied to industrial applications

Maleated high oleic sunflower oil-treated cellulose fiber-based styrene butadiene rubber composites

Surface treatment of cellulose fibers was performed with maleated high oleic sunflower oil (MSOHO). The MSOHO-treated cellulose fibers and unmodified cellulose fibers were dispersed in styrene butadiene rubber (SBR) using a two roll mill. Vapor grown carbon nanofibers (VGCNF) were also incorporated at only one parts per hundred rubber (phr) in unmodified cellulose fibers/SBR composites. The curing characteristics, mechanical properties, and water absorption of the resulting composites were determined. MSOHO-treated fibers completed curing at much slower rate and also decreased the cure density of composites, compared to unmodified fibers. In contrast, the combination of VGCNF and unmodified cellulose fibers accelerated the SBR curing process, but reduced the cure density. MSOHO treatment improved the dispersion of the fibers in the SBR, which resulted in improved mechanical properties of composites. The composite incorporating 1 phr VGCNF and 15 phr unmodified cellulose fibers showed the greatest increase in tensile strength as compared with neat SBR

A Stacked Machine and Deep Learning-based Approach for Analysing Electricity Theft in Smart Grids

The role of electricity theft detection (ETD) is critical to maintain cost-efficiency in smart grids. However, existing methods for theft detection can struggle to handle large electricity consumption datasets because of missing values, data variance and nonlinear data relationship problems, and there is a lack of integrated infrastructure for coordinating electricity load data analysis procedures. To help address these problems, a simple yet effective ETD model is developed. Three modules are combined into the proposed model. The first module deploys a combination of data imputation, outlier handling, normalization and class balancing algorithms, to enhance the time series characteristics and generate better quality data for improved training and learning by the classifiers. Three different machine learning (ML) methods, which are uncorrelated and skillful on the problem in different ways, are employed as the base learning model. Finally, a recently developed deep learning approach, namely a temporal convolutional network (TCN), is used to ensemble the outputs of the ML algorithms for improved classification accuracy. Experimental results confirm that the proposed framework yields a highly-accurate, robust classification performance, in comparison to other well-established machine and deep learning models and thus can be a practical tool for electricity theft detection in industrial applications

Heuristic algorithm based dynamic scheduling model of home appliances in smart grid

Smart grid provides an opportunity for customers as well as for utility companies to reduce electricity costs and regulate generation capacity. The success of scheduling algorithms mainly depends upon accurate information exchange between main grids and smart meters. On the other hand, customers are required to schedule loads, respond to energy demand signals, participate in energy bidding and actively monitor energy prices generated by the utility company. Strengthening communication infrastructure between the utility company and consumers can serve the purpose of consumer satisfaction. We propose a heuristic demand side management model for scheduling smart home appliances in an automated manner, to maximise the satisfaction of the consumers associated with it. Simulation results confirm that the proposed hybrid approach has the ability to reduce the peak-to-average ratio of the total energy demand and reduce the total cost of the energy without compromising user comfort

Big Data Analytics for Electricity Theft Detection in Smart Grids

In Smart Grids (SG), Electricity Theft Detection (ETD) is of great importance because it makes the SG cost-efficient. Existing methods for ETD cannot efficiently handle data imbalance, missing values, variance and non-linear data problems in the smart meter data. Therefore, an effective integrated strategy is required to address underlying issues and accurately detect electricity theft using big data. In this work, a simple yet effective approach is proposed by integrating two different modules, such as data pre-processing and classification, in a single framework. The first module involves data imputation, outliers handling, standardization and class balancing steps to generate quality data for classifier training. The second module classifies honest and dishonest users with a Support Vector Machine (SVM) classifier. To improve the classifier’s learning trend and accuracy, a Bayesian optimization algorithm is used to tune SVM’s hyperparameters. Simulation results confirm that the proposed framework for ETD significantly outperforms previous machine learning approaches such as random forest, logistic regression and SVM in terms of accuracy

Optimal power flow solution with uncertain RES using augmented grey wolf optimization

This work focuses on implementing the optimal power flow (OPF) problem, considering wind, solar and hydropower generation in the system. The stochastic nature of renewable energy sources (RES) is modelled using Weibull, Lognormal and Gumbel probability density functions. The system-wide economic aspect is examined with additional cost functions such as penalty and reserve costs for under and overestimating the imbalance of RES power outputs. Also, a carbon tax is imposed on carbon emissions as a separate objective function to enhance the contribution of green energy. For solving the optimization problem, a simple and efficient augmentation to the basic grey wolf optimization (GWO) algorithm is proposed, in order to enhance the algorithm's exploration capabilities. The performance of the new augmented GWO (AGWO) approach, in terms of robustness and scalability, is confirmed on IEEE-30, 57 and 118 bus systems. The obtained results of the AGWO algorithm are compared with modern heuristic techniques for a case of OPF incorporating RES. Numerical simulations indicate that the proposed method has better exploration and exploitation capabilities to reduce operational costs and carbon emissions

Reduced genomic tumor heterogeneity after neoadjuvat chemotherapy is related to favorable outcome in patients with esophageal adenocarcinoma

Neoadjuvant chemo(radio)therapy followed by surgery is the standard of care for patients with locally advanced resectable esophageal adenocarcinoma (EAC). There is increasing evidence that drug resistance might be related to genomic heterogeneity. We investigated whether genomic tumor heterogeneity is different after cytotoxic chemotherapy and is associated with EAC patient survival. We used arrayCGH and a quantitative assessment of the whole genome DNA copy number aberration patterns (‘DNA copy number entropy’) to establish the level of genomic tumor heterogeneity in 80 EAC treated with neoadjuvant chemotherapy followed by surgery (CS group) or surgery alone (S group). The association between DNA copy number entropy, clinicopathological variables and survival was investigated. DNA copy number entropy was reduced after chemotherapy, even if there was no morphological evidence of response to therapy (p<0.001). Low DNA copy number entropy was associated with improved survival in the CS group (p=0.011) but not in the S group (p=0.396). Our results suggest that cytotoxic chemotherapy reduces DNA copy number entropy, which might be a more sensitive tumor response marker than changes in the morphological tumor phenotype. The use of DNA copy number entropy in clinical practice will require validation of our results in a prospective study