Accurate Energy Consumption Model Research Articles

Industrial robot energy consumption model identification: A coupling model-driven and data-driven paradigm

Due to wide distribution and low energy efficiency, the energy-saving in industrial robots (IRs) is attracting extensive attention. Accurate energy consumption (EC) models of IRs lay the foundation for energy-saving. However, most dynamic and electrical parameters of IRs are not disclosed by manufacturers, which leads to the invalidity of most model-based EC prediction methods. To bridge this gap, a mechanism-data hybrid-driven method is proposed to predict the EC of IRs in this paper. First, a joint torque prediction model integrating a hybrid-driven parameter identification is developed based on deep reinforcement learning (DRL). The framework for DRL-based parameter identification is constructed through tailored design of interfaces and training mechanisms, wherein the DRL agent can learn to identify the dynamic parameters from the trajectory database. And a deep neural network based on long short-term memory (LSTM) is proposed to predict the EC of IRs according to the joint torques and velocities. The nonlinear item, which is not modeled in the robot dynamic equation, are also encapsulated in the deep neural network with one-dimensional convolutional neural network (1D-CNN) layers to improve the prediction accuracy. To validate the accuracy and efficacy of the proposed method, experiments are conducted on a KUKA KR60-3 industrial robot with different loads. The results demonstrate that the proposed method can predict EC with a mean absolute percentage error of less than 2% under a fixed load and less than 3% under loads not used for agent training.

Energy-Saving Optimization of HVAC Systems Using an Ant Lion Optimizer with Enhancements

The complex and time-varying external climate conditions and multi-equipment variable coupling characteristics make it challenging to optimize the Heating, Ventilation, and Air Conditioning (HVAC) systems in existing buildings effectively. Additionally, the intricate energy exchange processes within HVAC systems present difficulties in developing accurate and generalizable energy consumption models. In response to these challenges, this paper proposes an Ant Lion Optimizer with Enhancements (ALOE) that can dynamically adjust the number of populations and the movement trend to improve the convergence speed and optimization ability, and randomly adjust the movement amplitude to enhance the local optimal escape ability. Finally, a case study of an office building in Hangzhou was carried out, and an overall energy consumption model of the HVAC system based on parameter identification and a general mechanism model was established. In this model, the energy-saving optimization effects of various advanced swarm intelligence optimization algorithms were compared. The experimental results demonstrate that under high, medium, and low load conditions, the ALOE algorithm achieves energy-saving rates of 28.16%, 28.26%, and 24.85%, respectively, the overall energy-saving rate for the entire day reaches 29.06%, which indicates the ALOE has significant superiority. This work will contribute to the development of energy-saving and emission-reduction technologies.

Energy consumption estimation and profiling for queries in distributed database systems based on a bottom-up comprehensive energy model

Quantifying energy consumption of database operations is the foundation of building energy-efficient database systems. Existing approaches only focused on stand-alone database servers, while we are interested in modeling energy consumption of database operations in the distributed environments. In this study, we aim at providing an accurate energy consumption model for queries executed in distributed database systems, profiling energy consumption characteristics of both the individual queries and the system as a whole, to guide the design of green database systems and to reveal opportunities for energy-efficient computing. As the execution of a distributed query is a combination of a set of subqueries that decomposed from it, we start from building energy models for individual subqueries by extracting basic operations that can effectively reflect their energy consumption. Then we use a bottom-up measuring and modeling method as the basis to provide a comprehensive energy estimation model for the entire distributed query. To validate the accuracy of the model, we use queries with a variety complexity that generated from three standard benchmarks (TPC-H, SSB, and Sysbench) on real distributed databases. Extensive experimental results show that our solution can achieve a high average accuracy of 94.63% for distributed queries. More importantly, based on these results we further explored energy consumption patterns for distributed queries and presented several important implications. And finally, a significant role of distributed joins has been discovered in saving both idle and dynamic energy cost of the system. We hope that taking advantage of our observations can help readers who wish to substantially improve energy efficiency for distributed database systems.

Sustainable waste collection optimization using electric vehicles

Solid waste management is a crucial municipal service directly influencing urban livability. Optimizing refuse collection routes enhances service efficiency and reduces costs. The recent introduction of fully electric refuse trucks, boasting features like zero emissions, low noise, and lower operating costs compared to diesel trucks, holds promise. However, their limited autonomy presents challenges that need addressing before widespread deployment. This study proposes a realistic formulation for the refuse collection routing problem with electric trucks, introducing electromobility constraints never considered before in related works. The goal is to find optimal routes covering all collection points while minimizing travel distance within vehicle battery and capacity limits. An accurate energy consumption model is presented, accounting for route elevation, vehicle speed, waste collection operations, and regenerative braking. Genetic algorithms are used to find solutions to the problem, as well as the well-known Clarke and Wright heuristic for comparison purposes. The algorithms are tested on six diverse instances reflecting real-world scenarios in New York City (United States) and Puerto Real (Spain). Results indicate the genetic algorithm’s superiority, yielding solutions with reduced travel distance and consumption while requiring fewer routes compared to the state-of-the-art heuristic.

Multiobjective Energy Consumption Optimization of a Flying–Walking Power Transmission Line Inspection Robot during Flight Missions Using Improved NSGA-II

In order to improve the flight efficiency of a flying–walking power transmission line inspection robot (FPTLIR) during flight missions, an accurate energy consumption model is constructed, and a multiobjective optimization approach using the improved NSGA-II is proposed to address the high energy consumption and long execution time. The energy consumption model is derived from the FPTLIR kinematics to the motor dynamics, with the key parameters validated using a test platform. A multiobjective optimization model is proposed that considers many constraints related to the FPTLIR during missions, offering a comprehensive analysis of the energy consumption and execution time. The NSGA-II algorithm is improved by integrating the Cauchy variation operator and the simulated annealing algorithm, which is used to construct the multiobjective optimization approach. Simulation and experimental results demonstrate that the proposed model accurately predicts the energy consumption of the FPTLIR across different paths and flight conditions with an average relative error ranging from 0.76% to 3.24%. After optimization, energy savings of 5.33% and 5.01% are achieved for on-line and off-line missions, respectively, while maintaining the shortest execution time at the given energy level. The energy consumption optimization approach significantly improves the flight efficiency of the system, providing a reference for analyzing and optimizing energy consumption of inspection robots.

Estimated energy consumption and billing modelling using power availability recorder

Accurate estimation and billing of unmetered facilities and homes by the energy providers in Nigeria has been a serious nightmare to the rural and other consumers. The unjust billing due to inaccurate and wrong estimations have resulted to rancor, facilities damage and economic hardship among the people due to poor equipment and unreliable consumption data. This research work presents accurate energy consumption and billing model using power availability recorder (PAR) which keeps track of how long electricity lasts in a building or facility at discrete time scale. Two residential buildings situated at the University of Nigeria, Nsukka, and one facility each in two chosen communities in Nsukka, were used as commercial and residential electricity consumers respectively for this study. The readings from the PAR were obtained in discrete scale (mini-seconds), and were later converted to seconds and hours. Similarly, the readings from the Energy Consumption meter were taken in kWh. These daily results taken simultaneously at 6 am and 6 pm were used to calculate the Availability Factor (AF), Load Use Factor (LUF) and Average Load (AL) in kW. With PAR as energy meter in significant number of houses within the chosen communities, AF, LUF, and AL for different facilities in the community were obtained and used for the energy billing model development. A relationship, which depends on availability and load use factor, has been suggested in this study with a proportionality constant value of 14.78 kWh/day/customer for use in obtaining better estimates of the bills chargeable to unmetered customers. This will control the black-out relay period usually encountered by consumers instead of the grossly unjust estimated bills currently being used by Nigeria's electricity distribution companies.

Wireless Energy Harvesting for Autonomous Reconfigurable Intelligent Surfaces

In the current contribution, we examine the feasibility of fully-energy-autonomous operation of reconfigurable intelligent surfaces (RIS) through wireless energy harvesting (EH) from incident information signals. Towards this, we first identify the main RIS energy-consuming components and present a suitable and accurate energy-consumption model that is based on the recently proposed integrated controller architecture and includes the energy consumption needed for channel estimation. Building on this model, we introduce a novel RIS architecture that enables EH through RIS unit-cell (UC) splitting. Subsequently, we introduce an EH policy, where a subset of the UCs is used for beamsteering, while the remaining UCs absorb energy. In particular, we formulate a subset al.ocation optimization problem that aims at maximizing the signal-to-noise ratio (SNR) at the receiver without violating the RIS’s energy consumption demands. As a problem solution, we present low-complexity heuristic algorithms. The presented numerical results reveal the feasibility of the proposed architecture and the efficiency of the presented algorithms with respect to both the optimal and very high-complexity brute-force approach and the one corresponding to random subset selection. Furthermore, the results reveal how important the placement of the RIS as close to the transmitter as possible is, for increasing the harvesting effectiveness.

Energy Consumption Analysis of Helicopter Traction Device: A Modeling Method Considering Both Dynamic and Energy Consumption Characteristics of Mechanical Systems

Since modern times, the increase in shipborne equipment has brought tremendous pressure to the energy supply system. Establishing an accurate and reliable energy consumption model that reflects the dynamic characteristics of the system will provide an essential theoretical reference for energy efficiency optimization. This paper proposes a modeling method that considers both the dynamic characteristics and energy consumption characteristics of the system, based on the power bond-graph theory. Firstly, the transmission principle and energy transfer process of hydraulic and electric helicopter traction devices are analyzed. Then, the energy consumption is analyzed, and the state equation and energy equation of the system are established. Finally, the simulation tests are carried out. The results show that the proposed dynamic modeling method is reasonable and effective and can well reflect the dynamic characteristics and energy consumption characteristics of the system.

Performance analysis of PHY layer for RIS-assisted wireless communication systems with retransmission protocols

Reconfigurable intelligent surface (RIS) is one of the most promising technologies for constructing sixth-generation (6G) wireless networks. It has the capability of controlling the electromagnetic response, thereby improving the communication performance. Current studies about physical (PHY) layer analysis of RIS assisted WLC systems neglect the influence of retransmission protocols. Furthermore, the impacts of traffic rate and limited transit buffer on packet transmission and energy consumption are not considered for RIS assisted WLC systems. Focusing on the above problems, we put forward an insightful model to analyze the PHY layer performance of RIS assisted WLC systems using retransmission protocols. In the modeling process, we firstly provide an E2E (end to end) transmission model for the RIS assisted WLC system. The influence of typical retransmission protocols, containing ARQ (automatic repeat request) and hybrid ARQ (HARQ), channel fading types, frame duration on data transmission are comprehensively taken into consideration. Then, we establish an accurate energy consumption model through analyzing the queueing state of the transmitter with finite buffer size and traffic rate. Based on the above two models, we derive the closed-form expressions of PHY layer metrics for RIS assisted WLC systems considering the impact of retransmission protocols. In addition, we also analyze the bound performance of coverage range and expected power efficiency of RIS assisted WLC system with retransmission protocols. Finally, we evaluate the performance of RIS assisted WLC systems under different retransmission protocols and system parameters, and verify the proposed theoretical model.

Toward Energy-Efficient Multiple IRSs: Federated Learning-Based Configuration Optimization

Intelligent reflecting surface (IRS) can enhance the capacity and cost-effectiveness in future wireless networks substantially. However, the configuration optimization of IRS in an energy-efficient way is still a challenging work. In this paper, we propose a solution to the problem of maximizing the total throughput of a multiple IRSs assisted multi-user communication system. A federated deep learning (FDL) based algorithm is designed to obtain the optimal reflection configurations of all IRSs in parallel, where the model parameters are transmitted instead of the dataset itself as in deep learning (DL). Specifically, a deep neural network (DNN) is formulated to fit the coupling relationship between the coordinate information of users and the optimal reflecting vector of IRS. Meanwhile, the analysis of transmission and computation overhead is performed to establish an accurate energy consumption model. For performance evaluation, we conduct a series of simulations to verify the effectiveness of the FDL framework. The simulation results demonstrate that the test accuracy of the FDL framework is as high as 95.22% with only 1/36 of the transmission energy consumption compared with the DL. Moreover, the total throughput can achieve 93% of the theoretical performance.

Analysis of roll center compensation method for underwater gliders based on deep learning

Underwater glider (UG) is one of the most promising low-power and long-voyage autonomous ocean observation platforms. During the gliding, the roll regulation unit (RRU) frequently works to regulate the disturbed heading due to the influence of time-varying current, biofouling, and cumulative error of RRU. In this paper, an accurate energy consumption model of RRU (ERRU) is established to quantify the impact of RRU on the overall energy consumption, and the trial data collected by the Petrel-L glider, China, in the South China Sea are used to verify the accuracy of ERRU. To minimize the working frequency of RRU, a new roll center compensation method (RCCM) is proposed based on variational mode decomposition and long short-term memory (VMD-LSTM). This study analyzes six classical prediction methods based on deep learning, and the VMD-LSTM at k = 5 method is the optimal one to predict the deviation of roll center for UGs. The results indicate that the proposed RCCM can obtain accurate and reliable compensation value for the roll center and effectively restrain the working frequency of RRU. Compared with the data before compensation, the ERRU of the Petrel-L glider is reduced by approximately 22%. Furthermore, the RCCM can apply to other similar UGs.

Application of machine learning and its improvement technology in modeling of total energy consumption of air conditioning water system.

Accurate energy consumption model is the basis of energy saving optimal control of air conditioning system. The existing energy consumption model of air conditioning water system mainly focuses on a certain equipment or a part of the cycle. However, the coupling between water system equipment will affect the setting of optimal energy consumption of equipment. It is necessary to establish the energy consumption model of water system as a whole. However, air conditioning water system is a highly nonlinear complex system, and its precise physical model is difficult to establish. The main goal of this paper is to develop an accurate machine learning modeling and optimization technique to predict the total energy consumption of air conditioning water system by using the actual operation data collected. The main contributions of this work are as follows: (1) Three commonly used machine learning techniques, artificial neural network (ANN), support vector machine (SVM) and classification regression tree (CART), are used to build prediction models of air conditioning water system energy consumption. The results show that all the three models have fast training speed, but the ANN model has better performance in cross-validation. (2) The improved differential evolution algorithm was used to optimize the parameters (initial weights and thresholds) of the ANN, which solved the problem that the ANN is easy to fall into the local optimal solution. The simulation results show that the root mean square error (RMSE) of the improved model decreases by 20.5%, the mean absolute error (MAE) decreases by 30.2%, and the coefficient of determination (R2) increases from 0.9227 to 0.9512. (3) Sensitivity analysis of the established optimization model shows that chilled water flow, chilled water outlet temperature and air conditioning load are the main factors affecting the total energy consumption.

Secure Eco-Routing with private function evaluations

Ubiquity of connected devices ranging from cellphones to in-vehicle navigation systems has enabled information rich routing and navigation services. Eco-routing utilizes these infrastructure and data about the routes to ascertain and inform the driver the energy cost of traversing a route to their destination. An accurate energy consumption model of the vehicle traversing the route is essential to perform eco-routing effectively. Unfortunately, very accurate energy consumption models also contain operating strategies that their owners are disinclined to disclose publicly. We propose the use of partially homomorphic cryptosystem for private function evaluations to enable secure eco-routing. A novel way to encrypt the energy consumption model to enable secure eco-routing, methods for private evaluation of the encrypted energy consumption model, and the associated protocol are described. Practical considerations for implementing such a system are explored through software implementation.

A transfer-learning based energy consumption modeling method for industrial robots

Industrial robots have been widely utilized in factories to replace or help employees performing tasks such as handling, welding, and assembly. With the increasing breadth of installation and deployment of industrial robots, it is critical to predict and optimize their energy consumption in order to assure their environmentally friendly characteristics. While the data-driven modeling method based on multi-layer perception has been demonstrated to be a viable way for revealing quantitative relationships between operating parameters and energy consumption for industrial robots regardless of physical process or case-specific information, there are still some limitations in terms of prediction range, modeling efficiency, and sample size requirement. To accelerate model rebuilding and improve model accuracy without collecting a large number of samples simultaneously, this paper proposes a transfer-learning-based model creation method for energy consumption of industrial robots, where the associated operating parameters are analyzed, and structure adjustment strategies for multi-layer perception are planned for various industrial robot systems, then, schemes for the reuse of parameters in well-trained networks on source domain and the fine-tunning of multi-layer perception model are formulated to efficiently build the accurate energy consumption model on target domains. Experiments are conducted on two distinct robot systems, the Epson C4 and the Siasun SR10C, with nonidentical operating parameter structures and sample sizes. The results demonstrate that transfer learning models with fine-tuning strategy outperform previous data-driven modeling methods in terms of model accuracy and modeling efficiency in both of these situations.

New Energy Consumption Model for Rotary-Wing UAV Propulsion

Accurate and convenient energy consumption models (ECMs) for rotary-wing unmanned aerial vehicles (UAVs) are important for UAV communication designs. Existing models are complex and inconvenient to use. In this letter, a simple and easy-to-use model with closed-form expression as a function of the initial velocity, acceleration and time duration is derived. Using this model, the UAV flight control parameters, such as polling force and tilt angle, are analyzed in analytical form. Numerical results show the validity and reliability of the proposed model.

Optimizing Energy Savings for a Fleet of Commercial Autonomous Trucks

In the era of Connected and Autonomous Vehicles, platooning has the potential to increase roadway capacity and reduce energy consumption. However, as vehicles try to form platoons they may also expend extra energy. Also, depending on its position within a platoon, the energy savings of each vehicle can be different. Thus, optimizing and quantifying the savings that may be gained from platooning is challenging. In this paper, we develop a simulation-optimization framework to tackle this challenge of quantifying energy savings from platooning. Our optimization model determines vehicle-to-platoon assignments given the current location, speed, and destination of all the vehicles and platoons on the freeway. The simulation model takes these platooning decisions from the optimization model and implements them. Vissim is used to simulate the actions taken by all the vehicles and platoons and capture the energy expended by each vehicle over its entire trip duration. To quantify the energy savings, the system is simulated with and without platooning. That is, the optimization model is turned off when assessing the performance of the system without platooning. In addition to the simulation-optimization framework, an accurate energy consumption model is developed in this paper, which is inspired by the work of Tadakuma and colleagues. The energy consumption model utilizes a hybrid prediction formula for aerodynamic drag reduction in multi-vehicle formations unifying both physical mechanisms and existing empirical study data. Our results show that a system wide savings of about 3% can be realized over 160 kilometers when platoons are formed strategically.

Development of a Transient Energy Prediction Model for Machine Tools

Machine tools are one of the vital equipment in the manufacturing sector and consume a significant amount of energy during the steady state and transient state. An extensive amount of research work had conducted to model energy consumption in a steady state of machine tools. The researchers generally neglect the transient state because the profile of transient state energy consumption is stochastic, and time duration is concise, which results in a significant error during the prediction of total energy consumption of machine tools. This work presents an experimental study to acquire the transient state energy consumption of a machine tool. The Matlab software is used to process the captured data, and an empirical model is developed for the prediction of energy consumed by a machine tool in a transient state. The coefficient of determination is determined to evaluate the fitness of the empirical model. The results indicate that the model can predict the transient state energy consumption accurately. This approach can be conveniently applied to predict the total energy consumption of machine tools accurately. The accurate energy consumption model of machine tools is the foundation for selection of optimal process parameters of a machining process that leads to sustainable manufacturing

Energy consumption model for milling processes considering auxiliary load loss and its applications

With the modern manufacturing industry evolving and advancing and amid a more energy conscious society, high energy demand in manufacturing—particularly in machining—has drawn more and more attention. Accurate energy consumption modelling is critical to the improvement of energy efficiency in machining. In the existing energy models of machining processes, typically the so-called auxiliary load loss (which is whatever the difference between the total energy consumption and the amount of energy consumed due to the physical cutting of material and the idle running of the machine tool) is not considered. However, the auxiliary load loss could account for as much as 15–20% of the total energy consumption; simply ignoring it or making crude assumption on it would obviously lead to a less accurate energy consumption model. In this paper, a new energy consumption model for milling that takes full account of the auxiliary load loss is proposed. The proposed energy consumption model is crucially based on an empirical validation work that correctly characterizes the auxiliary power loss as a quadratic function of the cutting power. Cutting experiments are then carried out that convincingly confirm the correctness of the proposed energy consumption model. Moreover, as an application of the proposed energy consumption model, we perform two deep slot cutting experiments and show how our new energy model can help find the best process parameter—the number of intermediate layers to cut—that will consume the minimum amount of energy, which is as much as more than 52% less than that without considering the auxiliary load loss. It is expected that the proposed energy consumption model will have its application in minimization of energy consumption to a wider range of machining tasks, not limited to only the simplest deep slot cutting operation.

Sensitivity analysis and parameter optimization of energy consumption for underwater gliders

Most underwater gliders (UGs) operate on battery power, making energy a decisive factor for their duration and gliding range. Therefore, low power consumption technology is of great significance for enhancing the performance of UGs. In this paper, an accurate energy consumption model (ECM) and a gliding range model (GRM) of UGs are established. A field trial was conducted in the South China Sea to verify the accuracy of the ECM. To quantify the impact of different parameters on the ECM and GRM, the first-order and total sensitivity indexes of fourteen parameters are calculated through the Sobol sensitivity analysis method. The results indicate that gliding angle is the most influencing parameter on ECM, followed by velocity, diving depth, and drag coefficient KD0. For the GRM, the order is diving depth, velocity, KD0 and gliding angle. Based on a prototype UG, several simulations are performed to determine the optimal parameters leading to the maximum profile number and gliding range. The results demonstrate that with the same average diving depth of 1000 m, the optimal gliding range can increase by 11.97% compared with the gliding range in the actual sea trial.

Applying the na\xefve Bayes classifier to HVAC energy prediction using hourly data

Heating ventilation and air conditioning (HVAC) accounts for approximately 50% of the total energy consumption of buildings. Therefore, many studies have been focused on the simulation and optimal control of HVAC power consumption or the prediction of energy consumption through the construction of energy consumption models and the improvement of HVAC power consumption through energy management methods. The prediction of energy consumption by optimal energy-saving control or energy baseline is dependent on an accurate energy consumption model, however, the accuracy of the energy consumption model is influenced by the model variables. In addition, different operating periods and load conditions also lead to different changes in energy consumption, which will affect the accuracy of optimal energy consumption control or prediction of energy consumption. The present study proposes a method to enhance the accuracy and sensitivity of HVAC power consumption prediction, which involves the use of a clustering technique to locate clusters with similar information within hourly data, the construction of energy consumption models by converting the clustered hourly data into monthly data, and the application of the proposed Naïve Bayes classifier to classify hourly data under different operating conditions into the energy consumption model with the smallest prediction error. A multiple variable regression model and an artificial neural network (ANN) model were compared with the models developed in the present study, and the normalized mean bias error (NMBE) and the coefficient of variation of the root mean squared error (Cv-RMSE) were used as criteria for the predicted energy consumption values.