energy consumption model
Recently Published Documents


TOTAL DOCUMENTS

270
(FIVE YEARS 88)

H-INDEX

19
(FIVE YEARS 5)

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 545
Author(s):  
Aleksy Kwilinski ◽  
Oleksii Lyulyov ◽  
Henryk Dzwigol ◽  
Ihor Vakulenko ◽  
Tetyana Pimonenko

The COVID-19 pandemic has significantly affected the energy sector. The new behavior of industrial and non-commercial consumers changes the energy consumption model. In addition, the constraints associated with the coronavirus crisis have led to environmental effects from declining economic activity. The research is based on evidence from around the world showing significant reductions in emissions and improved air quality. This situation requires rethinking the energy development strategy, particularly the construction of smart grids as a leading direction of energy development. Evaluating the efficiency of smart grids is a vital tool for disseminating successful experience in improving their management. This paper proposes an approach to a comprehensive assessment of smart grids based on a comparative analysis of existing methods, taking into account the changes that need to be considered after the experience gained from the COVID-19 pandemic. The approach provides an accurate set of efficiency indicators for assessing smart grids to account for the direct and indirect effects of smart grids’ implementation. This evaluation approach can be helpful to policymakers in developing energy efficiency programs and implementing energy policy.


Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 529
Author(s):  
Kyoungho Ahn ◽  
Hesham A. Rakha

This paper presents a simple hydrogen fuel cell vehicle (HFCV) energy consumption model. Simple fuel/energy consumption models have been developed and employed to estimate the energy and environmental impacts of various transportation projects for internal combustion engine vehicles (ICEVs), battery electric vehicles (BEVs), and hybrid electric vehicles (HEVs). However, there are few published results on HFCV energy models that can be simply implemented in transportation applications. The proposed HFCV energy model computes instantaneous energy consumption utilizing instantaneous vehicle speed, acceleration, and roadway grade as input variables. The mode accurately estimates energy consumption, generating errors of 0.86% and 2.17% relative to laboratory data for the fuel cell estimation and the total energy estimation, respectively. Furthermore, this work validated the proposed model against independent data and found that the new model accurately estimated the energy consumption, producing an error of 1.9% and 1.0% relative to empirical data for the fuel cell and the total energy estimation, respectively. The results demonstrate that transportation engineers, policy makers, automakers, and environmental engineers can use the proposed model to evaluate the energy consumption effects of transportation projects and connected and automated vehicle (CAV) transportation applications within microscopic traffic simulation models.


2021 ◽  
Author(s):  
Shrikant Shankarrao Pawar ◽  
Tufan Chandra Bera ◽  
Kuldip Singh Sangwan

Abstract The accurate estimation of energy consumption is beneficial to manufacturing enterprises economically as well as to overcome global energy crisis. The present work concentrates on developing an energy consumption model in milling of variable curved geometries where magnitudes and directions of workpiece curvature vary along tool contact path of a component. The current work deals with estimation and analysis of energy consumption in peripheral milling of variable curved surfaces where cutting forces differ along tool contact path in the presence of workpiece curvature. The proposed hybrid model developed in MATLAB involves process mechanics, cutting forces and energy consumption and have modules for idle, auxiliary and cutting power. The proposed model is validated by the experimental work. The model is generic and versatile in nature and is useful for milling of straight, circular and curved surfaces. In addition to it, the influence of workpiece curvature on power consumption has been investigated to realize the variation of power consumption along the tool contact path. The developed model offers a basic platform to understand and characterize the energy consumption for general peripheral milling considering workpiece geometry. The comparison of predicted and measured results indicate that the model is capable to estimate the power consumption accurately. The proposed model will be used by the practitioners to find the optimum cutting conditions to reduce power consumption during the machining of curved geometries; a pragmatic condition but not much researched condition in machining.


2021 ◽  
Vol 9 (11) ◽  
pp. 1164
Author(s):  
Yang Song ◽  
Huangjie Ye ◽  
Yanhui Wang ◽  
Wendong Niu ◽  
Xu Wan ◽  
...  

Energy management is a critical and challenging factor required for efficient and safe operation of underwater gliders (UGs), and the energy consumption model (ECM) is indispensable. In this paper, a more complete ECM of UGs is established, which considers ocean currents, seawater density variation, deformation of the pressure hull, and asymmetry of gliding motion during descending and ascending. Sea trial data are used to make a comparison between ECMs with and without the consideration of ocean currents, and the results prove that the ECM that considers the currents has a significantly higher accuracy. Then, the relationship between energy consumption and multiple parameters, including gliding velocity relative to the current, absolute gliding angle, and diving depth, is revealed. Finally, a simple example is considered to illustrate the effects of the depth-averaged current on the energy consumption.


2021 ◽  
Vol 9 ◽  
Author(s):  
Yujun Su ◽  
Mingyao Zou ◽  
Cheng Jiang ◽  
Hong Qian

As to the nonlinear and time-varying problems of the energy consumption model, this paper proposes an adaptive hybrid modeling method. Firstly, the recursive least squares algorithm with adaptive forgetting factor based on fuzzy algorithm and recursive least squares algorithm is used to identify the simplified mechanism energy consumption model, which solves the data saturation phenomenon and the weights of the “old and new” data during the online identification process and guarantees the adaptability of the mechanism model. Secondly, because there is a deviation between the identified model and the simplified mechanism energy consumption model, the deviation compensation model of mechanism model is established through kernel partial least squares algorithm and the model updating strategy with sliding window, which is used to update the deviation compensation model, and then the adaptive hybrid model is established by combining with the mechanism model identified online and updated deviation compensation model. Finally, the effectiveness, generalization and adaptability of the model are verified by the actual operating data of a single working condition and variable working conditions. And comparing with the mechanism model and the data model, The comparison results show that the adaptive hybrid model has higher calculation accuracy with adaptation.


Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6398
Author(s):  
Sébastien Maudet ◽  
Guillaume Andrieux ◽  
Romain Chevillon ◽  
Jean-François Diouris

LPWAN technologies such as LoRa are widely used for the deployment of IoT applications, in particular for use cases requiring wide coverage and low energy consumption. To minimize the maintenance cost, which can become significant when the number of sensors deployed is large, it is essential to optimize the lifetime of nodes, which remains an important research topic. For this reason, it is necessary that it is based on a fine energy consumption model. Unfortunately, many existing consumption models do not take into account the specifications of the LoRaWAN protocol. In this paper, a refined energy consumption model based on in-situ measurements is provided for a LoRaWAN node. This improved model takes into account the number of nodes in the network, the collision probability that depends on the density of sensors, and the number of retransmissions. Results show the influence of the number of nodes in a LoRaWAN network on the energy consumption of a node and demonstrate that the number of sensors that can be integrated into a LoRaWAN network is limited due to the probability of collision.


Sign in / Sign up

Export Citation Format

Share Document