Atrous Convolutions and Residual GRU Based Architecture for Matching Power Demand with Supply.

Nowadays, for efficient energy management, local demand-supply matching in power grid is emerging research domain. However, energy demand is increasing day by day in many countries due to rapid growth of the population and most of their work being reliant on electronic devices. This problem has highlighted the significance of effectively matching power demand with supply for optimal energy management. To resolve this issue, we present an intelligent deep learning framework that integrates Atrous Convolutional Layers (ACL) with Residual Gated Recurrent Units (RGRU) to establish balance between the demand and supply. Moreover, it accurately predicts short-term energy and delivers a systematic method of communication between consumers and energy distributors as well. To cope with the varying nature of electricity data, first data acquisition step is performed where data are collected from various sources such as smart meters and solar plants. In the second step a pre-processing method is applied on raw data to normalize and clean the data. Next, the refined data are passed to ACL for spatial feature extraction. Finally, a sequential learning model RGRU is used that learns from complicated patterns for the final output. The proposed model obtains the smallest values of Mean Square Error (MSE) including 0.1753, 0.0001, 0.0177 over IHEPC, KCB, and Solar datasets, respectively, which manifests better performance as compared to existing approaches.

Towards Efficient Building Designing: Heating and Cooling Load Prediction via Multi-Output Model.

In the current technological era, energy-efficient buildings have a significant research body due to increasing concerns about energy consumption and its environmental impact. Designing an appropriate energy-efficient building depends on its layout, such as relative compactness, overall area, height, orientation, and distribution of the glazing area. These factors directly influence the cooling load (CL) and heating load (HL) of residential buildings. An accurate prediction of these load facilitates a better management of energy consumption and enhances the living standards of inhabitants. Most of the traditional machine learning (ML)-based approaches are designed for single-output (SO) prediction, which is a tedious task due to separate training processes for each output with low performance. In addition, these approaches have a high level of nonlinearity between input and output, which need more enhancement in terms of robustness, predictability, and generalization. To tackle these issues, we propose a novel framework based on gated recurrent unit (GRU) that reliably predicts the CL and HL concurrently. To the best of our knowledge, we are the first to propose a multi-output (MO) sequential learning model followed by utility preprocessing under the umbrella of a unified framework. A comprehensive set of ablation studies on ML and deep learning (DL) techniques is done over an energy efficiency dataset, where the proposed model reveals an incredible performance as compared to other existing models.

A comparative analysis of parallel computing approaches for genome assembly.

Over the last two decades, we have witnessed a tremendous growth of sequenced genomic data. However, the algorithms and computational power required to expeditiously process, classify, and analyze genomic data has lagged considerably. In bioinformatics, one of the most challenging and computationally intensive processes, which may take up to weeks of compute time, is the assembly of large size genomes. Several computationally feasible sequential assemblers have been devised and implemented to assist in the process. A few algorithms also have been parallelized to speed up the assembly process. However, very little has been done to thoroughly analyze such parallel algorithms using the specific metrics of parallel computing paradigm. It is essential to investigate parallel assembly algorithms to ascertain their scalability and efficiency. The genomic data varies considerably in size that ranges from a few thousand units of data to several billions. Moreover, the degree of repetition in the data also exhibits high variance from one set to another. Therefore, we must establish an association between the nature, size, and degree of repetition in the genomic data and the best parallel assembly algorithm. The paper includes a comparative analysis of some of the most widely used approaches to assemble genomes using the parallel computing paradigm.