scholarly journals Energy Consumption Estimation Model for Complete Coverage of a Tetromino Inspired Reconfigurable Surface Tiling Robot

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2257 ◽  
Author(s):  
Arunmozhi Manimuthu ◽  
Anh Vu Le ◽  
Rajesh Elara Mohan ◽  
Prabahar Veerajagadeshwar ◽  
Nguyen Huu Khanh Nhan ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Energy Cost ◽  
Pulse Width Modulation ◽  
Real Life ◽  
Area Coverage ◽  
Estimation Model ◽  
Energy Estimation ◽  
Proposed Model ◽  
Estimation Scheme ◽  
Optimal Area

As autonomous tiling devices begin to perform floor cleaning, agriculture harvesting, surface painting tasks, with minimal or no human intervention, a new challenge arises: these devices also need to be energy efficient and be constantly aware of the energy expenditure during deployments. Typical approaches to this end are often limited to fixed morphology robots with little or no consideration for reconfiguring class of robots. The main contribution of the paper is an energy estimation scheme that allows estimating the energy consumption when a tetromino inspired reconfigurable floor tiling robot, hTetro moves from one configuration to another for completing the area covering task. To this end, the proposed model applying the Newton-Raphson algorithm in combination with Pulse width modulation (PWM)-H bridge to characterize the energy cost associated with locomotion gaits across all valid morphologies and identify optimal area coverage strategy among available options is presented. We validate our proposed approach using an 8’ × 8’ square testbed where there exist 12 possible solutions for complete area coverage however with varying levels of energy cost. Then, we implemented our approach to our hTetro platform and conducted experiments in a real-life environment. Experimental results demonstrate the application of our model in identifying the optimal area coverage strategy that has the least associated energy cost.

Trip Energy Estimation Methodology and Model Based on Real-World Driving Data for Green-Routing Applications

2018 ◽  
Vol 2672 (24) ◽  
pp. 41-48 ◽  
Author(s):  
Jacob Holden ◽  
Harrison Van Til ◽  
Eric Wood ◽  
Lei Zhu ◽  
Jeffrey Gonder ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Fuel Consumption ◽  
Real World ◽  
Energy Use ◽  
Average Energy ◽  
Estimation Model ◽  
Energy Estimation ◽  
Conventional Vehicle ◽  
Look Up Table ◽  
Wide Range

A data-informed model to predict energy use for a proposed vehicle trip has been developed in this paper. The methodology leverages roughly one million miles of real-world driving data to generate the estimation model. Driving is categorized at the sub-trip level by average speed, road gradient, and road network geometry, then aggregated by category. An average energy consumption rate is determined for each category, creating an energy rate look-up table. Proposed vehicle trips are then categorized in the same manner, and estimated energy rates are appended from the look-up table. The methodology is robust and applicable to a wide range of driving data. The model has been trained on vehicle travel profiles from the Transportation Secure Data Center at the National Renewable Energy Laboratory and validated against on-road fuel consumption data from testing in Phoenix, Arizona. When compared against the detailed on-road conventional vehicle fuel consumption test data, the energy estimation model accurately predicted which route would consume less fuel over a dozen different tests. When compared against a larger set of real-world origin–destination pairs, it is estimated that implementing the present methodology should accurately select the route that consumes the least fuel 90% of the time. The model results can be used to inform control strategies in routing tools, such as change in departure time, alternate routing, and alternate destinations to reduce energy consumption. This work provides a highly extensible framework that allows the model to be tuned to a specific driver or vehicle type.

scholarly journals Residential End-Use Energy Estimation Models in Korean Apartment Units through Multiple Regression Analysis

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2327 ◽  
Author(s):  
Soo-Jin Lee ◽  
You-Jeong Kim ◽  
Hye-Sun Jin ◽  
Sung-Im Kim ◽  
Soo-Yeon Ha ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Energy Consumption ◽  
Survey Data ◽  
Field Survey ◽  
Maximum Error ◽  
Hot Water ◽  
Annual Data ◽  
Estimation Model ◽  
Energy Estimation ◽  
End Use

The aim of this study was to develop a mathematical regression model for predicting end-use energy consumption in the residential sector. To this end, housing characteristics were collected through a field survey and in-depth interviews with residents of 71 households (15 apartment complexes) in Seoul, South Korea, and annual data on end-use energy consumption were collected from measurement systems installed within each apartment unit. Based on the data collected, correlativity between the field-survey data and end-use energy consumption was analyzed, and effective independent variables from the field-survey data were selected. Regression models were developed and validated for estimating six end uses of energy consumption: heating, cooling, domestic hot water (DHW), lighting, electric appliances, and cooking. Regression analysis for ventilation was not applied, and instead a calculation formula was derived, because the energy-consumption proportion was too low. The adj-R2 of the estimation model ranged from 0.406 to 0.703, and the maximum error between measured and estimated values was around ±30%, depending on the end use.

scholarly journals An Exhaustive Search Energy Optimization Method for Residential Building Envelope in Different Climatic Zones of Kazakhstan

Buildings ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 633
Author(s):  
Mirzhan Kaderzhanov ◽  
Shazim Ali Memon ◽  
Assemgul Saurbayeva ◽  
Jong R. Kim
Keyword(s):  
Energy Consumption ◽  
Energy Savings ◽  
Residential Buildings ◽  
Average Energy ◽  
Optimization Method ◽  
Building Envelope ◽  
Energy Reduction ◽  
Estimation Model ◽  
Total Energy Consumption ◽  
Energy Estimation

Nowadays, the residential sector of Kazakhstan accounts for about 30% of the total energy consumption. Therefore, it is essential to analyze the energy estimation model for residential buildings in Kazakhstan so as to reduce energy consumption. This research is aimed to develop the Overall Thermal Transfer Value (OTTV) based Building Energy Simulation Model (BESM) for the reduction of energy consumption through the envelope of residential buildings in seven cities of Kazakhstan. A brute force optimization method was adopted to obtain the optimal envelope configuration varying window-to-wall ratio (WWR), the angle of a pitched roof, the depth of the overhang shading system, the thermal conductivity, and the thicknesses of wall composition materials. In addition, orientation-related analyses of the optimized cases were conducted. Finally, the economic evaluation of the base and optimized cases were presented. The results showed that an average energy reduction for heating was 6156.8 kWh, while for cooling it was almost 1912.17 kWh. The heating and cooling energy savings were 16.59% and 16.69%, respectively. The frontage of the building model directed towards the south in the cold season and north in the hot season demonstrated around 21% and 32% of energy reduction, respectively. The energy cost savings varied between 9657 to 119,221 ₸ for heating, 9622 to 36,088 ₸ for cooling.

scholarly journals Developing a Hydrogen Fuel Cell Vehicle (HFCV) Energy Consumption Model for Transportation Applications

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 529
Author(s):  
Kyoungho Ahn ◽  
Hesham A. Rakha
Keyword(s):  
Energy Consumption ◽  
Fuel Cell ◽  
Fuel Cell Vehicle ◽  
Hydrogen Fuel Cell ◽  
Hydrogen Fuel ◽  
Energy Estimation ◽  
Transportation Projects ◽  
Proposed Model ◽  
Energy Consumption Model ◽  
Transportation Applications

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.

scholarly journals Energy estimation for differential drive mobile robots on straight and rotational trajectories

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142090965
Author(s):  
Mauricio F Jaramillo-Morales ◽  
Sedat Dogru ◽  
Juan B Gomez-Mendoza ◽  
Lino Marques
Keyword(s):  
Energy Consumption ◽  
Mobile Robots ◽  
Experimental Validation ◽  
Energy Estimation ◽  
Differential Drive ◽  
Curved Trajectories ◽  
Proposed Model ◽  
Power Models ◽  
Robotic Tasks ◽  
Energy Autonomy

Energy autonomy is an important aspect that needs to be improved in order to increase efficiency in mobile robotic tasks. Having accurate power models allows the estimation of energy consumption along different trajectories. This article proposes a power model for two-wheel differential drive mobile robots. The proposed model takes into account the dynamic parameters of the robot and its motors, and predicts the energy consumption for trajectories with variable accelerations and variable payloads. The experimental validation of the proposed model was performed with a Nomad Super Scout II mobile robot which was driven along straight and curved trajectories, with different payloads and accelerations. The experiments using the proposed model showed accuracies of 96.67% along straight trajectories and 81.25% along curved trajectories in the estimation of energy consumption.

Efficient Model for Minimizing Energy in Wireless Sensor Network for Internet of Things

2019 ◽  
Vol 09 ◽  
Author(s):  
Jay Kumar Jain ◽  
Dipti Chauhan ◽  
Sanjay Sharma
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Reconstruction Error ◽  
Wireless Sensor ◽  
Estimation Model ◽  
Energy Estimation ◽  
Proposed Model ◽  
Overall Performance ◽  
Function Measure

Background & Objective: The main aim of this paper is to development of efficient model of minimize the energy for wireless sensor networks. In this paper we have proposed the dual probability based energy estimation model in wireless sensor network. Methods: We proposed dual probability based function measure the expected value of energy for the transmission of data. This function creates subgroup of networks based on energy function and will change the operation of energy management in scenario of sensor node data processing. This function also integrates the cloud based services with sensor network. The benefit of this function is that it increases the throughput of network and quality of service. Results: The proposed model is simulated in MATLAB R-2014a environment and the results are obtained using different scenario of network density. Finally, we analyze the performance of our proposed work with respect to following metrics: Data Utility, Energy Consumptions, and Data Reconstruction Error. Conclusion: The dual probability based function reduces the level value of sensors node grouping of data according to their design model. So the overall performance of proposed model is better instead of CP model.

Risk-Based Robust Bidding Strategies for EVs’ Aggregators in Day-ahead Markets with Uncertainty

2020 ◽  
Author(s):  
Ahmed Abdelmoaty ◽  
Wessam Mesbah ◽  
Mohammad A. M. Abdel-Aal ◽  
Ali T. Alawami
Keyword(s):  
Robust Optimization ◽  
Electricity Market ◽  
Real Life ◽  
Optimization Approach ◽  
Case Scenario ◽  
Worst Case ◽  
Bidding Strategies ◽  
Wind Generators ◽  
Proposed Model ◽  
Optimal Bidding

In the recent electricity market framework, the profit of the generation companies depends on the decision of the operator on the schedule of its units, the energy price, and the optimal bidding strategies. Due to the expanded integration of uncertain renewable generators which is highly intermittent such as wind plants, the coordination with other facilities to mitigate the risks of imbalances is mandatory. Accordingly, coordination of wind generators with the evolutionary Electric Vehicles (EVs) is expected to boost the performance of the grid. In this paper, we propose a robust optimization approach for the coordination between the wind-thermal generators and the EVs in a virtual<br>power plant (VPP) environment. The objective of maximizing the profit of the VPP Operator (VPPO) is studied. The optimal bidding strategy of the VPPO in the day-ahead market under uncertainties of wind power, energy<br>prices, imbalance prices, and demand is obtained for the worst case scenario. A case study is conducted to assess the e?effectiveness of the proposed model in terms of the VPPO's profit. A comparison between the proposed model and the scenario-based optimization was introduced. Our results confirmed that, although the conservative behavior of the worst-case robust optimization model, it helps the decision maker from the fluctuations of the uncertain parameters involved in the production and bidding processes. In addition, robust optimization is a more tractable problem and does not suffer from<br>the high computation burden associated with scenario-based stochastic programming. This makes it more practical for real-life scenarios.<br>

scholarly journals Economical Speed for Optimizing the Travel Time and Energy Consumption in Train Scheduling using a Fuzzy Multi-Objective Model

2021 ◽  
Author(s):  
Ahmad Reza Jafarian-Moghaddam
Keyword(s):  
Energy Consumption ◽  
Travel Time ◽  
Pollutant Emissions ◽  
Scheduling Problems ◽  
Train Scheduling ◽  
Multi Objective ◽  
Consumption Ratio ◽  
Proposed Model ◽  
Objective Model ◽  
Time And Energy

AbstractSpeed is one of the most influential variables in both energy consumption and train scheduling problems. Increasing speed guarantees punctuality, thereby improving railroad capacity and railway stakeholders’ satisfaction and revenues. However, a rise in speed leads to more energy consumption, costs, and thus, more pollutant emissions. Therefore, determining an economic speed, which requires a trade-off between the user’s expectations and the capabilities of the railway system in providing tractive forces to overcome the running resistance due to rail route and moving conditions, is a critical challenge in railway studies. This paper proposes a new fuzzy multi-objective model, which, by integrating micro and macro levels and determining the economical speed for trains in block sections, can optimize train travel time and energy consumption. Implementing the proposed model in a real case with different scenarios for train scheduling reveals that this model can enhance the total travel time by 19% without changing the energy consumption ratio. The proposed model has little need for input from experts’ opinions to determine the rates and parameters.

scholarly journals Predicting the Energy Consumption of a Robot in an Exploration Task Using Optimized Neural Networks

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 920
Author(s):  
Liesle Caballero ◽  
Álvaro Perafan ◽  
Martha Rinaldy ◽  
Winston Percybrooks
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Mobile Robot ◽  
Energy Budget ◽  
Dynamic Models ◽  
Pearson Correlation ◽  
Experimental Conditions ◽  
Grid Map ◽  
Proposed Model ◽  
Exploration Task

This paper deals with the problem of determining a useful energy budget for a mobile robot in a given environment without having to carry out experimental measures for every possible exploration task. The proposed solution uses machine learning models trained on a subset of possible exploration tasks but able to make predictions on untested scenarios. Additionally, the proposed model does not use any kinematic or dynamic models of the robot, which are not always available. The method is based on a neural network with hyperparameter optimization to improve performance. Tabu List optimization strategy is used to determine the hyperparameter values (number of layers and number of neurons per layer) that minimize the percentage relative absolute error (%RAE) while maximize the Pearson correlation coefficient (R) between predicted data and actual data measured under a number of experimental conditions. Once the optimized artificial neural network is trained, it can be used to predict the performance of an exploration algorithm on arbitrary variations of a grid map scenario. Based on such prediction, it is possible to know the energy needed for the robot to complete the exploration task. A total of 128 tests were carried out using a robot executing two exploration algorithms in a grid map with the objective of locating a target whose location is not known a priori by the robot. The experimental energy consumption was measured and compared with the prediction of our model. A success rate of 96.093% was obtained, measured as the percentage of tests where the energy budget suggested by the model was enough to actually carry out the task when compared to the actual energy consumed in the test, suggesting that the proposed model could be useful for energy budgeting in actual mobile robot applications.

Sign in / Sign up

Export Citation Format

Share Document