Mirpur University of Science and Technology

About: Mirpur University of Science and Technology is a education organization based out in Mirpur Khas, Pakistan. It is known for research contribution in the topics: Nanofluid & Boundary layer. The organization has 509 authors who have published 897 publications receiving 9601 citations.

Prosumer based energy management and sharing in smart grid

Abstract: There is huge expectation from smart power grid to provide sustainable energy services using bi-directional flow of data and power enabled by advanced information, communication and control infrastructure. An important element of such a smart grid is prosumers i.e. the consumers who also produce and share surplus energy with grid and other users. Prosumers are not only an important stakeholder of the future smart grids but also have a vital role in peak demand management. Therefore, it is needed to investigate and review the Prosumers based Energy Management and Sharing (PEMS) along with associated challenges. It will help in understanding and analyzing the impact of prosumers in future smart grids. In order to achieve these objectives, this paper presents a comprehensive review of PEMS in smart grid environment and associated impact on power system reliability and energy sustainability. The process of energy sharing among prosumers involves two key elements: information and communication technologies and optimization techniques. These two elements have been discussed in detail to cover the PEMS implementation requirements. The relevant communications technologies presented in the paper include wired, wireless, short and long range options while linear and nonlinear optimization techniques, in context of PEMS, are described. Various technologies, methodologies and mechanisms adopted for PEMS are comprehensively discussed in order to enhance readers’ intuition. Challenges and issues faced by prosumer communities and energy sharing have also been elaborated in detail.

Load forecasting, dynamic pricing and DSM in smart grid: A review

Abstract: Load forecasting (LF) plays important role in planning and operation of power systems. It is envisioned that future smart grids will utilize LF and dynamic pricing based techniques for effective Demand Side Management (DSM). This paper presents a comprehensive and comparative review of the LF and dynamic pricing schemes in smart grid environment. Real Time Pricing (RTP), Time of Use (ToU) and Critical Peak Pricing (CPP) are discussed in detail. Two major categories of LF: mathematical and artificial intelligence based computational models are elaborated with subcategories. Mathematical models including auto recursive, moving average, auto recursive moving average, auto recursive integrated moving average, exponential smoothing, iterative reweighted mean square, multiple regression, etc. used for effective DSM are discussed. Neural networks, fuzzy logic, expert systems of the second major category of LF models have also been described.

Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review

Abstract: Multimedia content analysis is applied in different real-world computer vision applications, and digital images constitute a major part of multimedia data. In last few years, the complexity of multimedia contents, especially the images, has grown exponentially, and on daily basis, more than millions of images are uploaded at different archives such as Twitter, Facebook, and Instagram. To search for a relevant image from an archive is a challenging research problem for computer vision research community. Most of the search engines retrieve images on the basis of traditional text-based approaches that rely on captions and metadata. In the last two decades, extensive research is reported for content-based image retrieval (CBIR), image classification, and analysis. In CBIR and image classification-based models, high-level image visuals are represented in the form of feature vectors that consists of numerical values. The research shows that there is a significant gap between image feature representation and human visual understanding. Due to this reason, the research presented in this area is focused to reduce the semantic gap between the image feature representation and human visual understanding. In this paper, we aim to present a comprehensive review of the recent development in the area of CBIR and image representation. We analyzed the main aspects of various image retrieval and image representation models from low-level feature extraction to recent semantic deep-learning approaches. The important concepts and major research studies based on CBIR and image representation are discussed in detail, and future research directions are concluded to inspire further research in this area.