Energy Consumption Model Research Articles

Energy management of smart homes over fog-based IoT architecture

Existing research studies on home automation systems mostly conserve energy by modeling the occupancy of users within home. Some others apply statistical approaches on the survey data about usage of appliances. Consequently, these research works either reduce wastage of electricity through automation or achieve energy efficiency based on appliances’ usage estimations. However, they do not provide energy consumption modeling which is human comfort centric and also validated through practical implementation in real-world smart homes. We present a Markov-chain-based probabilistic model to obtain users stochastic activity patterns which are used to forecast the energy consumption in a smart home environment. These predictions are then leveraged by our novel comfort aware energy saving mechanism named as prediction- and feedback-based proactive energy conservation (PF-PEC) algorithm. The PF-PEC algorithm reduces the total energy consumption while ensuring standard human comfort. Furthermore, a fog-based Internet of Things (IoT) architecture is implemented and deployed in a smart home to efficiently incorporate the proposed algorithm in real-world scenarios. Experimental results show up to 36% energy conservation, marking substantial reduction in daily electricity usage.

Energy consumption model of coal grinding based on energy dissipation fractal dimension

Based on particle size distribution characteristics, this paper explored the energy dissipation law in the coal ultrafine grinding process. By studying the fractal dimension of coal particle size distribution under the multi-domain fractal rule, the truncated particle size was introduced to characterize the energy consumption changes of coarse, intermediate, and fine particles. Combined with macro-energy consumption laws and three-piecewise fractal dimensions, a micro-energy consumption theory considering lattice fracture and agglomeration of ultrafine particles was proposed. On this basis, the energy consumption fractal dimension DE was proposed, and its theoretical calculation value was 2.021. The energy consumption formula of coal ultrafine grinding was established, in which energy consumption was characterized by the DE th power of the added surface area of coal particles. The DE obtained by the specific surface area and the adsorption equilibrium constant of pores were 2.285 and 2.564, respectively, which confirmed the reliability of the above theoretical analysis.

Forecasting the progression of human civilization on the Kardashev Scale through 2060 with a machine learning approach

Energy has been propelling the development of human civilization for millennia. Humanity presently stands at Type 0.7276 on the Kardashev Scale, which was proposed to quantify the relationship between energy consumption and the development of civilizations. However, current predictions of human civilization remain underdeveloped and energy consumption models are oversimplified. In order to improve the precision of the prediction, we use machine learning models random forest and autoregressive integrated moving average to simulate and predict energy consumption on a global scale and the position of humanity on the Kardashev Scale through 2060. The result suggests that global energy consumption is expected to reach ~ 887 EJ in 2060, and humanity will become a Type 0.7449 civilization. Additionally, the potential energy segmentation changes before 2060 and the influence of the advent of nuclear fusion are discussed. We conclude that if energy strategies and technologies remain in the present course, it may take human civilization millennia to become a Type 1 civilization. The machine learning tool we develop significantly improves the previous projection of the Kardashev Scale, which is critical in the context of civilization development.

Conceptual design and analysis of a two-stage underwater glider for ultra-long voyage

An underwater glider (UG) designed for ultra-long voyage undoubtedly requires additional battery energy, which is generally realized by increasing the volume. However, a larger volume of UG results in a larger net buoyancy loss caused by seawater density variation and hull deformation, which will lead to a larger energy consumption. This study proposes the conceptual design of a novel two-stage UG (TUG) for enhancing the voyage range. To reduce the volume of UG, the first stage will be separated from the glider and discarded after its battery energy is drained. Mathematical models for the TUG are established, including the dynamic model, the energy consumption model and the performance evaluation models. To better evaluate this novel design, a multi-objective optimization model considering the energy utilization rate and voyage velocity is developed based on the proposed models, and implemented by surrogate model and non-dominated sorting genetic algorithm II. The results demonstrate that the designed TUG has a better motion performance than conventional UGs, and the two-stage design is more suitable for UGs with low hotel loads, which can theoretically improve the voyage range by 16.6% at a 0.25 W hotel load. This work can provide theoretical guidance for the optimization design of UGs applied for ultra-long voyage.

Development and application of life-cycle energy consumption and carbon footprint analysis model for passenger vehicles in China

China has become the global largest electric vehicle (EV) market, making the energy and climate issues of EV industry in terms of life cycle attract widespread attention. Based on the self-designed life-cycle analysis tool and up-to-date database, this study analyzes energy consumption and greenhouse gas (GHG) emissions of a typical passenger internal combustion engine vehicle (ICEV), and battery EV (BEV) and plug-in hybrid EV (PHEV) installed with different lithium-ion battery types, covering both fuel cycle and vehicle cycle. Owing to the battery installation, which shares nearly 30% of the GHG emissions in EV's vehicle cycle, the BEV and PHEV emit 13533–15445 kgCO2,eq/vehicle and 11572–12186 kgCO2,eq/vehicle, 65.6%–89.0% and 41.6%–49.1% higher than that of the ICEV, respectively. EV's fuel-cycle low emissions offset its surplus emissions in vehicle cycle. The EV generates 219.8–230.9 gCO2,eq/km in life cycle, achieving 18.3%–22.6% of reduction compared to the ICEV. Development of non-fossil power will highlight the advantages of EVs in energy conservation and GHG emission reduction, e.g. if the proportion of coal power decreased to 10%, BEV's life-cycle GHG emissions will reduce about 70% than current level. Policies should focus on both EVs development acceleration and the low-carbon automotive and battery industry creation.

Modeling of Energy Consumption and Print Time for FDM 3D Printing Using Multilayer Perceptron Network

Given the recognized advantages of additive manufacturing (AM) printing systems in comparison with conventional subtractive manufacturing systems, AM technology has become increasingly adopted in 3D manufacturing, with usage rates increasing dramatically. This strong growth has had a significant and direct impact not only on energy consumption but also on manufacturing time, which in turn has generated significant costs. As a result, this problem has attracted the attention of industry actors and the research community, and several studies have focused on predicting and reducing energy consumption and additive manufacturing time, which has become one of the main objectives of research in this field. However, there is no effective model yet for predicting and optimizing energy consumption and printing time in a fused deposition modeling (FDM) process while taking into account the correct part orientation that minimizes both of these costs. In this paper, a neural-network-based model has been proposed to solve this problem using experimental data from isovolumetrically shaped mechanical parts. The data will serve as the basis for proposing the appropriate model using a specific methodology based on five performance criteria with the following statistical values: R2-squared > 99%, explained variance > 99%, MAE < 0.99%, MSE < 0.02% and RMSE < 1.36%. These values show just how effective the proposed model will be in estimating energy consumption and FDM printing time, taking into account the best choice of part orientation for the lowest cost. This model provides a global understanding of the primary energy and time requirements for manufacturing while also improving the system’s cost efficiency. The results of this work can be extended and applied to other additive manufacturing processes in future work.

Electrification potential for heavy-duty vehicles in harsh climate conditions: A case study based technical feasibility assessment

Battery-electric heavy-duty vehicles are considered a potential solution to reduce emissions in freight transport and increase energy security. However, battery-electric trucks are not a one-fits-all solution due to current battery technology limitations and vast diversity in freight operations and vehicle categories. Although the literature available in electric truck feasibility provides an insightful overview of their potential, it does not fully cover the performance variations under adverse climate and technical conditions.This paper attempts to fill the gap in the literature by quantifying the impact of low temperatures, headwind, variable payload, and tail-lift use over different freight vehicles from a case study fleet in Iceland. NREL's FASTSim is the vehicle energy consumption model used for the analysis. The study also addresses on-route fast-charging, considering realistic non-linear charging rates and performance in low temperatures.The results showed that the range of the analyzed vehicles can experience a decrease of 41–47% under unfavorable conditions, compared to the estimated range under ideal conditions. Short-haul routes could be electrified even under the most challenging conditions, while regional routes might require some planification adjustments to reach 100% route electrification in tough conditions. On-route charging would be necessary to achieve full fleet electrification. In tough conditions, sprinter vans met 49% (85% with charging) of the analyzed routes, while trucks met 83% (90% with charging).The outcomes of this research support several UN Sustainable Development Goals (3, 7, 8, 11, 12, 13), and could be used to develop specific measures within the Icelandic 2020 Climate Action Plan. The methodology used is feasibly replicable for teams who wish to perform a similar analysis in different contexts.

Speed and energy optimization method for the inland all-electric ship in battery-swapping mode

Zero-emission battery-powered ship is considered the ideal technical solution to achieve emission reduction and energy conservation in inland shipping. However, due to long charging time, limited onboard space, and initial investment cost, the all-electric ship in battery charging mode may not be suitable for inland long-distance navigation. Therefore, the all-electric ship in battery-swapping mode is proposed, and a joint optimization method is designed to acquire optimal voyage scheduling and energy management. First, considering the environmental factors of water speed and water depth and cargo amount, the energy consumption model for the all-electric ship in battery-swapping mode is established by analyzing its energy transfer process. Second, a speed and energy optimization model is established to minimize the battery-swapping cost, considering the constraints of sailing speed, sailing time, and battery energy use. Third, differential evolution with neighborhood search (NSDE) is adopted to solve this nonlinear and complex problem. Finally, a case study for the ship in the Yangtze River is studied to verify the effectiveness of the proposed method. The results show that the operation cost could be minimized by this method effectively.

Novel method to predict the energy consumption of machined parts in the design phase to attain sustainability goals

To reduce CO2 emissions, besides the automotive industry, manufacturing industries are increasingly under pressure to optimize processes and procedures for energy efficiency. These optimizations mainly involve the production processes, where most energy demand occurs. Alternatively, when most of this energy demand can be determined during the product design phase, the product designer can make energy-efficient decisions in the design phase. Most product designers are unaware of their decisions’ significant impact on a product’s energy demand. Therefore, this paper presents a workflow and novel method for predicting the energy demand of parts of their machining operation during the design phase. For this purpose, 29 energy consumption models for machining processes are examined, and the data published in the literature are summarized. Four resulting comprehensive process maps are derived, which enable the prediction of a part’s energy consumption due to machining, specifically, the milling operation, based on the geometric features of the part. This was further verified on three machined parts. The workflow and methods presented in this paper are some of the first steps to facilitate conscious decisions already in the product design phase. The benefits of the method were demonstrated in a final survey: Both product designers and machine operators showed in this survey that their estimation of the energy consumption of the parts investigated differed by orders of magnitude.

Optimal Planning of Internet Data Centers Decarbonized by Hydrogen-Water-Based Energy Systems

Green and sustainable development of Internet data centers (IDCs) has attracted more attention in both academia and industry. Full utilization of renewable energy sources is widely known as the most effective way to supply electrical and thermal energy while reducing carbon emission. However, the integration of renewable energy into IDCs is still challenging due to the mismatch between uncertain renewable supply and time-varying demand requirements, and high requirement of operation reliability against IDC failures. Therefore, in this paper a hydrogen-water-based energy (HWBE) system is developed and its integrated planning-and-operation problem is formulated as a mixed-integer linear programming problem to determine the optimal capacity of energy facilities in the HWBE system with considering IDC operation reliability. A hybrid physics-based and data-driven method is developed to accurately capture the electrical and thermal energy consumption characteristics and their coupling which are the basis for the optimal planning of the HWBE system. Furthermore, a Benders decomposition-based reliability improvement algorithm is developed to enhance the operation reliability, which decomposes the problem into the planning problem with normal operation as the master problem and the operation problem with IDC failure as the subproblem. The reliability can be enhanced using the solution obtained by the master problem with the feasibility cut obtained from the subproblem. Numerical results show that the developed HWBE system is energy-efficient with low carbon emission, since the power usage efficiency of IDCs could be as low as 1.09 and the carbon emission could be reduced by 74.9% as compared by the electricity-driven IDC energy system. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper focuses on the integrated planning-and-operation optimization of an HWBE system for the application in IDCs. We improve the energy consumption model of IDCs based on a hybrid physics-based and data-driven method, which can describe the interaction between the dynamic thermal process and electricity consumption of IDCs. In this way, both the high accuracy of the physics-based model and the lower computational effort of the data-driven method could be simultaneously achieved in the energy consumption model. Furthermore, in practice, the optimal planning problem of IDCs is necessary to take into account the operation reliability against data center failures, since the capital expenditure of the backup energy devices is generally significant. This means that a trade-off between the solution accuracy of the planning problem and the computational complexity caused by the operation problem should be considered. Therefore, we develop a Benders decomposition-based reliability improvement algorithm to address the trade-off mentioned above. This technique can integrate the feasibility cut obtained from the operation problem with IDC failure into the planning problem, in order to improve the operation reliability against the supply-demand mismatching and IDC failures while reducing the capital cost, as compared to the system designed by the conventional redundancy standard. Numerical results show the effectiveness of the developed method which can make full use of renewable energy sources and support the green and sustainable development of IDCs.

Surrogate model of a HVAC system for PV self-consumption maximisation

In the last few years, energy efficiency has become a challenge. Not only mitigating environmental impact but reducing energy waste can lead to financial advantages. Buildings play an important role in this: they are among the biggest consumers. So, finding manners to reduce energy consumption is a way to minimise energy waste, and a technique for that is creating Demand Response (DR) strategies. This paper proposes a novel way to decrease computational effort of simulating the behaviour of a building using surrogate models based on active learning. Before going straight to the problem of a building, which is complex and computationally costly, the paper proposes the approach of active learning to a smaller problem: with reduced simulations, regress the curve of voltage versus current of a thermo-resistor. Then, the paper implements a surrogate model of energy consumption of a building. The goal is to be able to learn the consumption pattern based on a limited number of simulations. The result given by the surrogate can be used to set the reference temperature, maximising the PV self-consumption, and reducing energy usage from the grid. Thanks to the surrogate, the total time spent to map all possible consumption scenarios is reduced around 7 times.

Optimizing fuel economy and durability of hybrid fuel cell electric vehicles using deep reinforcement learning-based energy management systems

An effective energy management strategy (EMS) is crucial for the reliable operation of fuel cell hybrid electric vehicles (FCHEVs). This study proposes a power distribution optimization strategy for FCHEVs that leverages deep reinforcement learning (DRL) and Pontryagin's minimum principle (PMP). The DRL algorithm effectively balances fuel economy, battery durability, and fuel cell durability objectives. The degradation mechanisms of battery and fuel cell under extreme working conditions are considered in the PMP optimization. A comprehensive evaluation framework is established with degradation and energy consumption models to serve as a reward for deep reinforcement learning to balance fuel economy and power sources' lifetime. Simulation results show that the proposed EMS framework reduces FC degradation by 18.4% and battery degradation by 71.1% compared to traditional PMP-based EMS under the NEDC driving condition. Hardware-in-the-loop (HIL) testing demonstrates that the proposed EMS framework has the potential for real-time application, with an average relative error between experiment and simulation of approximately 0.0203. This research highlights the significance of the proposed EMS framework in ensuring the reliable operation of FCHEVs with enhanced performance and reduced cost.

Energy Management of Sowing Unit for Extended-Range Electric Tractor Based on Improved CD-CS Fuzzy Rules

In order to ensure the continuity and endurance mileage requirements during sowing operations, it is necessary to establish accurate modeling for the working condition of the electric tractor sowing unit by adopting a reasonable energy management strategy and realizing accurate energy prediction. The existing electric tractor sowing unit battery energy management strategy is not optimal since it is mostly based on extensive rules. In this paper, according to the requirements of the sowing conditions, a precise model of electric energy consumption in the sowing cycle was established and an energy management strategy of sowing unit of extended-range electric tractor with power CD-CS was proposed. Fuzzy control rules of the dynamic SOC correction factor were established in the battery maintenance stage, and the NSGA-II algorithm was used to optimize the fuzzy control rules to optimize the battery charging and discharging efficiency. A hardware-in-the-loop simulation test platform was built, and the proposed CD-CS strategy was compared with the fuzzy improvement strategy. The simulation results show that the proposed fuzzy improvement strategy extended the battery life of the power consumption stage by 2131.9 s, which is a significant improvement. The field practical results showed that the SOC decreased by 7.21% and the simulation by 4.94% in terms of power consumption in a cycle. The power consumption variance was within a reasonable range, which further verifies the feasibility of the strategy.

A REVIEW ON REAL VEHICLE USAGE MODELLING OF DRIVERLESS MULTIPURPOSE VEHICLES IN VEHICLE ROUTING PROBLEMS

AbstractReal vehicle usage rarely matches the predictions made during early phases of vehicle development and sales processes at commercial road vehicle manufacturers. The automotive industry needs multidisciplinary vehicle design methods to predict real-world vehicle operations by considering the vehicle level and the transport system level simultaneously, in a more holistic approach. The aim of this study was to analyse how realistic vehicle usage of driverless multipurpose vehicles can be modelled in Vehicle Routing Problems (VRPs) by conducting a systematic literature review. We found that real vehicle usage modelling of driverless multipurpose vehicles in VRPs mainly depended on the following elements: VRP variant, energy consumption model, energy consumption rate class, number of vehicle-specific design variables and transport system-level factors. Furthermore, we identified in the literature five classes of energy consumption rate edge behaviour in VRPs. These findings can support decision-making in the modelling process to select the most suitable combination of elements, and their level of detail for the overall modelling aim and purpose.

Comprehensive Energy Modeling Framework for Multi-Powertrain Bus Transit Systems

With the increasing emphasis on sustainable transportation solutions, there is an urgent need for a system that can accurately assess vehicle-specific energy consumption in real-time. However, the current modeling frameworks have a significant gap in that they are not demand- and congestion-responsive, nor are they capable of modeling the system-wide energy consumption of bus fleets at the vehicle-level, encompassing all three types of powertrains—conventional, hybrid, and electric. To address this gap, we present a comprehensive modeling framework that takes into account passenger loading, considers the dynamics of the system including acceleration, and is capable of predicting the real-time energy usage of each bus in a transit network. Using the Pioneer Valley Transit Authority system as a case study, we calibrated existing powertrain-specific models for conventional diesel, hybrid, and battery electric buses. Our demand- and congestion-responsive energy consumption modeling framework fills this critical gap and offers significant potential for future sustainable transportation planning. Relying on detailed trajectories computed from one month of bus location data, along with passenger counts and road grade, our framework is a novel contribution in that it provides spatiotemporal energy predictions for an entire bus system. As it is readily calibrated with potential improvements in accuracy given more data, this framework can be applied by transit agencies for the purposes of real-time energy consumption monitoring, fleet electrification planning, equity analyses, and carbon emission reduction strategies.

Skyline-Enhanced Deep Reinforcement Learning Approach for Energy-Efficient and QoS-Guaranteed Multi-Cloud Service Composition

Cloud computing has experienced rapid growth in recent years and has become a critical computing paradigm. Combining multiple cloud services to satisfy complex user requirements has become a research hotspot in cloud computing. Service composition in multi-cloud environments is characterized by high energy consumption, which brings attention to the importance of energy consumption in cross-cloud service composition. Nonetheless, prior research has mainly focused on finding a service composition that maximizes the quality of service (QoS) and overlooks the energy consumption generated during service invocation. Additionally, the dynamic nature of multi-cloud environments challenges the adaptability and scalability of cloud service composition methods. Therefore, we propose the skyline-enhanced deep reinforcement learning approach (SkyDRL) to address these challenges. Our approach defines an energy consumption model for cloud service composition in multi-cloud environments. The branch and bound skyline algorithm is leveraged to reduce the search space and training time. Additionally, we enhance the basic deep Q-network (DQN) algorithm by incorporating double DQN to address the overestimation problem, incorporating Dueling Network and Prioritized Experience Replay to speed up training and improve stability. We evaluate our proposed method using comparative experiments with existing methods. Our results demonstrate that our approach effectively reduces energy consumption in cloud service composition while maintaining good adaptability and scalability in service composition problems. According to the experimental results, our approach outperforms the existing approaches by demonstrating energy savings ranging from 8% to 35%.

Enhancement method of series hybrid ship energy efficiency for speed and energy collaborative optimization

AbstractHybrid ships have recently garnered increasing attention as a promising solution for energy shortages and environmental pollution. However, changes in the navigation environment can greatly affect the energy efficiency of these vessels. Therefore, improving the energy efficiency of hybrid ships continues to be a key issue. Our study examined the energy efficiency of a diesel‐electric series hybrid ship operating on an inland river, and a method to optimise series hybrid ship energy efficiency was proposed. The energy consumption model, power demand model, and decision function with minimum fuel consumption as the evaluation criteria were established according to the ship's characteristics and hybrid system. By analysing the data collected from an actual ship, the operating conditions of the navigation environment were identified using the k‐means algorithm, and the total distance travelled by the ship was divided into several sub‐trajectories. Afterwards, the optimised speed of the ship and the optimised output power of each power source were determined using the grasshopper optimization algorithm. The ship's energy efficiency levels before and after using the energy efficiency enhancement method were compared and analysed, and the effects of this optimization method on the ship's energy efficiency in several typical voyages were analysed. Our findings demonstrated that this optimization method can distribute the output of the power source in a better way, thereby optimising the speed of the vessel and maintaining stable sailing. More importantly, the proposed method can reduce fuel consumption by 17.04%.

UAV-Assisted Online Machine Learning Over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach

We investigate training machine learning (ML) models across a set of geo-distributed, resource-constrained clusters of devices through unmanned aerial vehicles (UAV) swarms. The presence of time-varying data heterogeneity and computational resource inadequacy among device clusters motivate four key parts of our methodology: (i) stratified UAV swarms of leader, worker, and coordinator UAVs, (ii) hierarchical nested personalized federated learning ( HN-PFL), a distributed ML framework for personalized model training across the worker-leader-core network hierarchy, (iii) cooperative UAV resource pooling to address computational inadequacy of devices by conducting model training among the UAV swarms, and (iv) model/concept drift to model time-varying data distributions. In doing so, we consider both micro (i.e., UAV-level) and macro (i.e., swarm-level) system design. At the micro-level, we propose network-aware HN-PFL, where we distributively orchestrate UAVs inside swarms to optimize energy consumption and ML model performance with performance guarantees. At the macro-level, we focus on swarm trajectory and learning duration design, which we formulate as a sequential decision making problem tackled via deep reinforcement learning. Our simulations demonstrate the improvements achieved by our methodology in terms of ML performance, network resource savings, and swarm trajectory efficiency.

Carbon Emission Effects of Land Use in Chaobai River Region of Beijing–Tianjin–Hebei, China

Beijing–Tianjin–Hebei, the main economic area in northern China, has seen significant changes in its regional economic and physical landscape as a result of the coordinated development strategy. Assessing the link between land use and land cover (LULC) change and carbon emissions in the Chaobai River region, which represents the growth of the Beijing–Tianjin–Hebei urban agglomeration, is crucial to achieve coordinated low-carbon development in this area. This study uses statistics from statistical yearbooks of Chinese provinces and cities along with land use change data to analyze the relationship between land use changes and carbon emissions in the Chaobai River region from 2001 to 2017 using dynamic land use attitudes and land use transfer matrices, combined with carbon emission factors based on the IPCC inventory method and carbon emission models for energy consumption. In addition, this study makes use of the LMDI model and geographical detectors to identify and assess the factors that influence changes in land use carbon emissions and the driving forces behind the regional differentiation of land use changes. The results show that: (1) The Chaobai River region’s predominant land use classes during the past 17 years have been agricultural land and construction land. In addition to the decrease in cropland and the increase in urban land, the land use patterns of other land classes also changed to a certain extent. (2) Carbon emissions from land use showed an increasing trend, from 6.1 × 106 tons in 2001 to 1.1 × 107 tons in 2017. (3) Carbon emission intensity, economic development level, land use efficiency, and construction land scale have a certain regularity in the evolution of carbon emissions, and economic development level has become the most important driving factor controlling the growth of land use carbon emissions. (4) Driving factors in different periods have different degrees of influence on land use change, among which socio-economic factors such as population density and GDP have the strongest explanatory power. In addition, the interactions of each factor mainly present a double factor enhancement. In the future, the Chaobai River region should be based on the coordinated development strategy and take the “double carbon” target as its guiding principle to promote the innovation of the regional development system and further achieve the optimization of the regional land use patterns.

Electric Heating System of Residences without Central Heating: Power Supply Optimization

For conservation and environmental purposes, three electricity consumption models, power grid, power grid + PV and power grid + PV + battery, are proposed in this paper, the optimization of the electric heating energy consumption model is carried out with the objective of the highest annual net return of the system. The results showed that for the single-layer residence with a housing area of 100m2, the use of the power grid + photovoltaic system has the highest annual net income, as well as good economic and environmental benefits.