Volume-1 Issue-1, November 2021 – Indian Journal of Energy and Energy Resources (IJEER)

A1005121121

IJEER Journal — Tue, 30 Nov 2021 10:45:13 +0000

The Indian Journal of Energy and Energy Resources (IJEER) has ISSN 2583-1186 (online), an open-access, peer-reviewed, periodical quarterly international journal, which is published by Lattice Science Publication (LSP) in February, May, August and November. The journal aims to publish high-quality peer–reviewed original articles in the area of Energy and Energy Resources that covers Energy Generation, Distribution, Storage and Management, Renewable (Biomass, Hydro Power, Geothermal, Solar, Wind), Non-Renewable and Energy (Petroleum, Natural Gas, Coal, Nuclear). #Energy Generation #Distribution #Storage and Management #Renewable (Biomass, Hydro Power, Geothermal, Solar, Wind) #Non-Renewable Energy (Petroleum, Natural Gas, Coal, Nuclear) #PhD ademic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons #PhD #Academic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons

An Improved Faster Domain-Specific Predicting Energy Consumption using Neural Network
Ch. Vara Prasad¹, G. Leela Krishna², K. Nikesh³, J. Caroline⁴

¹Ch. Vara Prasad, Department of Computer Science and Engineering, SRM Institute of Science and Technology (SRMIST), Chennai (Tamil Nadu), India.

²G. Leela Krishna, Department of Computer Science and Engineering, SRM Institute of Science and Technology (SRMIST), Chennai (Tamil Nadu), India.

³K. Nikesh, Department of Computer Science and Engineering, SRM Institute of Science and Technology (SRMIST), Chennai (Tamil Nadu), India.

⁴J. Caroline, Department of Computer Science and Engineering, SRM Institute of Science and Technology (SRMIST), Chennai (Tamil Nadu), India.

Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Thermal administration in the large-scale cloud server farms is an essential issue. Expanded host temperature makes areas of interest which fundamentally constructs cooling cost and effects trustworthiness. Precise supposition for have temperature is imperative for dealing with the resources reasonably and furthermore cash saving. Since great numerous dollars are being squandered on coolants in cloud server farms. Temperature assessment is a non-unimportant issue by virtue of thermal varieties in the server farm. Existing answers for heat assessment are wasteful because of their computational intricacy and nonappearance of careful suspicion. We have taken apart basically the precision and time devoured by different suspicion plans utilizing some commencement limits. From our assessment, we tracked down that the Rprop-calculation utilizing strategic initiation work is appropriate as it gives practically 97.2 rate precision. Our forecasted sensor cloud model coordinates Rprop-supposition plot utilizing the strategic origin work in cloud framework which predicts future sensor information, such a lot of that customers request is answered at cloud level which reserves energy as number of transmissions are decreased in the detector organization.

Keywords: Cloud Computing, Thermal administration, Host temperature, Coolants, Rprop Algorithm.

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A1004121121

IJEER Journal — Tue, 30 Nov 2021 10:40:15 +0000

Integration of Energy Efficiency of Conventional Building: A Sustainable Approach
Utsav N. Changole¹, Shantini A. Bokil²

¹Utsav N. Changole, M-Tech Student, Dept. of Civil Engineering, Dr. Vishwanath Karad MIT World Peace University, Pune (Maharashtra), India.

²Shantini A. Bokil, Assistant Professor, HOS, Department of Civil Engineering, Dr. Vishwanath Karad MIT World Peace University, Pune (Maharashtra), India.

Abstract: Green buildings are the structures which are environment responsible and resource efficient during its life cycle. To optimize the operation and maintenance of a building, sustainable requirement should be adopted to minimize the energy efficiency. The objective of this research is to do trail to minimize the energy consumption of a traditional building by adopting energy efficient parameters and analyze the feasibility of a structure converting into sustainable building. The main aim of this research paper is to identify the various parameters which can evaluate energy efficiency. The paper emphasizes the need for adopting an integrated approach with unconventional design construction and impact of effective optimization to achieve sustainable energy efficiency in any structure.

Keywords: Green Environment, Life Cycle Analysis, Sustainability, Energy efficiency.

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A1003121121

IJEER Journal — Tue, 30 Nov 2021 10:35:10 +0000

A Distributed and Partially Centralised Form of Energy Storage System Model to Reduce Energy Curtailment of Renewable Energy System
C.Rahul Guptha¹, Kothuri Ramakrishna², K. Mahesh³

¹C.Rahul Guptha, Research scholar, Department of Electrical and Electronics Engineering, B V Raju Institute of Technology, Narsapur, Telangana, India.

²Kothuri Ramakrishna, Associate Professor, Department of Electrical and Electronics Engineering, B V Raju Institute of Technology, Narsapur, Telangana, India.

³K. Mahesh, Asssitant Professor, Department of Electrical and Electronics Engineering, B V Raju Institute of Technology, Narsapur, Telangana, India.

Abstract: In today’s scenario, the difference between energy demand and load consumption is increasing at a rapid pace. The rising difference is faster than the development of technology, but the resources required to generate electrical energy are depleting. So, new technologies are being adapted to generate electrical energy i.e., using renewable energy sources. But the drawback of renewable energy sources is that the magnitude of energy output is not controllable compared to conventional energy sources. This uncontrollable nature results in curtailment of energy generated at the non-conventional source side integrated to the existing electrical grid. This curtailment of energy leads to waste of energy and the capital to generate the energy. So a secondary source of energy is required to effectively utilize the energy from renewable energy sources, which is the Energy Storage System (ESS). The ESS is used to store excess energy from renewable energy sources, but the question arises when you have to choose only one type of system which is simple enough to setup and can act as solution to the complex problems. Usually, the form of energy storage system adopted is the Battery Energy Storage System (BESS) even this kind of system has some challenges in integrating with the grid and the renewable energy sources. In this paper, the disadvantages of the energy curtailment, and a model is proposed by addressing the challenges of conventional or concentrated BESS and to overcome them. A simulation of the switching techniques during charging and discharging to show the switching based on SOC of the individual BESS units comparing with the other BESS units.

Keywords: Distributed BESS unit, Centralised data logging, Regional Control Unit (RCU), Central Control Unit (CCU)

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A1001121121

IJEER Journal — Tue, 30 Nov 2021 10:17:59 +0000

Applying Machine Learning to Enhancement the Level Energy-Based Smart Grid by the Renewable Environment
Osman Nuri UÇAN¹, Ghaith Thaaer Fadhil Aldoori², Alaa Hamid Mohammed³

¹Prof. Dr. Osman Nuri UÇAN, Department of Electrical and Computer Engineering, Altinbas University Turkey.

²Ghaith Thaaer Fadhil Aldoori, Department of Electrical and Computer Engineering Altinbas University, Turkey.

³Alaa Hamid Mohammed, Department of Electrical and Computer Engineering, Altinbas University, Turkey.

Abstract: Beneficent the power grid (PG) of the electrical system and solar freightage rectifiers stay a confront. We intend to suggest the optimal design of the intelligent system with a smart grid for the efficient operation of the energy management system. This design is based on an interactive combination of machine learning with robust neural networks and control circuits. The best parameters of power grid and robust control are determined via optimization, where NN is tuned using genetic algorithm to achieve the optimal solution. Neural Network is used to enhance the robust control parameters for designing NN of the Machine learning system. The entire scheme is further tuned by hybrid energy parameters under various operating conditions to improve the power grid management performance in terms of charging and rectifying. Performing the proposed analog-implemented energy management controller is evaluated by interfacing it with a hardware prototype experimental application of dual photovoltaic (PV) system.

Keywords: Machine learning, Smart Grid, Energy level, RBF-ANN.

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