Energy Estimation Model Research Articles

Compact Model for 3D Printer Energy Estimation and Practical Energy-Saving Strategy

3D printing is emerging as a technology for future production due to its support for human life. Increasingly more printed products include many applications. Developers and companies have expressed their ambition to develop the next generation to bring 3D printers to most families. However, energy efficiency is a big challenge for such devices. In this research, we investigated the power of components given by measurements on commercial 3D printers. We then built a compact model to estimate the energy of 3D printers and proposed an energy-saving strategy, primarily focused on the heating process. We separated thermal plates into two independent temperature sections to cut wasted energy costs when printing specially shaped objects and small prints. In order to reduce power dissipation, the printing process needs to be installed at high ambient temperatures. Experimental results show that our method reduces 23% of total power consumption in comparison to the current commercial device.

An optimization-based planning tool for on-demand mobility service operations

Regions worldwide are adopting and exploring low-speed automated electric shuttle (AES) service as an on-demand shared mobility service in dense geofenced urban areas. Building on this concept, the National Renewable Energy Laboratory (NREL) recently developed the Automated Mobility District (AMD) toolkit. The AMD toolkit—comprising of a travel micro-simulation model and an energy estimation model—estimates the mobility and energy impacts of a given shuttle configuration within an AMD. Early-stage AMD deployments need to find optimal operational configurations that include: (a) passenger capacity of an AES, (b) time-dependent routes, and (c) fleet size (AES units) to satisfy the demand for the region. This research extends the AMD toolkit functionality by developing an optimization-based planning module that will assist in the operations of AES units. We developed a constrained mixed-integer program accounting for passenger waiting time, battery range, and passenger capacity of AES units. For scalability, we demonstrated the Tabu search-based solution technique for a real-world network—a proposed AMD deployment in Greenville, South Carolina, USA. Compared to rule-based operations, our developed solution yields higher travel time and energy savings for the network at different demand levels. The sensitivity analyses for waiting time thresholds indicate nonlinearity in the system performance, underscoring the need to meet shared-use mobility user-level expectations. The developed optimization framework can be adapted and extended to accommodate different categories of shared-use on-demand mobility services.

A novel energy estimation model for constraint based task offloading in mobile cloud computing

The utility of mobile applications has been increased enormously due to the advancements in science and technology to assist the users for various purposes. The main intention of integrating cloud services and resources with the mobile application is to reduce battery usage and to improve the efficiency of mobile devices. Thus the process of shifting a task that can be run on the cloud resources for assistance is referred to as task offloading. The process of task offloading is critical in the field of Mobile Cloud Computing. The major issue that pairs with task offloading are the communication cost estimation of the devices. To overcome the above-mentioned issues and to create an effective task offloading model, A Novel Mobile Cloud Computing framework called Rule Generation based Energy Estimation Model (RG-EEM) is designed. The energy required for executing the task in the local mobile device and cloud is estimated by implementing an energy estimation algorithm. Then a novel constraints specific rule generation algorithm is used to estimate the task execution time and memory utilization of the task, from which the decision has to be taken for offloading or local execution by considering all the possible affecting characteristics. Further, a novel task clustering and scheduling algorithm are implemented to execute the task in the cloud server effectively which will help in allocating similar tasks to a particular virtual machine in the cloud. The RG-EEM algorithm will also help in partitioning the task and parallel execution. The effectiveness of the RG-EEM technique is evaluated using the parametric measures and compared with the existing techniques.

A method for estimating the potential power available to building mounted wind turbines within turbulent urban air flows

Small-scale wind energy applications have shown great promise in terms of their potential contribution to transitions to low carbon economies. However, the energy generation potential of such turbines within built environments has not yet been fully utilised due to the complexity of turbulent urban winds, and the challenges this creates in developing effective scoping tools for viability assessments. Effective scoping tools for turbine systems across sites within built environments require an estimation of power generated by the turbine under turbulent conditions, in addition to more commonly applied assessments based on mean wind speeds. A new methodology is therefore presented here for predicting the power generated by a turbine system operating within an urban wind resource. It was developed by employing high temporal resolution wind measurements from eight potential turbine sites within urban/suburban environments as inputs to a vertical axis wind turbine 2-D double multiple streamtube model. A relationship between turbulence intensity and the unsteady performance coefficient obtained from the turbine model was demonstrated. Hence, an analytical methodology for estimating the unsteady power coefficient at a potential turbine site is proposed. This analytical methodology was combined with an excess energy estimation model to develop a turbine power estimation (TPE) model which is used in predicting the turbine power within urban canopies. Finally, the effect of turbine control response times on the unsteady power coefficient and the turbine power estimation model was assessed. Estimates of turbine performance based on the present methodology allow a more comprehensive assessment of potential urban wind projects.

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

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.

CoFEE: Context‐aware futuristic energy estimation model for sensor nodes using Markov model and autoregression

SummaryNowadays, the emerging internet of things (IoT) technology offers the connectivity and communication between all things (various objects/things, devices, actuators, sensors, and mobile devices) at anywhere and anytime. These devices have embedded environment monitoring capabilities (sensors) and significant computational responsibilities. Most of the devices are working by utilizing their limited resources such as energy, memory, and bandwidth. Obviously, battery power is a crucial factor in any network. It makes tedious overheads to the network operations. Prediction of the future energy of the devices could be more helpful for managing resources, connectivity, and communication between the devices in IoT and wireless sensor networks (WSNs). It also facilitates the reliable internet and network connection establishment to the nodes. Hence, this paper presents an energy estimation model to predict the future energy of devices using the Markov and autoregression model. The proposed model facilitates smarter energy management among internet‐connected devices. Performance results show that the proposed method gives significant improvement compared with the neural network and other existing predictions. Further, the proposed model has very lower error performance metrics such as mean square error and computation overhead. The proposed model yields more perfect energy predictions for a node with 64% to 97% and 16% to 43% of higher prediction accuracy throughout the time series.

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

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.

Deep Learning Approach of Energy Estimation Model of Remote Laser Welding

Due to concerns about energy use in production systems, energy-efficient processes have received much interest from the automotive industry recently. Remote laser welding is an innovative assembly process, but has a critical issue with the energy consumption. Robot companies provide only the average energy use in the technical specification, but process parameters such as robot movement, laser use, and welding path also affect the energy use. Existing literature focuses on measuring energy in standardized conditions in which the welding process is most frequently operated or on modularizing unified blocks in which energy can be estimated using simple calculations. In this paper, the authors propose an integrated approach considering both process variation and machine specification and multiple methods’ comparison. A deep learning approach is used for building the neural network integrated with the effects of process parameters and machine specification. The training dataset used is experimental data measured from a remote laser welding robot producing a car back door assembly. The proposed estimation model is compared with a linear regression approach and shows higher accuracy than other methods.

Energy consumption estimation integrated into the Electric Vehicle Routing Problem

When planning routes for fleets of electric commercial vehicles, it is necessary to precisely predict the energy required to drive and plan for charging whenever needed, in order to manage their driving range limitations. Although there are several energy estimation models available in the literature, so far integration with Vehicle Routing Problems has been limited and without demonstrated accuracy.This paper introduces the Two-stage Electric Vehicle Routing Problem (2sEVRP) that incorporates improved energy consumption estimation by considering detailed topography and speed profiles.First, a method to calculate energy cost coefficients for the road network is outlined. Since the driving cycle is unknown, the model generates an approximation based on a linear function of mass, as the latter is only determined while routing. These coefficients embed information about topography, speed, powertrain efficiency and the effect of acceleration and braking at traffic lights and intersections.Secondly, an integrated two-stage approach is described, which finds the best paths between pairs of destinations and then finds the best routes including battery and time-window constraints. Energy consumption is used as objective function including payload and auxiliary systems. The road cost coefficients are aggregated to generate the path cost coefficients that are used in the routing problem. In this way it is possible to get a proper approximation of the complete driving cycle for the routes and accurate energy consumption estimation.Lastly, numerical experiments are shown based on the road network from Gothenburg-Sweden. Energy estimation is compared with real consumption data from an all-electric bus from a public transport route and with high-fidelity vehicle simulations. Routing experiments focus on trucks for urban distribution of goods. The results indicate that time and energy estimations are significantly more precise than existing methods. Consequently the planned routes are expected to be feasible in terms of energy demand and that charging stops are properly included when necessary.

ENERGY POTENTIAL OF SOLID WASTE GENERATED AT A TERTIARY INSTITUTION: ESTIMATIONS AND CHALLENGES

Waste to energy (WtE) refers to any treatment process that creates energy in the form of electricity or heat from a waste source. This research reviews the potential uses of municipal solid waste generated at the University of Lagos, Akoka campus as a sustainable energy source for the tertiary institution. Waste characterization study of the residual waste at the University’s sorting centre was conducted to determine the amount, composition and physical properties of the waste. A novel compositional trending ratio (CTR) was used to evaluate the possible calorific variation in samples using ASTMD3286-77 method. A validation of the experimental results was carried out using energy estimation model described by Smith and Scott (2005) and World Bank (1999). The major components of the residual waste were mainly polythene materials (24%), inert (30%), organic waste (15%), and paper (15%). The average calorific value of 17.23 MJ/kg and moisture content of 41.3% could potentially generate 34, 787 kWh daily (about 48.32% of the 72,000 KWh energy demand of the University). There was no significant statistics difference between experimental energy estimation of samples and model energy values (p < 0.001) and a Relative Percent Difference (RPD) < 3% (experimental energy 1112.1MJ/Kg, model value 1108.3 MJ/Kg).The major challenge to adopting WtE technology is the gap in daily tonnage of waste generated which can be overcome through collaborative solid waste management programme with closed neighbourhood and tertiary institution. The findings provide resourceful information on sustainable management of waste generated for typical tertiary institution.

Stochastic Wind Energy Management Model within smart grid framework: A joint Bi-directional Service Level Agreement (SLA) between smart grid and Wind Energy District Prosumers

The evolvement of prosumers (energy producing consumers) in Smart Grid (SG) ensures reliable and efficient bi-directional power-flow. However, the prosumers interactions and interfacing within the SG system requires a bi-directional Energy Management Model, a demanding task to monitor, manage, and measure probabilistic prosumers activities. Considering weather parametric effects with Price-Based Programs and generation capacity of prosumers within Energy District (ED) is highly complex and stochastic problem, we propose Stochastic Wind Energy Management Model (SWEMM) with bi-directional energy flows between SG and Wind Energy Prosumers (WEPs). Moreover, our model incorporates an effective Service Level Agreement (SLA) design for efficient energy exchange structure between both parties (SG and WEPs). Furthermore, non-linear Stochastic price model is employed that overcomes the uncertainty of market price with SLA implementation, while wind energy estimation model within ED is employed for prosumer energy generation. The aforementioned models are incorporated for SWEMM that maximizes Prosumers Energy Surplus (PES) and minimizes Prosumer Energy Cost (PEC), while Grid Revenue (GR) maximizes for SG. Finally, data analysis (3D-plots) of Copano Bay (Texas US), model simulation and SLA validation with convergence and divergence plots with tabular statistics prove the effectiveness of our proposed model. We believe that our work is more versatile in modeling stochastic energy management model for SG and WEPs, compared to prior works.

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

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.

Model uncertainty analysis using data analytics for life-cycle assessment (LCA) applications

Objective uncertainty quantification (UQ) of a product life-cycle assessment (LCA) is a critical step for decision-making. Environmental impacts can be measured directly or by using models. Underlying mathematical functions describe a model that approximate the environmental impacts during various LCA stages. In this study, three possible uncertainty sources of a mathematical model, i.e., input variability, model parameter (differentiate from input in this study), and model-form uncertainties, were investigated. A simple and easy to implement method is proposed to quantify each source. Various data analytics methods were used to conduct a thorough model uncertainty analysis; (1) Interval analysis was used for input uncertainty quantification. A direct sampling using Monte Carlo (MC) simulation was used for interval analysis, and results were compared to that of indirect nonlinear optimization as an alternative approach. A machine learning surrogate model was developed to perform direct MC sampling as well as indirect nonlinear optimization. (2) A Bayesian inference was adopted to quantify parameter uncertainty. (3) A recently introduced model correction method based on orthogonal polynomial basis functions was used to evaluate the model-form uncertainty. The methods are applied to a pavement LCA to propagate uncertainties throughout an energy and global warming potential (GWP) estimation model; a case of a pavement section in Chicago metropolitan area was used. Results indicate that each uncertainty source contributes to the overall energy and GWP output of the LCA. Input uncertainty was shown to have significant impact on overall GWP output; for the example case study, GWP interval was around 50%. Parameter uncertainty results showed that an assumption of ± 10% uniform variation in the model parameter priors resulted in 28% variation in the GWP output. Model-form uncertainty had the lowest impact (less than 10% variation in the GWP). This is because the original energy model is relatively accurate in estimating the energy. However, sensitivity of the model-form uncertainty showed that even up to 180% variation in the results can be achieved due to lower original model accuracies. Investigating each uncertainty source of the model indicated the importance of the accurate characterization, propagation, and quantification of uncertainty. The outcome of this study proposed independent and relatively easy to implement methods that provide robust grounds for objective model uncertainty analysis for LCA applications. Assumptions on inputs, parameter distributions, and model form need to be justified. Input uncertainty plays a key role in overall pavement LCA output. The proposed model correction method as well as interval analysis were relatively easy to implement. Research is still needed to develop a more generic and simplified MCMC simulation procedure that is fast to implement.

Uncertainty quantification in the analyses of operational wind power plant performance

In the present work, we examine the variation introduced in the evaluation of an operating plant’s wind power production as a result of the choices analysts make in the processing of the operational data. For this study, an idealized power production for individual turbines over an operational period was predicted by fitting power curves to the turbine production data collected during expected operation (that is, without curtailment or availability losses). A set of 240 possible methods were developed for (a) defining what data represented expected operation and (b) modeling the power curve. The spread in the idealized power production as predicted by the different methods was on average almost 3% for the 100 turbines considered. Such significant variation places a lower bound on the precision with which analysts may employ such data as benchmarks for calibration of their energy estimation processes and limits the potential for identification of refinements to the energy estimation models for improved accuracy.

Design analysis of MVC desalination unit powered by a grid connected photovoltaic system

The use of solar energy especially for desalination of seawater is well adapted for the Saharan region where the fresh water is scarce and solar energy potential is high. This paper presents a design analysis of a single-effect mechanical vapor compression (MVC) desalination unit powered by a grid connected photovoltaic (PV) solar system. The aim of this work is to define, as function of the freshwater needs and site specificity, the embodiment of the MVC components and the PV panels’ size. The proposed approach is based on MVC components models coupled with a solar energy estimation model. The results are illustrated using a case study in Dakhla city sited at the south of Morocco; they show that the embodiment of the global system depends greatly on the temperature of brine and distillate streams. A large gap between these design variables reduces the heat transfer area, but increases the size of the PV system.

An Improved Predictive Model for Energy Estimation in Milling

Abstract The present research work was concerned with the development of an improved predictive model for energy estimation in the machining process. The need for a comprehensive predictive model which can account for generic cutting conditions together with all machine tool related factors was felt and has been attempted here. The proposed model was able to consider all influencing factors such as machine tool specific factors, axes configurations, acceleration effects of feed drives, and machine tool accessories. This component of energy from the machine tool was combined with the cutting energy estimated from cutting forces. This experimentally validated model can estimate energy consumption for any generic case directly from computer numerical control (CNC) programs or process plans. The newly developed model is used to study various machining situations to demonstrate its effectiveness.

Energy consumption characteristics of turn-mill machining

Reducing the energy consumption of manufacturing processes and machine tools can considerably affect the environmental and economic impact of industrial activities. Since the 2008 global financial crisis, many scholars have focused on modeling the energy consumption of basic machining processes such as turning, milling or grinding, etc., and investigated the consumption characteristics of related machine tools. At the same time, various industries have increased their use of more complex and hybrid machine tool systems such as turn-mill machines; these advanced systems have part and operation flexibility and can be set up in a relatively short amount of time. As the complexity of these typically high-precision machining systems increases, understanding their energy consumption characteristics becomes more difficult. This study aimed to develop a generic energy model for turn-mill machine tools and related processes in order to predict the energy consumption of complex parts with both turn and mill features. The generic prediction model is adapted to a high precision, high-end turn-mill machine tool and further verified by two case studies. The results of the first case study revealed that the energy estimation model developed in this study had a 95% accuracy in estimating the energy consumption of a workpiece with both milling and turning features. The second case study investigated energy consumption of orthogonal turn-milling process with a high material removal rate (MRR), first time in literature. The results of this second case study indicate that even though the power requirements of turn-milling are higher than conventional rough cut turning, the high MRR results in a lower total energy consumed per feature.

System identification and data fusion for on-line adaptive energy forecasting in virtual and real commercial buildings

Accurate, computationally efficient, and cost-effective energy forecasting models are essential for model based control. Existing studies in model based control have mostly been focusing on developing energy forecasting models using simplified physics based or data driven models. However, creating and identification the simplified physics model are often challenging, which requires expert knowledge for model simplification and significant engineering efforts for model training. In addition, the accuracy and robustness of data driven models are always bounded by the training data. To this end, developing high fidelity energy forecasting models with less engineering effort and good performance is still an urgent task. Although the previous studies from the authors have shown great promises in a system identification model and outperformed other data-driven and grey box models, they still have large errors at the special operation situations. Therefore, this paper investigates a novel methodology to develop energy estimation models for on-line building control and optimization using an integrated system identification and data fusion approach. The data fusion approach is able to adapt the forecasting model under the special operation situations based on the real measurements. An eigensystem realization algorithm based model reformation method is developed to convert the system identification models into state space models. Kalman filter based data fusion techniques are then implemented on the state space models to improve the model accuracy and robustness. The developed methodology are evaluated using data from a virtual building (simulated) and a real small size commercial building. Three different data fusion intervals: 15, 30, and 60min, have been tested. The overall building energy estimation accuracy from this proposed methodology can reach to above 95% in the virtual building and around 90% in the real building. The results also show that the shorter data fusion interval used, the higher accuracy can be achieved.

Two-parametric model of electron beam in computational dosimetry for radiation processing

Computer simulation of irradiation process of various materials with electron beam (EB) can be applied to correct and control the performances of radiation processing installations. Electron beam energy measurements methods are described in the international standards. The obtained results of measurements can be extended by implementation computational dosimetry. Authors have developed the computational method for determination of EB energy on the base of two-parametric fitting of semi-empirical model for the depth dose distribution initiated by mono-energetic electron beam. The analysis of number experiments show that described method can effectively consider random displacements arising from the use of aluminum wedge with a continuous strip of dosimetric film and minimize the magnitude uncertainty value of the electron energy evaluation, calculated from the experimental data.Two-parametric fitting method is proposed for determination of the electron beam model parameters. These model parameters are as follow: E0 – energy mono-energetic and mono-directional electron source, X0 – the thickness of the aluminum layer, located in front of irradiated object. That allows obtain baseline data related to the characteristic of the electron beam, which can be later on applied for computer modeling of the irradiation process.Model parameters which are defined in the international standards (like Ep– the most probably energy and Rp – practical range) can be linked with characteristics of two-parametric model (E0, X0), which allows to simulate the electron irradiation process. The obtained data from semi-empirical model were checked together with the set of experimental results. The proposed two-parametric model for electron beam energy evaluation and estimation of accuracy for computational dosimetry methods on the base of developed model are discussed.

Energy Cost Models of Smartphones for Task Offloading to the Cloud

Task offloading from smartphones to the cloud is a promising strategy to enhance the computing capability of smartphones and prolong their battery life. However, task offloading introduces a communication cost for those devices. Therefore, the consideration of the communication cost is crucial for the effectiveness of task offloading. To make task offloading beneficial, one of the challenges is to estimate the energy consumed in communication activities of task offloading. Accurate energy estimation models will enable these devices to make the right decisions as to whether or not to perform task offloading, based on the energy cost of the communication activities. Simply put, if the offloading process consumes less energy than processing the task on the device itself, then the task is offloaded to the cloud. To design an energy-aware offloading strategy, we develop energy models of the WLAN, third-generation, and fourth-generation interfaces of smartphones. These models make smartphones capable of accurately estimating the energy cost of task offloading. We validate the models by conducting an extensive set of experiments on five smartphones from different vendors. The experimental results show that our estimation models accurately estimate the energy required to offload tasks.