Energy Cost Prediction Research Articles

Energy cost prediction for chromium removal by nanofiltration membrane

Abstract This paper aims to investigate the energy cost of eliminating Cr(VI) by nanofiltration membranes (NF). The modified pore flow model was utilized to predict the performance of NF membrane in terms of ion retention and water productivity. Then, the energy cost was estimated according to the obtained results from this model as well as available data from literature. The effect of feed flow, applied pressure, and temperature on energy cost were highlighted and thoroughly assessed. It is shown that high retention values can be achieved with relatively high energy cost. Nonetheless, energy cost of 0.04 $/m3 have been estimated for Cr retention less than 95 % at a pressure of 5 bar. It is also assessed that feed flow has a significant influence on energy cost relative to other operating parameters. In particular, increasing feed flow from 40 to 760 L/h leading the energy cost to be increased by twelve times. At high feed flow, however, energy cost can be largely reduced by applying pressure higher than 10 bar. It is concluded that high feed temperature is favored if there is no need for heating equipment.

A unified model and method for forecasting energy consumption in distributed computing systems based on stationary and mobile devices

The subject of research in this article is the forecasting of energy consumption when computing distributed tasks on computer networks built on the basis of server solutions and distributed systems based on personal smartphones. The goal of this study was to create a universal computing energy cost prediction model that can be applied to both traditional and mobile cloud systems. Tasks: conduct an analysis of energy-saving approaches and technologies used to calculate data; consider computer system models and actions with them, namely: model of distributed job, model of distributed computing system, model of distribution strategy; develop a common and uniform dynamic method of forecasting spent energy with a focus on heterogeneous systems; conduct a study of the proposed approach on stationary and mobile devices. The obtained results include. The results of the experimental measurement of the energy consumption of mobile digital systems and stationary ones are presented. The energy efficiency of computing on GPUs of a stationary device based on CUDA technology and GPUs on mobile devices based on Apple Metal technology was determined. Computation during the calculation of 600 frames on a distributed system from mobile devices with failure settings showed a consumption of 15320 joules of energy. Simulation of computing on a distributed system with stationary devices showed a consumption of 52806 joules of energy. This gives us 3,45 times the consumption benefit from computing on mobile devices. Forecasted consumption is also very accurate. Conclusions. The energy consumption assessment model proved to be quite effective. The results of the experiments show that the energy consumption estimation model takes into account the features of the hardware platform where data processing is performed. Computation of data on the GPU of stationary devices loses energy efficiency to a similar implementation on the GPU of Apple Metal from mobile devices. Therefore, the presented results demonstrate the rationality of using mobile graphics processors for energy-efficient information processing.

THERMODYNAMIC AND ECONOMIC ANALYSIS OF A BIOGAS -FUELED MICRO GAS TURBINE WITH COMPRESSOR PHOTOVOLTAIC DRIVE

The increase in global and Brazilian energy demand plus environmental concern due to pollutant emissions are motivating investigation and development of sustainable energy sources and the interaction between them. In this context, electricity generation through solar and solid urban waste energy harnessing has become an effective option for Brazilian energy matrix diversification. Therefore, in order to ratify this trend, a thermodynamic analysis of a 200 kWe micro gas turbine using biogas as fuel and photovoltaic panels to drive compressor shaft is presented in this paper. In terms of methodology, Engineering Equation Solver (EES) Demo was used to run a classic steady state thermodynamic model and a parametric analysis to evaluate MGT performance with a PV system integrated in order to assess specific fuel consumption reduction (SFC) at Belo Horizonte, Minas Gerais, Brazil, operation. Radiasol 2 was the software used to acquire solar insolation data over the year. The fuel economy occurs since power produced in turbine does not need to be discounted to drive compressor shaft during periods when solar insolation is present or when energy generation during the day is sufficient to be credited as Brazilian legislation allows grid integrated systems. In cases that solar energy is not enough or absent, a transmission mechanism similar to a clutch should be considered as well as proposed in other studies to guarantee uninterrupted system operation. Futhermore, an economic analysis was performed to evaluate kWh cost for conventional and hybrid systems. Interest rate, inflation, construction period and amortization period are important parameters taken into account to calculated annuity factor that must be evaluated to predict energy cost. The thermodynamic results showed that SFC=0,718 kg/kWh for conventional operation and SFC=0,266 kg/kWh for hybrid operation for the best operating compressor pressures ratio. The economic results showed that payback time for MGT and MGT with compressor photovoltaic drive are lower than lifetime systems, and hybrid MGT electricity cost is lower when compared to paid ones nowadays.

Energy cost analysis of growing strawberries in a controlled environment chamber

The purpose of this paper is to examine the energy cost of strawberry cultivation in a controlled environment chamber. To determine the energy cost, a 2.25 × 5.05 × 2.2 m3 testing room for growing strawberries was set up for the experiment in Chiang Mai, Thailand. Light-emitting diode (LED) grow lighting and air conditioning were used to regulate the environment inside the room to satisfy the strawberry growing requirements. There were 180 strawberry plants in the room. The energy consumption of the room was recorded for about 20 weeks. A computer model of the controlled environment chamber was developed and validated using the TRaNsient SYStem simulation tool (TRNSYS). The validated model was then used to predict the energy cost for one strawberry crop. In addition, the TRNSYS model was used to predict the energy consumption and costs of the room using weather data from five Southeast Asian cities (Chiang Mai, Kuala Lumpur, Yangon, Ho Chi Minh City, and Vientiane). It was found that the energy cost of growing strawberries in the controlled environment chamber in Chiang Mai was 2170 USD/crop cycle (71,500 THB/crop cycle) which is equivalent to 12 USD/plant. It was assumed that the average yield of strawberries would be around 150–400 grams per plant under protected cultivation for about 35 days from the fertilisation of the first flower. Consequently, the predicted energy cost for growing strawberries in the controlled environment chamber in Vientiane was the lowest, at 906 USD per crop cycle (5 USD/plant or 14 USD/kg), because of the low ambient temperature and low electricity tariff.

Accelerometer Calibration: The Importance of Considering Functionality.

To compare the accuracy and precision of a hip-worn accelerometer to predict energy cost during structured activities across motor performance and disease conditions. 118 adults self-identifying as healthy (n = 44) and those with arthritis (n = 23), multiple sclerosis (n = 18), Parkinson's disease (n = 17), and stroke (n =18) underwent measures of motor performance and were categorized into groups: Group 1, usual; Group 2, moderate impairment; and Group 3, severe impairment. The participants completed structured activities while wearing an accelerometer and a portable metabolic measurement system. Accelerometer-predicted energy cost (metabolic equivalent of tasks [METs]) were compared with measured METs and evaluated across functional impairment and disease conditions. Statistical significance was assessed using linear mixed effect models and Bayesian information criteria to assess model fit. All activities' accelerometer counts per minute (CPM) were 29.5-72.6% less for those with disease compared with those who were healthy. The predicted MET bias was similar across disease, -0.49 (-0.71, -0.27) for arthritis, -0.38 (-0.53, -0.22) for healthy, -0.44 (-0.68, -0.20) for MS, -0.34 (-0.58, -0.09) for Parkinson's, and -0.30 (-0.54, -0.06) for stroke. For functional impairment, there was a graded reduction in CPM for all activities: Group 1, 1,215 CPM (1,129, 1,301); Group 2, 789 CPM (695, 884); and Group 3, 343 CPM (220, 466). The predicted MET bias revealed similar results across the Group 1, -0.37 METs (-0.52, -0.23); Group 2, -0.44 METs (-0.60, -0.28); and Group 3, -0.33 METs (-0.55, -0.13). The Bayesian information criteria showed a better model fit for functional impairment compared with disease condition. Using functionality to improve accelerometer calibration could decrease variability and warrants further exploration to improve accelerometer prediction of physical activity.

Predicting energy cost of public buildings by artificial neural networks, CART, and random forest

The paper deals with modeling the cost of energy consumed in public buildings by leveraging three machine learning methods: artificial neural networks, CART, and random forest regression trees. Energy consumption is one of the major issues in global and national policies, therefore scientific efforts in creating prediction models of energy consumption and cost are highly important. One of the largest energy consumers in every state is its public sector, consisting of educational, health, public administration, military, and other types of public buildings. Recent technologies based on sensor networks and Big data platforms enable collection of large amounts of data that could be used to analyze energy consumption and cost. A real data from Croatian public sector is used in this paper including a large number of constructional, energetic, occupational, climate and other attributes. The algorithms for data pre-processing and modeling by optimizing parameters are suggested. Three strategies were tested: (1) with all available variables, (2) with a filter-based variable selection, and (3) with a wrapper-based variable selection which integrates Boruta algorithm and random forest. Prediction models of energy cost are created using two approaches: (a) comparative usage of artificial neural networks and two types of regression trees, CART and random forest, and (b) integration of RF-Boruta variable selection and machine learning methods for prediction. A cross-validation procedure was used to optimize the artificial neural network and regression tree topology, as well to select the most appropriate activation function. Along with creating a prediction model, the aim of the paper was also to extract the relevant predictors of energy cost in public buildings which are important in planning the construction or renovation of buildings. The results have shown that the second approach which integrates machine learning with Boruta method, where the random forest algorithm is used for both variable reduction and prediction modeling, has produced a higher accuracy of prediction than the individual usage of three machine learning methods. Such findings confirm the potential of hybrid machine learning methods which are suggested in previous research, but in favor of random forest method over CART and artificial neural networks. Regarding the variable selection, the model has extracted heating and occupational data as the most important, followed by constructional, cooling, electricity, and lighting attributes. The model could be implemented in public buildings information systems and their IoT networks within the concept of smart buildings and smart cities.

СОВЕРШЕНСТВОВАНИЕ ТЕХНОЛОГИИ РАБОТЫ БЕНЗОПИЛАМИ НА НЕСПЛОШНЫХ РУБКАХ

The article presents a new comprehensive three-level concept for evaluating design decisions for performing processing operations on incomplete logging, which is distinguished by the ability to simultaneously predict energy costs, technical and economic results, and forestry and environmental consequences for the forest environment. To implement this concept, a structure of a three-level automated system for increasing the efficiency of working with chainsaws on incomplete cutting was proposed by substantiating the optimal technology and organization of work, a set of gasoline-powered tools, a method and parameters for developing apiaries. At the first level, to substantiate the optimal set of gasoline-powered tools, a mathematical model of the process of performing butt cuts for the determination of specific energy costs is proposed. At the second level, to substantiate the optimal technology and labor organization, simulation models of the process of performing processing operations to determine the specific reduced energy costs are proposed. At the third level, in order to justify the optimal method and parameters for apiary development, a generalized criterion is used to assess the efficiency of processing operations taking into account direct production costs, forestry damage, as well as damage to the undergrowth. For each indicated level of tasks to be solved, a mathematical apparatus, information and software recommended for use at logging enterprises was developed. Currently, it has been successfully implemented in the Moscow and Tambov regions. According to the results of the implementation, there is a decrease in the performance of processing operations: specific and specific reduced energy costs (from 4 to 9%); total direct costs (from 3 to 7%); as well as forestry and environmental damage (from 5 to 11%).

Off‐road ground robot path energy cost prediction through probabilistic spatial mapping

AbstractEnergy is an important factor in planning for ground robots, particularly in off‐road environments where the terrain significantly impacts energy usage. Unfortunately, energy costs in these environments are variable and uncertain, making planning difficult. In this paper, we present a method, based on Gaussian process regression (GPR) and known vehicle modeling information, for predicting future path energy costs. The method uses data, collected by a robot operating in a 3D environment with varying terrains, to build a map from inputs (including position, heading, slope, and satellite imagery) to power consumption. The energy costs of future paths are predicted through the summation of power predictions. Importantly, correlations in those predictions are considered to avoid overconfidence. Experimental cross‐validation results demonstrate improved accuracy of path energy cost predictions against a baseline approach, as well as the effect of Gaussian process inputs and kernel choice. Additionally, we show how vehicle modeling can aid in predicting energy costs, particularly when data on the environment is sparse.

Evaluating physiological signal salience for estimating metabolic energy cost from wearable sensors.

Body-in-the-loop optimization algorithms have the capability to automatically tune the parameters of robotic prostheses and exoskeletons to minimize the metabolic energy expenditure of the user. However, current body-in-the-loop algorithms rely on indirect calorimetry to obtain measurements of energy cost, which are noisy, sparsely sampled, time-delayed, and require wearing a respiratory mask. To improve these algorithms, the goal of this work is to predict a user's steady-state energy cost quickly and accurately using physiological signals obtained from portable, wearable sensors. In this paper, we quantified physiological signal salience to discover which signals, or groups of signals, have the best predictive capability when estimating metabolic energy cost. We collected data from 10 healthy individuals performing 6 activities (walking, incline walking, backward walking, running, cycling, and stair climbing) at various speeds or intensities. Subjects wore a suite of physiological sensors that measured breath frequency and volume, limb accelerations, lower limb EMG, heart rate, electrodermal activity, skin temperature, and oxygen saturation; indirect calorimetry was used to establish the 'ground truth' energy cost for each activity. Evaluating Pearson's correlation coefficients and single and multiple linear regression models with cross validation (leave-one- subject-out and leave-one- task-out), we found that 1) filtering the accelerations and EMG signals improved their predictive power, 2) global signals (e.g., heart rate, electrodermal activity) were more sensitive to unknown subjects than tasks, while local signals (e.g., accelerations) were more sensitive to unknown tasks than subjects, and 3) good predictive performance was obtained combining a small number of signals (4-5) from multiple sensor modalities. NEW & NOTEWORTHY In this paper, we systematically compare a large set of physiological signals collected from portable sensors and determine which sensor signals contain the most salient information for predicting steady-state metabolic energy cost, robust to unknown subjects or tasks. This information, together with the comprehensive data set that is published in conjunction with this paper, will enable researchers and clinicians across many fields to develop novel algorithms to predict energy cost from wearable sensors.

Development of a control algorithm aiming at cost-effective operation of a VRF heating system

This study aims to develop a control algorithm that can operate an intermittently working variable refrigerant flow (VRF) heating system in a cost-effective manner. An artificial neural network (ANN) model, which is designed to predict the heating energy cost during the next control cycle, is embedded in the control algorithm. By comparing the predicted energy costs for the different setpoint combinations for the system parameters, such as the air handling unit (AHU) supply air temperature, condensing warm fluid temperature, condensing warm fluid amount, and refrigerant condensing temperature, the control algorithm can determine the most cost-effective setpoints to optimally operate the heating system. Two major processes are conducted—development of the predictive control algorithm in which the ANN model is embedded, and performance tests in terms of prediction accuracy and cost efficiency using computer simulation. Results analysis reveals that the ANN model accurately predicts the energy cost, presenting a low coefficient of variation of the root mean square error value (7.42%) between the simulated and predicted results. In addition, the predictive control algorithm significantly saves on the heating energy cost by as much as 7.93% compared with the conventional heuristic control method. From the results analysis, the ANN model and the control algorithm show the potential for prediction accuracy and cost-effectiveness of the intermittently working VRF heating system.

Development of an energy cost prediction model for a VRF heating system

This study developed a predictive model using artificial neural network (ANN) to forecast the energy cost for a variable refrigerant flow (VRF) heating system. The energy cost is predicted with the ANN model by considering the set-points for the refrigerant condensation temperature, condenser fluid temperature, condenser fluid pressure, and air handling unit supply air temperature together with past operational data and other climatic data. The predicted energy cost was used as a determinant for the control algorithm to optimize the heating system operation in terms of cost.The study consisted of three steps: initial model development, model optimization, and performance evaluation. The neural network toolbox in the Matrix laboratory was used to develop the model and conduct the performance tests. For the model training and performance evaluation, data sets were collected in the winter from a test building.Initial model consisted of a structure that included ten input neurons and a learning method. Then, the optimization process was used to find the optimal structure of the ANN model, which was 1 hidden layer with 15 hidden neurons, while the optimal learning method had a 0.5 learning rate and 0.4 momentum. In the performance evaluation, the optimized model demonstrated its prediction accuracy to be within the recommended level, with 0.8417 r2 and 4.87% coefficient of variation root mean squared error between the measured and the predicted costs, thus proving its applicability in the control algorithm to supply a comfortable indoor thermal environment in a cost-efficient manner.

The Validity of an Energy Cost Prediction Equation for Unloaded Cycling

The Validity of an Energy Cost Prediction Equation for Unloaded Cycling

Energy Expenditure During Incline Walking – Benefits of Integrating a Barometer into Activity Monitors

Objective: This study aimed to compare different methods to determine energy expenditure (EE) on incline walking. Approach: The methods tested were a conventional triaxial accelerometer (GT3X), a versatile system (SenseWear), both utilizing single regression models, and a device equipped with a triaxial accelerometer and an air pressure sensor (move II). Twenty-five healthy participants wore the activity monitors and a portable indirect calorimeter (IC) as reference while walking up- and downhill as well as up- and downstairs. The accuracy of the three devices for estimating EE was assessed based on Pearson correlation, ICC, and Bland–Altman analysis. Main results: For GT3X and SenseWear the ICCs showed a weak correlation (between 0.42 and 0.08) and for move II a strong correlation (between 0.97 and 0.84) between the prediction of energy cost and the output from IC, respectively. Overall, the differences absolute to the IC values were 11 to 35 (12 to 30) times higher for the GT3X (SenseWear) than for the move II devices. Significance: The study showed that a device equipped with an accelerometer and an air pressure sensor had higher accuracy in predicting EE during incline walking than a conventional accelerometer or a versatile system.

Automated calculation of reduced resistance to a heat transfer of the window based on BIM technologies

The aim of this work is the development of automated methods of calculation of thermophysical characteristics of the window based on BIM technology. The structure and materials of window frames and glazing are taken into account while calculating reduced resistance to a heat transfer of the window. Software Autodesk Revit was used for creating the information model of the window. As a result of current research a parametric families of windows were created. Each family contains an automatically calculated parameter of reduced resistance to a heat transfer of the window. Windows with one, two and three sashes were considered in the paper. Calculations have been made for wooden and plastic frames. Russian design standards were taken into consideration. The results of the calculations of reduced resistance to a heat transfer of the window are presented in the table at the end of the paper. The introduced method is universal for any window structure. Accurate calculation of heat loss of window units will allow to choose the design decisions and to predict energy costs for building maintenance.

Data-driven optimal charging decision making for connected and automated electric vehicles: A personal usage scenario

This study introduces an optimal charging decision making framework for connected and automated electric vehicles under a personal usage scenario. This framework aims to provide charging strategies, i.e. the choice of charging station and the amount of charged energy, by considering constraints from personal daily itineraries and existing charging infrastructure. A data-driven method is introduced to establish a stochastic energy consumption prediction model with consideration of realistic uncertainties. This is performed by analyzing a large scale electric vehicle data set. A real-time updating method is designed to construct this prediction model from new consecutive data points in an adaptive way for real-world applications. Based on this energy cost prediction framework from real electric vehicle data, multistage optimal charging decision making models are introduced, including a deterministic model for average outcome decision making and a robust model for safest charging strategies. A dynamic programming algorithm is proposed to find the optimal charging strategies. Detailed simulations and case studies demonstrate the performance of the proposed algorithms to find optimal charging strategies. They also show the potential capability of connected and automated electric vehicles to reduce the range anxiety and charging infrastructure dependency.

Adaptive Multiresolution Energy Consumption Prediction for Electric Vehicles

This paper introduces an adaptive multiresolution framework for electric vehicle (EV) energy consumption estimation with real-time capability. Three key parameters, namely powertrain efficiency, wind speed, and rolling resistance, are adaptively estimated using a two-step nonlinear iterative algorithm. Based on this algorithm, a multichannel framework for high-resolution powertrain efficiency estimation is introduced. Employing the “connected vehicles” concept, more reliable trip level energy estimates are achieved by sharing sensed environmental information. In addition, state-of-charge aware energy cost prediction methods of different accuracy and complexity are introduced to combat range anxiety and reduce computational complexity during times of high energy reserves. A variety of detailed simulations illustrate the introduced concept and its benefits for future EV systems.

ANN/BIM-based model for predicting the energy cost of residential buildings in Saudi Arabia

This paper introduces a novel conceptual system to predict the energy cost of residential buildings in the Kingdom of Saudi Arabia (KSA). The paper briefly describes the main models of the developed system to maintain continuity and focuses on the energy cost prediction model. The developed system aims to assist architects in designing residential buildings to minimise energy costs under Saudi Arabian environmental conditions. The system was built based on data collected from house owners in six cities in the eastern province of KSA. The initial results of validating the model demonstrate the system’s capabilities in assisting architects in the selection of the optimum architectural design.

Using wearable physiological sensors to predict energy expenditure.

Lower-limb assistive robotic devices are often evaluated by measuring a reduction in the user's energy cost. Using indirect calorimetry to estimate energy cost is poorly suited for real-time estimation and long-term collection. The goal of this study was to use data from wearable sensors to predict energy cost with better temporal resolution and less variability than breath measurements. We collected physiological data (heart rate, electrodermal activity, skin temperature) and mechanical data (EMG, accelerometry) from three healthy subjects walking on a treadmill at various speeds on level ground, inclined, and backwards. Ground truth energy cost was established by averaging steady-state breath measurements. Raw physiological signals correlated well with ground truth energy cost, but raw mechanical signals did not. Correlation of mechanical signals was improved by calculating accelerometer magnitudes and linear envelope EMG signals, and further improved by averaging the signals over several seconds. A multiple linear regression including physiological and mechanical data accurately predicted ground truth energy cost across all subjects and activities tested, with less variability and better temporal resolution than breath measurements. The sensors used in this study were fully portable, and such algorithms could be used to estimate energy cost of users in the real world. This could greatly improve the design, control, and evaluation of lower-limb assistive robotic devices.

A green framework for DBMS based on energy-aware query optimization and energy-efficient query processing

Traditional database systems result in high energy consumption and low energy efficiency due to the lack of consideration of energy issues and environmental adaptation in the design process. In this study, we report our recent efforts on this issue, with a focus on energy-aware query optimization and energy-efficient query processing. Firstly, a method of modeling energy cost of query plans during query processing based on their resource consumption patterns is proposed, which helps predict energy cost of queries before execution. Secondly, as the traditional query optimizer focuses on solely optimizing for performance and ignores energy-efficient query plans, a query-plan evaluation model is proposed after a comprehensive study of plan evaluation principles. Using the cost model as a basis, the evaluation model can utilizes the trade-offs between power and performance of plans, and helps the query optimizer select plans that meet performance requirements but result in lower energy cost. Finally, a green database framework integrated with the two above models is proposed to enhance a commercial DBMS. Experimental results reveal that, with reliable and accurate statistical data, the proposed framework in this study can achieve significant energy savings and improve energy efficiency.

Li-Ion Synaptic Transistor for Low Power Analog Computing.

Nonvolatile redox transistors (NVRTs) based upon Li-ion battery materials are demonstrated as memory elements for neuromorphic computer architectures with multi-level analog states, "write" linearity, low-voltage switching, and low power dissipation. Simulations of backpropagation using the device properties reach ideal classification accuracy. Physics-based simulations predict energy costs per "write" operation of <10 aJ when scaled to 200 nm × 200 nm.