Energy Consumption Model Research Articles

Effect of Wind on Electric Vehicle Energy Consumption: Sensitivity Analyses and Implications for Range Estimation and Optimal Routing

The energy consumption of electric vehicles (EVs) depends on multiple factors. As it affects vehicle range, energy consumption must be accurately predicted. After a summary of the relevant literature, this paper focuses on two sensitivity studies: one on the impact of wind on energy consumption, and the other on the identifiability of wind in the absence of vehicles’ speed and acceleration profiles. The studies show that wind has a significant impact on the energy consumption for a trip, and without high-resolution knowledge of the acceleration and instantaneous velocity, minor variations in the wind condition do not drastically alter the energy consumption distribution. After that, data sources for the information on the wind velocity and direction are discussed. A data-driven approach based on fuzzy set theory is proposed to incorporate wind into the energy prediction; the best model from this approach shows a notable improvement (3.62%) over the currently implemented production-level predictive model for energy consumption on a data set of 35,139 real-world trips; the improvement is even more pronounced (∼ 7%) for trips with more substantial headwind or tailwind level. Recognizing the interplay between range prediction and route selection, we consider a Markov Decision Process (MDP) framework for battery-charge- and travel-time-aware optimal route planning that accounts for the impact of the wind and includes stops at the charging stations. Finally, we propose a framework that includes wind in the operation of EVs, which consists of learning the impact of wind, incorporating wind forecasting into range and energy prediction, and using that prediction to perform optimal routing.

Spatial Model for Energy Consumption of LEED-Certified Buildings

In this research endeavor, we undertook a comprehensive examination of the factors influencing the energy consumption of LEED-certified buildings, employing both a general linear regression model and a spatial delayed regression model. Gaining a profound understanding of energy utilization patterns within LEED-certified structures can significantly contribute to advancing eco-friendly construction practices. Our investigation draws upon data from a 2010 study conducted at the University of Wisconsin—Madison (UW), encompassing various independent variables, such as temperature, that exhibit some degree of correlation with energy consumption in LEED-certified buildings. The principal objective of this study is twofold: firstly, to ascertain the significance of specific exogenous variables, notably temperature, and secondly, to explore the impact of spatial factors, such as function and location, on energy usage. Our research framework encompasses meticulous data collection and rigorous analysis, culminating in the presentation and discussion of our findings. Notably, our study unveils intriguing insights. Contrary to conventional assumptions, we discovered that the energy consumption of LEED-certified buildings does not exhibit a robust linear association with average annual temperature, the count of power plants within a 50 mile radius, or the LEED rating itself. However, our spatial regression models unveil a compelling narrative: the geographic distribution and functional diversity of distinct LEED buildings wield discernible influence over energy consumption patterns. The implications of our research resonate profoundly in the realm of LEED-certified building design and construction. Architects, builders, and stakeholders should consider the nuanced interplay of spatial variables and geographical positioning in the pursuit of optimal energy efficiency. Moreover, our findings stimulate further inquiry in this field, paving the way for future investigations aimed at refining sustainable building practices and enhancing our understanding of energy consumption within LEED-certified structures.

Thermal characteristics analysis of a new compound electromagnetic linear actuator based on multi-physical coupling method

The new compound electromagnetic linear actuator (CELA) features high energy efficiency, high power ratio, fast response and high precision. However, it is compact in structure due to volume constraints. Also, there are significant internal electrical, magnetic and thermal coupling effects and the temperature rise problem is prominent. In this paper, the electric-magnetic-thermal coupling mechanism of multiple physical fields inside the CELA was analyzed first. Considering the influence of different driving modes on energy consumption, the theoretical model of energy consumption was built based on iron loss separation. The heat transfer mechanism and process of the mechanism were studied with energy consumption as heat source. Considering the influence of thermal characteristics on the material properties, an accurate numerical model with electric-magnetic-thermal multi-physical field bidirectional coupling was established. The energy consumption distribution and the transient change characteristics of the temperature distribution with time and space were studied quantitatively under different driving modes and different operating conditions of the CELA. A test platform for energy consumption and temperature rise was established to verify the accuracy of theoretical analysis and simulation. The results showed the test and simulation results were in good agreement under typical operating conditions, with the error less than 2%.

Sequential attention deep learning architecture with unsupervised pre-training for interpretable and accurate building energy prediction with limited data

ABSTRACT Predicting building energy consumption is important for energy efficiency and reducing carbon emissions. However, deep learning (DL) models for energy consumption forecasting often have limited predictive generalization and lack explainability. To address these challenges, this research adopts a sequential attention deep learning architecture (SADLA) that uses attention mechanisms to learn the importance of different features for predicting cooling energy consumption, which helps building occupants and managers to make informed decisions about energy optimization. The model was trained and tested on comprehensive and scarce datasets in terms of recording periods and predictor numbers. The data were derived from a three-month field experiment in a commercial office building in Seoul, Korea. Furthermore, models were constructed with other prevalent algorithms like Long Short-Term Memory, deep neural networks, and Extreme Gradient Boosting for comparative assessment. The results from the one- and two-week datasets and reduced features suggested that the SADLA-based models surpassed others in model generalization (R2 = 0.961, 0.967, and 0.976 respectively). However, all algorithms demonstrated comparable performance with the three-month dataset achieving an average R2 of 0.970. These findings underscore the potential of the adopted model in addressing data scarcity in existing and new buildings for accurate cooling energy prediction.

Multi-objective optimization of micro-gas turbine coupled with LCPV/T combined cooling, heating and power (CCHP) system based on following electric load strategy

A distributed energy system integrated with solar and natural gas can achieve cascade energy utilization, high efficiency and energy-saving, and significantly reduce carbon emissions, which is receiving increasing attention. Therefore, this paper establishes an optimization model for primary energy consumption saving rate (PECSR), annual total cost saving rate (ATCSR), carbon dioxide emission reduction rate (CDERR) and exergy efficiency of micro-gas turbine coupled with low-concentrating photovoltaic/thermal (LCPV/T) CCHP system, and analyzes the influence of the parameters, such as solar irradiation, LCPV/T inlet temperature, LCPV/T inlet flow, LCPV/T area, low-temperature water source heat pump capacity, high-temperature water source heat pump capacity, the heat transfer efficiency of waste heat recovery boiler (WHRB) and gas boiler capacity, on the objective functions. The analysis results indicate that when the heat exchange efficiency of WHRB reaches 0.71, the CCHP system is most economical. Based on this, the Strength Pareto Evolutionary Algorithm-II (SPEA-II) optimization algorithm and decision-making method combining Technique for order preference by similarity to an ideal solution (TOPSIS) and weight entropy method are employed to solve the model comprehensively, and acquire the optimum solution. After multi-objective optimization and selection, the system exergy efficiency remains around 25 %. Compared with the separation production system, the PECSR and CDERR of the CCHP system are improved by more than 24 % and 41 %, respectively.

What really matters to reduce the energy demand of household electronics? Global sensitivity analysis of circular economy strategies for the United Kingdom

Limiting the global temperatures to +1.5 °C from pre-industrial levels requires achieving net-zero greenhouse gas emissions by 2050. This should be achieved across all sectors, and for electrical and electronic equipment (EEE) the promotion of repair, lifetime extension and other Circular Economy (CE) strategies are often proposed as the main areas for intervention. However, few studies have quantified and compared the effectiveness of different CE strategies at reducing emissions at a national level and into the future. To fill this gap, an extended dynamic stock model was combined with a device energy consumption model to anticipate the embodied, operational, and repair energy requirements arising from the stocks and flows of seven devices up to 2050 in the United Kingdom (UK). To understand the relative influence of all model inputs on device energy demand in 2050, a variance-based sensitivity analysis was performed using the Sobol method. The results show that device size is the most significant variable for six out of the seven devices. Whereas substantial increases in device lifetime and repair rates were shown not to be significant in reducing energy demand. A reduction of 30% of energy demand by 2050 is possible for almost all EEE devices, except laptops and TVs. However, delivering these savings requires significant changes to consumption behaviours such as decreasing device size by about 40% or decreasing ownership rates by approximately two thirds through shared ownership. This analysis prioritises the most impactful strategies to reduce the total energy demand of EEE in the UK.

Utilizing Mixture Regression Models for Clustering Time-Series Energy Consumption of a Plastic Injection Molding Process

Considering the issue of energy consumption reduction in industrial plants, we investigated a clustering method for mining the time-series data related to energy consumption. The industrial case study considered in our work is one of the most energy-intensive processes in the plastics industry: the plastic injection molding process. Concerning the industrial setting, the energy consumption of the injection molding machine was monitored across multiple injection molding cycles. The collected data were then analyzed to establish patterns and trends in the energy consumption of the injection molding process. To this end, we considered mixtures of regression models given their flexibility in modeling heterogeneous time series and clustering time series in an unsupervised machine learning framework. Given the assumption of autocorrelated data and exogenous variables in the mixture model, we implemented an algorithm for model fitting that combined autocorrelated observations with spline and polynomial regressions. Our results demonstrate an accurate grouping of energy-consumption profiles, where each cluster is related to a specific production schedule. The clustering method also provides a unique profile of energy consumption for each cluster, depending on the production schedule and regression approach (i.e., spline and polynomial). According to these profiles, information related to the shape of energy consumption was identified, providing insights into reducing the electrical demand of the plant.

Comparison of ethane recovery processes for lean gas based on a coupled model

Ethane recovery from natural gas is crucial for improving the economic efficiency of enterprises, saving energy and reducing emissions. This study analyses the characteristics of process parameters based on three lean gas ethane recovery processes. A prediction and multiobjective optimization model for ethane recovery and system energy consumption is established. Based on this framework, a new method for ethane recovery process comparison for lean gas is proposed. In the process analysis, two additional coolers are added to the cold residue recycling process to improve ethane recovery. Seven main process parameters are determined based on the three processes. According to the prediction results, support vector regression is the most effective model among the individual models. The prediction accuracy of the support vector regression model based on grey wolf optimization is the highest. Compared with traditional support vector regression, this approach yields average reductions in the mean absolute error, mean absolute percentage error and root mean square error of 63.95%, 64.21% and 47.98%, respectively, reflecting significantly improved prediction accuracy and stability. The multiobjective multiverse optimization algorithm displays the best optimization performance and the highest solution diversity among the optimization results. Additionally, it yields high-quality solutions at the shortest run time. Based on the optimization results, the supplemental rectification with compression process yields the lowest system energy consumption of any method considered and meets the ethane recovery requirements real projects.

Agent-based cloud simulation model for resource management

Driven by the successful service model and growing demand, cloud computing has evolved from a moderate-sized data center consisting of homogeneous resources to a heterogeneous hyper-scale computing ecosystem. This evolution has made the modern cloud environment increasingly complex. Large-scale empirical studies of essential concepts such as resource allocation, virtual machine migration, and operational cost reduction have typically been conducted using simulations. This paper presents an agent-based cloud simulation model for resource management. The focus is on how service placement strategies, service migration, and server consolidation affect the overall performance of homogeneous and heterogeneous clouds, in terms of energy consumption, resource utilization, and violation of service-level agreements. The main cloud elements are modeled as autonomous agents whose properties are encapsulated. The complex relationships between components are realized through asynchronous agent-to-agent interactions. Operating states and statistics are displayed in real time. In the evaluation, the efficiency of the simulator is studied empirically. The performance of various resource management algorithms is assessed using statistical methods, and the accuracy of server energy consumption models is examined. The results show that agent-based models can accurately reflect cloud status at a fine-grained level.

Energy scheduling in a smart energy hub system with hydrogen storage systems and electrical demand management

The energy price uncertainty in energy markets is the main challenge for achieving economic modeling of energy consumption and generation. This paper proposes optimal energy scheduling in a smart energy hub (SEH) under the uncertainty of electricity prices in the day-ahead. The proposed optimization approach is implemented by a two-layer interval approach for minimizing energy generation cost. In first layer optimization, demand side management (DSM) for electrical demand subject to the optimal level of electrical consumption is implemented. In second-layer optimization, the interval approach is used for modeling uncertainty. The energy generation cost is converted to bi-objective functions including average and deviation costs with conflicting nature than each other. Also, the multi-performance of the hydrogen storage system based on electrical and natural gas generation is considered for managing uncertainties in second layer optimization. Since the interval optimization approach is proposed in the second layer, minimizing the average cost and deviation cost are formulated as bi-objective functions by the augmented epsilon-constraint method. In the following, the LINMAP procedure is utilized to achieve maximum compatibility and the best trade-off consequence in the Pareto frontier of the multi-objective functions. Finally, the proposed optimization approach is modeled as a numerical simulation in the case studies to verify obtained results. The participation of the DSM and the hydrogen storage systems leads to minimizing the average and deviation costs by 3.03 % and 3.16 % in comparison with non-participation.

Data-driven probabilistic energy consumption estimation for battery electric vehicles with model uncertainty

ABSTRACT This paper presents a novel probabilistic data-driven approach to trip-level energy consumption estimation of battery electric vehicles (BEVs). As there are very few electric vehicle (EV) charging stations, EV trip energy consumption estimation can make EV routing and charging planning easier for drivers. In this research article, we propose a new driver behavior-centric EV energy consumption estimation model using probabilistic neural networks with model uncertainty. By incorporating model uncertainty into neural networks, we have created an ensemble of neural networks using Monte Carlo approximation. Our method comprehensively considers various vehicle dynamics, driver behavior, and environmental factors to estimate EV energy consumption for a given trip. We propose relative positive acceleration (RPA), average acceleration, and average deceleration as driver behavior factors in EV energy consumption estimation, and this paper shows that the use of these driver behavior features improves the accuracy of the EV energy consumption model significantly. Instead of predicting a single-point estimate for EV trip energy consumption, this proposed method predicts a probability distribution for the EV trip energy consumption. The experimental results of our approach show that our proposed probabilistic neural network with weight uncertainty achieves a mean absolute percentage error of 9.3% and outperforms other existing EV energy consumption models in terms of accuracy.

Large-scale integration of renewable energies by 2050 through demand prediction with ANFIS, Ecuador case study

The growing reliance on hydroelectric power and the risk of future droughts pose significant challenges for power systems, especially in developing countries. To address these challenges, comprehensive long-term energy planning is essential. This paper proposes an optimized electrical system for 2050, using Ecuador as a case study. For forecasting electricity demand, a Neuro-Fuzzy Adaptive Inference System is employed, utilizing real historical data. Subsequently, the EnergyPlan software constructs a long-term energy consumption model, exploring three scenarios based on Ecuador's energy potential. The first scenario represents a 'business as usual’ approach, mirroring the current trend in the Ecuadorian electricity system. In contrast to the second scenario, it encompasses a broader range of renewable sources, including offshore wind, pumped storage, biomass, and geothermal energy. The third scenario extends the second one by incorporating demand response systems, such as vehicle-to-grid and hydrogen-to-grid technologies. In terms of novelty, this study highlights the innovative use of the Neuro-Fuzzy Adaptive Inference System for demand forecasting, along with a comprehensive exploration of multiple scenarios to optimize the electrical system. Research findings indicate that the integration of these new renewable energy sources not only reduces electricity import costs but also ensures surplus electricity production. Consequently, it is anticipated that the 2050 electricity system will reduce its dependence on hydroelectric energy while adopting photovoltaic and wind energy with penetration rates of 65 %, 11.2 %, and 9 %, respectively. This transition will be facilitated by a pumped storage system with a 28 % penetration rate and enhanced connectivity with neighboring countries, enabling the seamless integration of electric and hydrogen vehicles.

Sink Node Placement and Partial Connectivity in Wireless Sensor Networks.

This research delves into the aspects of communication and connectivity problems within random Wireless Sensor Networks (WSNs). It takes into account the distinctive role of the sink node, its placement, and application-specific requirements for effective communication while conserving valuable network resources. Through mathematical modeling, theoretical analysis, and simulation evaluations, we derive, compare, and contrast the probabilities of partial and full connectivity within a random WSN, factoring in network parameters and the maximum allowable hop distance/count hmax. hmax captures the diverse range of delay-sensitive requirements encountered in practical scenarios. Our research underscores the significant impact of the sink node and its placement on network connectivity and the sensor connection rate. The results exemplify a noteworthy decline in the sensor connection rate, dropping from 98.8% to 72.5%, upon relocating the sink node from the network center to the periphery. Moreover, as compared with full connectivity, partial connectivity and the sensor connection rate are more suitable metrics for assessing the communication capability of random WSNs. The results illustrate that 1.367 times more energy is required to connect less than 4% of the remote sensors, based on the examined network settings. Additionally, to increase the sensor connection rate slightly from 96% to 100%, an additional 538% more energy is required in multipath fading based on the widely adopted energy consumption model. This research and its outcomes contribute to establishing appropriate performance metrics and determining critical network parameters for the practical design and implementation of real-world wireless sensor networks.

Assessment of energy consumption for information flow control protocols in IoT devices

In the IoT (Internet of Things), millions of devices are interconnected and devices support too poor computation resources to install enough security functions to protect from malicious accesses. Hence, devices have to be supported with access control mechanisms. The CBAC (Capability-Based Access Control) model is most useful to be as access control model for IoT devices because it is sufficient for each device to just check the capability of each access request. However, illegal and late information flows among devices and subjects occur. Types of IFC (Information Flow Control) protocols like the OI and TBOI protocols to prevent illegal and late types of information flows are proposed in our previous studies. A secure device supports the CBAC model and the IFC protocols. First, an EC (Energy Consumption) model is proposed to show how a secure device consumes the electric energy in this paper. By using the EC model, the IFC protocols are evaluated in terms of the energy consumed by secure devices. A secure device mostly consumes energy to check capability tokens of each access request. In the simulation, the energy consumed by secure devices to process access requests can be reduced by selecting only tokens required. About 69% and 73% of energy in the OI and TBOI protocols can be reduced by adopting the CTS (Capability Token Selection) algorithms, respectively.

Optimization of Exterior Wall Cladding Materials for Residential Buildings Using the Non-Dominated Sorting Genetic Algorithm II (NSGAII) Based on the Integration of Building Information Modeling (BIM) and Life Cycle Assessment (LCA) for Energy Consumption: A Case Study

In today’s construction industry, a topic of paramount importance is reducing energy consumption within buildings. This study endeavors to combine Life Cycle Assessment (LCA) and Building Information Modeling (BIM) through a multi-objective optimization algorithm to enhance the environmental efficiency of buildings. The core objective is the optimization of materials used in the building’s outer shell to effectively curtail operational energy consumption. To achieve this, we employed BIM modeling, parametric simulations with the Energy Plus engine, and Athena to assess the embodied energy in materials. The multi-objective optimization algorithm NSGAII was harnessed to determine the most suitable materials. The results derived from the LCA and BIM analyses illustrate that the selection of optimal materials for residential building facades in Iran can lead to a remarkable 40% reduction in annual average energy consumption. Furthermore, this approach contributes significantly to mitigating global warming potential (GWP). Experts and architects can apply this method to evaluate and select the best materials for various building components, especially in high-rise buildings.

Applying self-powered sensor and support vector machine in load energy consumption modeling and prediction of relational database

This study explores the analysis and modeling of energy consumption in the context of database workloads, aiming to develop an eco-friendly database management system (DBMS). It leverages vibration energy harvesting systems with self-sustaining wireless vibration sensors (WVSs) in combination with the least square support vector machine algorithm to establish an energy consumption model (ECM) for relational database workloads. Through experiments, the performance of self-sustaining WVS in providing power is validated, and the accuracy of the proposed ECM during the execution of Structured Query Language (SQL) statements is evaluated. The findings demonstrate that this approach can reliably predict the energy consumption of database workloads, with a maximum prediction error rate of 10% during SQL statement execution. Furthermore, the ECM developed for relational databases closely approximates actual energy consumption for query operations, with errors ranging from 1 to 4%. In most cases, the predictions are conservative, falling below the actual values. This finding underscores the high predictive accuracy of the ECM in anticipating relational database workloads and their associated energy consumption. Additionally, this paper delves into prediction accuracy under different types of operations and reveals that ECM excels in single-block read operations, outperforming multi-block read operations. ECM exhibits substantial accuracy in predicting energy consumption for SQL statements in sequential and random read modes, especially in specialized database management system environments, where the error rate for the sequential read model is lower. In comparison to alternative models, the proposed ECM offers superior precision. Furthermore, a noticeable correlation between model error and the volume of data processed by SQL statements is observed. In summary, the relational database ECM introduced in this paper provides accurate predictions of workload and database energy consumption, offering a theoretical foundation and practical guidance for the development of eco-friendly DBMS.

Learning eco-driving strategies from human driving trajectories

Eco-driving represents a promising avenue for mitigating energy consumption in road transportation. To enhance the applicability of learning-based eco-driving strategies, this study presents a novel framework that employs offline reinforcement learning in eco-driving control. This framework enables a vehicle agent to acquire eco-driving behavior by leveraging real-world human driving trajectories. Specifically, the human driving trajectories, along with the corresponding traffic signal timing scheme, obtained from empirical data, are utilized to construct a comprehensive Markov Decision Process (MDP) dataset for offline policy training. To accommodate learning from sub-optimal human-driving data, a Conservative Q-learning (CQL) algorithm is deployed. Subsequently, the proposed offline learning method is compared with alternative learning-based, model-based, and rule-based approaches, effectively illustrating the feasibility of offline learning and the efficacy of the CQL algorithm. Notably, the energy consumption is demonstrated to be improved by 67.3% compared to a behavioral car-following model, with only marginal compromise to travel efficiency. Furthermore, a sensitivity analysis is conducted, revealing the generalizability of the offline learning-based method across various simulation configurations and even diverse energy consumption models.

The Energy-Saving Control Criterion for Impact Crushing Machines

The purpose of the article is to substantiate the use of a new criterion for controlling the grinding technological process to improve the use of electrical energy. The subject of the study is the process of using electrical energy in impact grinding machines. The set goal is achieved by solving the following problems: analyzing the energy of the grinding process, determining the parameters of raw materials and operating modes of the electric drive that affect the efficiency of electrical energy usage. Based on theoretical research, a mathematical model of energy consumption in grinding processes has been developed. Analytical studies of the resulting mathematical model showed the nonlinear extreme nature of the specific energy consumption of the grinding process with changing raw material parameters and operating modes. This creates the prerequisites both for using specific electrical energy costs as a control criterion and for the use a controlled electric drive to achieve maximum energy savings. The most important result is that the operating modes of grinding machines could be established by more efficient use of electrical energy. The significance of the work lies in the fact that by adapting the operating modes of the electric drive to the current parameters of the raw materials and the state of the equipment make possible to reduce the specific electrical energy consumption up to 15% and this is the basis for using a criterion based on specific costs in control systems for technological processes with grinding impact machines. For further application of the obtained results it is necessary to conduct experimental studies to confirm the increase in the efficiency of electrical energy use.

Analysis of the Impact of High-Space Building Heating System Solutions on Building Energy Efficiency

The existence of stratification in high-ceiling buildings is real, but not visible or easy noticeable by humans. For field measurements, the Prayer House – The Kaunas Basilica of the Resurrection of Christ, located in Kaunas, Lithuania, was chosen, in which the temperature distribution at different heights (6 m, 8 m, 10 m, 12 m, 14 m, 16 m and 18 m) during the heating season was observed. The Basilica had radiant heaters installed at 5 m height. According to the plan of the Prayer House, CFD model was created, and the temperature distribution of the different heating (radiant heating and floor heating) systems were compared. The IDA-ICE modelling of energy consumption was conducted using the CFD results as a basis, and the economic analysis was performed based on the obtained results. The study confirmed the existence of stratification in high-ceiling buildings.

Development and performance assessment of a new opensource Bayesian inference R platform for building energy model calibration

Many factors contribute to the inherent uncertainty of energy consumption modeling in buildings. It is essential to perform a calibration and sensitivity analysis in order to manage these uncertainties. Despite the availability of several calibration methods, they are often deterministic and lack quantified uncertainties. Moreover, the selection of parameters in building energy modeling for calibration depends on the user’s experience. Therefore, a more rigorous selection process is required. This study developed a new automated Bayesian Inference calibration platform running as an R package. A sensitivity analysis module and a Bayesian inference module determine the calibration parameters and uncertainties, respectively. The Meta-model module is developed to replace the building energy model for the Markov Chain Monte Carlo process to save computing time. The proposed platform is successfully demonstrated on a synthetic high-rise office building and a real high-rise residential building in a hot and arid climate. The relationship between the number of calibration parameters, calibration performance, and the accuracy of the Meta-model is further discussed. The developed calibration platform in this study proved to have clear advantages over the existing platforms, with the ability to reasonably estimate building energy performance in a short computing time.