scholarly journals Energy-Saver Mobile Manipulator Based on Numerical Methods

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1100 ◽  
Author(s):  
Julio Francisco Acosta Núñez ◽  
Víctor Hugo Andaluz Ortiz ◽  
Guillermo González-de-Rivera Peces ◽  
Javier Garrido Salas
Keyword(s):  
Energy Consumption ◽  
Numerical Methods ◽  
Energy Use ◽  
Dynamic Control ◽  
Robotic System ◽  
Experimental Results ◽  
Mobile Manipulator ◽  
Curvature Analysis ◽  
Kinematic And Dynamic Control ◽  
Unified Method

The work presents the kinematic and dynamic control of a mobile robotic manipulator system based on numerical methods. The proposal also presents the curvature analysis of a path not parameterized in time, for the optimization of energy consumption. The energy optimization considers two aspects: the velocity of execution in curves and the amount of movements generated by the robotic system. When a curve occurs on the predefined path, the execution velocity is analyzed throughout the system in a unified method to prevent skid effects from affecting the mobile manipulator, while the number of movements is limited by the redundancy presented by the robotic system to optimize energy use. The experimental results are shown to validate the mechanical and electronic construction of the system, the proposed controllers, and the saving of energy consumption.

scholarly journals Estimation of the Energy Consumption of an All-Terrain Mobile Manipulator for Operations in Steep Vineyards

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 217
Author(s):  
Ivan Hrabar ◽  
Goran Vasiljević ◽  
Zdenko Kovačić
Keyword(s):  
Energy Consumption ◽  
Estimation Method ◽  
Google Earth ◽  
Robotic System ◽  
Robotic Arm ◽  
Mobile Manipulator ◽  
Formal Approach ◽  
Aerial Robot ◽  
Readiness Level ◽  
Flying Robot

A heterogeneous robotic system that can perform various tasks in the steep vineyards of the Mediterranean region was developed and tested as part of the HEKTOR—Heterogeneous Autonomous Robotic System in Viticulture and Mariculture—project. This article describes the design of hardware and an easy-to-use method for evaluating the energy consumption of the system, as well as, indirectly, its deployment readiness level. The heterogeneous robotic system itself consisted of a flying robot—a light autonomous aerial robot (LAAR)—and a ground robot—an all-terrain mobile manipulator (ATMM), composed of an all-terrain mobile robot (ATMR) platform and a seven-degree-of-freedom (DoF) torque-controlled robotic arm. A formal approach to describe the topology and parameters of selected vineyards is presented. It is shown how Google Earth data can be used to make an initial estimation of energy consumption for a selected vineyard. On this basis, estimates of energy consumption were made for the tasks of protective spraying and bud rubbing. The experiments were conducted in two different vineyards, one with a moderate slope and the other with a much steeper slope, to evaluate the proposed estimation method.

scholarly journals DV-DVFS: merging data variety and DVFS technique to manage the energy consumption of big data processing

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Hossein Ahmadvand ◽  
Fouzhan Foroutan ◽  
Mahmood Fathy
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Processing Time ◽  
Experimental Results ◽  
The Other ◽  
Data Sets ◽  
Multiple Sources ◽  
Evaluation Phase ◽  
Dynamic Voltage ◽  
Processing Resources

AbstractData variety is one of the most important features of Big Data. Data variety is the result of aggregating data from multiple sources and uneven distribution of data. This feature of Big Data causes high variation in the consumption of processing resources such as CPU consumption. This issue has been overlooked in previous works. To overcome the mentioned problem, in the present work, we used Dynamic Voltage and Frequency Scaling (DVFS) to reduce the energy consumption of computation. To this goal, we consider two types of deadlines as our constraint. Before applying the DVFS technique to computer nodes, we estimate the processing time and the frequency needed to meet the deadline. In the evaluation phase, we have used a set of data sets and applications. The experimental results show that our proposed approach surpasses the other scenarios in processing real datasets. Based on the experimental results in this paper, DV-DVFS can achieve up to 15% improvement in energy consumption.

scholarly journals Evaluation of CO2 emissions and energy use with different container terminal layouts

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Muhammad Arif Budiyanto ◽  
Muhammad Hanzalah Huzaifi ◽  
Simon Juanda Sirait ◽  
Putu Hangga Nan Prayoga
Keyword(s):  
Energy Efficiency ◽  
Sustainable Development ◽  
Energy Consumption ◽  
Statistical Analysis ◽  
Energy Use ◽  
Container Terminal ◽  
Container Terminals ◽  
Future Planning ◽  
Container Handling

AbstractSustainable development of container terminals is based on energy efficiency and reduction in CO2 emissions. This study estimated the energy consumption and CO2 emissions in container terminals according to their layouts. Energy consumption was calculated based on utility data as well as fuel and electricity consumptions for each container-handling equipment in the container terminal. CO2 emissions were estimated using movement modality based on the number of movements of and distance travelled by each container-handling equipment. A case study involving two types of container terminal layouts i.e. parallel and perpendicular layouts, was conducted. The contributions of each container-handling equipment to the energy consumption and CO2 emissions were estimated and evaluated using statistical analysis. The results of the case study indicated that on the CO2 emissions in parallel and perpendicular layouts were relatively similar (within the range of 16–19 kg/TEUs). These results indicate that both parallel and perpendicular layouts are suitable for future ports based on sustainable development. The results can also be used for future planning of operating patterns and layout selection in container terminals.

scholarly journals Ensemble Prediction Approach Based on Learning to Statistical Model for Efficient Building Energy Consumption Management

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 405
Author(s):  
Anam Nawaz Khan ◽  
Naeem Iqbal ◽  
Rashid Ahmad ◽  
Do-Hyeun Kim
Keyword(s):  
Energy Consumption ◽  
Statistical Model ◽  
Residential Buildings ◽  
Residential Building ◽  
Experimental Results ◽  
Consumption Patterns ◽  
Ensemble Prediction ◽  
Short Term ◽  
Term Energy ◽  
Prediction Approach

With the development of modern power systems (smart grid), energy consumption prediction becomes an essential aspect of resource planning and operations. In the last few decades, industrial and commercial buildings have thoroughly been investigated for consumption patterns. However, due to the unavailability of data, the residential buildings could not get much attention. During the last few years, many solutions have been devised for predicting electric consumption; however, it remains a challenging task due to the dynamic nature of residential consumption patterns. Therefore, a more robust solution is required to improve the model performance and achieve a better prediction accuracy. This paper presents an ensemble approach based on learning to a statistical model to predict the short-term energy consumption of a multifamily residential building. Our proposed approach utilizes Long Short-Term Memory (LSTM) and Kalman Filter (KF) to build an ensemble prediction model to predict short term energy demands of multifamily residential buildings. The proposed approach uses real energy data acquired from the multifamily residential building, South Korea. Different statistical measures are used, such as mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and R2 score, to evaluate the performance of the proposed approach and compare it with existing models. The experimental results reveal that the proposed approach predicts accurately and outperforms the existing models. Furthermore, a comparative analysis is performed to evaluate and compare the proposed model with conventional machine learning models. The experimental results show the effectiveness and significance of the proposed approach compared to existing energy prediction models. The proposed approach will support energy management to effectively plan and manage the energy supply and demands of multifamily residential buildings.

scholarly journals Towards Sustainable Energy Retrofitting, a Simulation for Potential Energy Use Reduction in Residential Buildings in Palestine

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3876
Author(s):  
Sameh Monna ◽  
Adel Juaidi ◽  
Ramez Abdallah ◽  
Aiman Albatayneh ◽  
Patrick Dutournie ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Energy Savings ◽  
Residential Buildings ◽  
Residential Building ◽  
Simulation Models ◽  
Water Heating ◽  
Heating And Cooling ◽  
Space Cooling ◽  
Cooling Loads

Since buildings are one of the major contributors to global warming, efforts should be intensified to make them more energy-efficient, particularly existing buildings. This research intends to analyze the energy savings from a suggested retrofitting program using energy simulation for typical existing residential buildings. For the assessment of the energy retrofitting program using computer simulation, the most commonly utilized residential building types were selected. The energy consumption of those selected residential buildings was assessed, and a baseline for evaluating energy retrofitting was established. Three levels of retrofitting programs were implemented. These levels were ordered by cost, with the first level being the least costly and the third level is the most expensive. The simulation models were created for two different types of buildings in three different climatic zones in Palestine. The findings suggest that water heating, space heating, space cooling, and electric lighting are the highest energy consumers in ordinary houses. Level one measures resulted in a 19–24 percent decrease in energy consumption due to reduced heating and cooling loads. The use of a combination of levels one and two resulted in a decrease of energy consumption for heating, cooling, and lighting by 50–57%. The use of the three levels resulted in a decrease of 71–80% in total energy usage for heating, cooling, lighting, water heating, and air conditioning.

scholarly journals A Decomposition Analysis of the Korean Manufacturing Sector: Monetary vs. Physical Outputs

2021 ◽  
Vol 13 (11) ◽  
pp. 6192
Author(s):  
Junghwan Lee ◽  
Jinsoo Kim
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Driving Forces ◽  
Manufacturing Sector ◽  
Decomposition Analysis ◽  
Primary Energy ◽  
Intensity Effect ◽  
Activity Effect ◽  
Index Decomposition ◽  
Output Indicator

This study analyzes the changes in energy consumption of the Korean manufacturing sector using the index decomposition analysis (IDA) method. To capture the production effect based on actual physical activities, we applied the activity revaluation (AR) approach in the analysis. We also developed energy consumption data in terms of primary energy supply to consider conversion loss in the energy sector to avoid any distortions in the intensity effect. The analysis covers every manufacturing subsector in Korea over the period between 2006 and 2018. Combining two distinctive approaches from the previous literature, the AR approach and primary energy-based analysis gives us helpful findings for a climate policy. First, the overall activity effect estimated from the physical output indicator is lower than that from the monetary output indicator. The monetary indicator shows that the share of energy-intensive industries decreases, whereas the physical indicator shows the opposite. Second, in terms of energy efficiency, the intensity effect is estimated as an increasing factor of energy use, whereas inversed results are shown when we use the monetary indicator. Lastly, unlike the previous studies, the AR approach results indicate that Korean manufacturing sectors have been shifting toward an energy-intensive, so it is hard to anticipate positive intensity effects, which means decreasing energy consumption factor, for a while. These results support why analyzing the driving forces of energy consumption through the AR approach and primary energy base is highly recommended.

scholarly journals Spatiotemporal Patterns and Influencing Mechanism of Urban Residential Energy Consumption in China

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3864
Author(s):  
Qiucheng Li ◽  
Jiang Hu ◽  
Bolin Yu
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Decomposition Analysis ◽  
Theil Index ◽  
Residential Energy ◽  
Residential Energy Consumption ◽  
Index Method ◽  
Inequality Measures ◽  
Influencing Mechanism ◽  
Per Capita

The residential sector has become the second largest energy consumer in China. Urban residential energy consumption (URE) in China is growing rapidly in the process of urbanization. This paper aims to reveal the spatiotemporal dynamic evolution and influencing mechanism of URE in China. The spatiotemporal heterogeneity of URE during 2007–2018 is explored through Kernel density estimation and inequality measures (i.e., Gini coefficient, Theil index, and mean logarithmic deviation). Then, with several advantages over traditional index decomposition analysis approaches, the Generalized Divisia Index Method (GDIM) decomposition is employed to investigate the impacts of eight driving factors on URE. Furthermore, the national and provincial decoupling relationships between URE and residential income increase are studied. It is found that different provinces’ URE present a significant agglomeration effect; the interprovincial inequality in URE increases and then decreases during the study period. The GDIM decomposition results indicate the income effect is the main positive factor driving URE. Besides, urban population, residential area, per capita energy use, and per unit area energy consumption positively influence URE. By contrast, per capita income, energy intensity, and residential density have negative effects on URE. There is evidence that only three decoupling states, i.e., weak decoupling, strong decoupling, and expansive negative decoupling, appear in China during 2007–2018. Specifically, weak decoupling is the dominant state among different regions. Finally, some suggestions are given to speed up the construction of energy-saving cities and promote the decoupling process of residential energy consumption in China. This paper fills some research gaps in urban residential energy research and is important for China’s policymakers.

scholarly journals Investigating Primary Factors Affecting Electricity Consumption in Non-Residential Buildings Using a Data-Driven Approach

Energies ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 4046 ◽  
Author(s):  
Sooyoun Cho ◽  
Jeehang Lee ◽  
Jumi Baek ◽  
Gi-Seok Kim ◽  
Seung-Bok Leigh
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Simulation Analysis ◽  
Residential Buildings ◽  
Electric Energy ◽  
Electricity Consumption ◽  
Building Energy ◽  
Major Effect ◽  
Measuring Instruments ◽  
Building Energy Consumption

Although the latest energy-efficient buildings use a large number of sensors and measuring instruments to predict consumption more accurately, it is generally not possible to identify which data are the most valuable or key for analysis among the tens of thousands of data points. This study selected the electric energy as a subset of total building energy consumption because it accounts for more than 65% of the total building energy consumption, and identified the variables that contribute to electric energy use. However, this study aimed to confirm data from a building using clustering in machine learning, instead of a calculation method from engineering simulation, to examine the variables that were identified and determine whether these variables had a strong correlation with energy consumption. Three different methods confirmed that the major variables related to electric energy consumption were significant. This research has significance because it was able to identify the factors in electric energy, accounting for more than half of the total building energy consumption, that had a major effect on energy consumption and revealed that these key variables alone, not the default values of many different items in simulation analysis, can ensure the reliable prediction of energy consumption.

Control-Oriented Modelling and Optimal Adaptive Control of Parallel Compressors

2021 ◽  
Author(s):  
Ayman Ismail Al Zawaideh ◽  
Khalifa Hassan Al Hosani ◽  
Igor Boiko ◽  
Abdulla AlQassab ◽  
Ibrahim Khan
Keyword(s):  
Energy Consumption ◽  
Optimization Algorithm ◽  
Dynamic Control ◽  
Operating Conditions ◽  
High Rate ◽  
Total Energy Consumption ◽  
Process Industries ◽  
Gas Processing ◽  
Processing Plants ◽  
Efficient Power

Abstract Compressors are widely used to transport gas offshore and onshore. Oil rigs and gas processing plants have several compressors operating either alone, in parallel or in trains. Hence, compressors must be controlled optimally to insure a high rate of production, and efficient power consumption. The aim of this paper is to provide a control algorithm to optimize the compressors operation in parallel in process industries, to minimize energy consumption in variable operating conditions. A dynamic control-oriented model of the compression system has been developed. The optimization algorithm is tested on an experimental prototype having two compressors connected in parallel. The developed optimization algorithm resulted in a better performance and a reduction of the total energy consumption compared to an equal load sharing scheme.

An Investigation of the Impact of COVID-19 Pandemic on Energy Consumption in the United States

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Lindsey Kahn ◽  
Hamidreza Najafi
Keyword(s):  
Energy Consumption ◽  
Correlation Coefficient ◽  
Energy Use ◽  
The United States ◽  
Annual Growth Rate ◽  
Total Consumption ◽  
The Usa ◽  
End Use ◽  
The Times ◽  
The Impact

Abstract Lockdown measures and mobility restrictions to combat the spread of COVID-19 have impacted energy consumption patterns. The overall decline of energy use during lockdown restrictions can best be identified through the analysis of energy consumption by source and end-use sectors. Using monthly energy consumption data, the total 9-months use between January and September for the years 2015–2020 is calculated for each end-use sector (transportation, industrial, residential, and commercial). The cumulative consumption within these 9 months of the petroleum, natural gas, biomass, and electricity energy by the various end-use sectors are compared. The analysis shows that the transportation sector experienced the greatest decline (14.38%). To further analyze the impact of COVID-19 on each state within the USA, the consumption of electricity by each state and each end-use sector in the times before and during the pandemic is used to identify the impact of specific lockdown procedures on energy use. The distinction of state-by-state analysis in this study provides a unique metric for consumption forecasting. The average total consumption for each state was found for the years 2015–2019. The total average annual growth rate (AAGR) for 2020 was used to find a correlation coefficient between COVID-19 case and death rate, population density, and lockdown duration. A correlation coefficient was also calculated between the 2020 AAGR for all sectors and AAGR for each individual end-user. The results show that Indiana had the highest percent reduction in consumption of 10.07% while North Dakota had the highest consumption increase of 7.61%. This is likely due to the amount of industrial consumption relative to other sectors in the state.

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