Energy Efficiency Model Research Articles

Modeling and Analysis of Data and Coverage Energy Efficiency for Different Demographic Areas in 5G Networks

It is expected that fifth-generation (5G) mobile networks will need to accommodate vast data traffic volumes. The practical implementation of 5G networks is further challenged by the presence of versatile demographic areas differing in user density and expected transmission rates. Accommodating the increased traffic demands in such versatile demographic areas mandates the installation of different in size, number, and capacity 5G base stations (BSs), which will have an impact on the energy consumption of the 5G network. In this article, for the square kilometer area of the radio part of the 5G network, standardized network energy-efficiency (EE) metrics are presented. The EE analysis of the 5G network was performed for four different demographic areas (indoor hotspot, dense urban, urban, and rural) and two standardized data (bit/J) and coverage (m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> /J) EE metrics. To determine how the different 5G BSs deployment strategies affect the network's EE, five different 5G BSs installation and operation strategies have been analyzed for each demographic area. Interpolation functions expressing the impact of the data volume (DV) on the data and coverage EE metrics have been developed for each demographic area and network installation and operation scenario. The obtained results show that the DVs of the different demographic areas significantly impact the standardized EE metrics.

OPTIMAL IN THE NATIONAL ENERGY SECTOR DIRECTIONS OF FORMATION OF INFRASTRUCTURE COMPLEX

OPTIMAL IN THE NATIONAL ENERGY SECTOR DIRECTIONS OF FORMATION OF INFRASTRUCTURE COMPLEX

Energy efficient thermal comfort predictive control for household heat metering room

Heating accounts for about a quarter of the building’s energy consumption. In order to increase the value of green buildings, it is necessary to do some research on reducing heating energy consumption. The use of household heat metering in buildings can reduce the energy required for heating. In view of the regulation and control problems in energy consumption reduction and thermal comfort improvement, an energy efficient thermal comfort model predictive control (EETCMPC) method was proposed in the household thermal metering building. Firstly, the dynamic temperature change model of the heating room was analyzed and modeled. Then, the structure of EETCMPC system for household thermal metering room was presented, and the thermal comfort index based on Predicted Mean Vote (PMV) was introduced into the cost function, and the EETCMPC was designed. Finally, the performance of EETCMPC was analyzed and verified by means of simulation with Proportion Integration Differentiation (PID) and temperature reference tracking MPC (TRTMPC). Energy consumption of EETCMPC was reduced by approximately 4.4% compared with TRTMPC when the thermal comfort demand was met. Therefore, this method is valid and reliable for energy efficient thermal comfort control for household heat metering room and is significant for engineering application.

Problems and conditions for using renewable energy sources in the energy supply systems of the Far North

The article discusses the problems of energy supply systems and the applications of renewable sources in the Far North. The specifics of energy supply in the northern regions (the lack of a centralized power supply system, the presence of a large number of isolated autonomous power supply systems) is studied. The special conditions of the Far North regions, which are taken into account in the implementation of projects, are analyzed and the main ones are discussed. Disadvantages of RES-based plants are identified. Programs aimed to increase the efficiency of electricity consumption are proposed. Measures such as the use of frequency-controlled drives and energy efficient engines, the replacement of industrial equipment with more energy efficient models, the use of modern thermal insulation materials in the construction of buildings, should be taken. Examples of buildings in Far North settlements and wind turbines and SESs are provided.

A Building Energy-Saving Method of Small High-Rise Office Building Based on BIM Model

Building green energy-saving is an important area of current architectural application research. Artificial intelligence algorithm can effectively improve the design of building energy-saving digital, intelligent level. Therefore, this paper proposes the use of BIM model based on genetic algorithms to optimize the application of building energy conservation research. In this paper, the basic structure of genetic algorithm and advantages and disadvantages of the algorithm are described. According to the need of optimal design of building energy-saving integration, multiobjective optimization function is introduced and a multiobjective optimization genetic algorithm is established to improve the prediction effect of building energy efficiency model. Finally, results of analysis of some of the influencing factors and the simulation test of building energy-saving integrated optimization algorithm in small high-rise office building show that the improved genetic algorithm can effectively improve the effect of energy-saving integrated optimization and has good application prospects.

Analysis of energy efficiency in China’s export trade: A perspective based on the synergistic reduction of CO2 and SO[formula omitted

China has become the world’s largest energy consumer with its energy consumption growth accounts for three-quarters of the global growth. It is necessary to measure its energy efficiency reasonably. Previous studies mainly investigated energy use efficiency from the perspective of direct energy consumption without considering the energy consumption related to intermediate production, and did not eliminate the influence of random errors and environmental variables, resulting in deviations in energy efficiency measurement results. Combining a non-competitive input–output model and a three-stage data envelopment analysis, and based on the perspective of synergistic CO2 and SO2 emission reduction, this study constructs a new environmentally extended energy efficiency model to elucidate the true energy efficiency of China’s export trade. The results are as follows. (1) The implied energy efficiency of China’s export trade is generally on the rise under environmental constraints. Although its real efficiency in excluding external environmental factors has declined, its potential for improvement is still great. (2) Technological innovation and the expansion of export trade scale are conducive to the improvement of energy efficiency. Due to overcapacity, increasing industry capital investment will not greatly improve energy efficiency, but will increase the energy consumption and pollution emissions. (3) The average energy saving potential of China’s export trade is 8.91%, and the average emission reduction potential is 11.77%. (4) From 2002 to 2017, the average annual growth rate of implied energy efficiency in export trade was 8.9%, and technological progress was the main source of growth.

Analysis and Optimization of Grinding Performance of Vertical Roller Mill Based on Experimental Method

This work concentrates on the energy consumption and grinding energy efficiency of a laboratory vertical roller mill (VRM) under various operating parameters. For design of experiments (DOE), the response surface method (RSM) was employed with the VRM experiments to systematically investigate the influence of operating parameters on the energy consumption and grinding energy efficiency. The prediction models of energy consumption (Ecs) and grinding energy efficiency (η) were established respectively with the operating parameters (loading pressure, rotation speed and moisture content). Analysis of variance (ANOVA) was performed to obtain useful knowledge in designing operating parameters. Moreover, the multi-objective optimization design (MOD) method was conducted to seek out the optimal parameters of the VRM, and a set of optimal parameters was gained based on the desirability approach by Design-Expert. It is proved that the optimized prediction results match the experimental results well, which indicates this research offers a reliable guidance for reducing energy consumption and improving grinding energy efficiency.

Experimental investigation and annual performance mathematical-prediction on a novel LT-PV/T system using spiral-descent concentric copper tube heat exchanger as the condenser for large-scale application

The condensers of loop thermosyphon PV/T systems (LT-PV/T) are usually integrated inside water tanks, which may bring some challenges during combination use. This research innovatively proposed a concentric copper tube heat exchanger as the condenser, which is combined with a copper tube evaporator beneath the absorber. The gaseous working fluid flows in the inner tube and the cooling water flows in the outer tube. Since ordinary water pipes are used for water circulating between the outer tube and water tank, this LT-PV/T collector can be used individually or combined with other collectors flexibly. To access its' performance, researches have been conducted: (1) Designing and fabricating the system prototypes; (2) Investigating system performance with different volume-filling ratios (26.5%, 34.8%, 43.2%); (3) Investigating the influences of working fluid (water, ethanol and R134A). (4) Evaluating the systems’ performance with energy efficiency, exergy efficiency, and semi-empirical system efficiency models; (5) Conducting two case studies in South China (an individual collector & a 4 parallelly/serially-combined LT-PV/T collectors system). The system is first-of-its-kind and has obvious advantages in reliability, flexibility, space-saving and large-scale applications. The typical primary energy-saving efficiency of the LT-PV/T with R134a of 40% filling ratio can reach 78.0%, higher than the published LT-PV/T systems.

Optimization of energy-efficient open shop scheduling with an adaptive multi-objective differential evolution algorithm

There have been growing interests in the energy-efficient production scheduling recently because of the growing shortage of energy. Open shop scheduling problem (OSSP) is a kind of common but seldom concerned production scheduling problem. This paper focuses on energy-efficient OSSP (EOSSP), where the effect of setup operation on production efficiency and energy consumption is considered. A multi-objective energy-efficient model based on machine speed scaling mechanism is proposed. To handle this multi-objective problem, we propose an effective adaptive multi-objective differential evolution (AMODE) algorithm. The AMODE uses a new fitness evaluation mechanism (FEM) based on dynamic reference point and fuzzy correlation entropy analysis to assess the solutions in evolution population. It also uses an adaptive opposition-based learning (AOBL) to improve its local search ability. Taguchi method is utilized to obtain the best combination of critical parameters of the AMODE. The proposed mathematical model is validated with CPLEX, and a lexicographic method is used to determine the preferable solution. Experimental results show that both our proposed FEM and AOBL can improve the performance of AMODE. Extensive experiments reveal that the performance of AMODE is superior to the other three well-known algorithms in addressing the EOSSP.

Analysis of a linear programming‐based decision‐making model for microgrid energy management systems with renewable sources

With the increasing share of renewable energy sources in microgrids, systems enhancing the power flexibility at the demand side have become mandatory in microgrid architecture. Thus, the electrical energy storage system has been mostly integrated into microgrids although its capital and operating costs are very high. Hence, diversifying microgrid energy storage based on the end-energy usage can improve the reliability, operating cost, and power demand flexibility of the microgrid. The hereby study integrates an absorption chiller and electrical heat pump coupled to a heat storage into a renewable energy resources-based microgrid and develops its centralized energy management model for both off-grid and on-grid operation modes. The model considers the current energy level of energy storages in the microgrid, current, and forecasted information state to compute the vector-valued decision minimizing a realistic microgrid operating cost. The operating cost includes the energy purchasing/generation, heating/electrical storage, and penalty cost due to load shedding. Hence, such an objective function leads to maximizing the consumption of the generated renewable power, minimizing the energy storage and load shedding cost. The load shedding has been allowed to increase the flexibility in microgrid energy management. However, a high penalty has been imposed on each electrical, heating, or cooling load shedding decision. The decision vector components are thermal and electrical power flow between each energy source to loads and energy storage systems. The problem has been modeled as a Linear Programming with forecasted multi-parametric inputs at different prediction horizons. The effect of the charging/discharging rate and capacity of energy storages, coefficient of performance of absorption chiller and heat pump, prediction horizon, and energy level of storage devices provided to the myopic energy management model has been evaluated for better integration of renewable sources and thermal storage systems into microgrids. Suggestions have been made to improve the microgrid self-consumption, energy efficiency, and myopic decision-making model using the energy level of storage devices obtained from the full look-ahead optimization model. Highlights The optimization problem combining electricity, heating, and cooling is formulated The electrical and thermal storage charging/discharging rate significantly affect the power supplied by the renewable power source decision of the microgrid energy management model. Accurate energy storage level information into the myopic energy management model improves the model decisions. The absorption chiller coefficient of performance greatly impacts the cooling and electrical loads shedding.

Overview on agricultural potentials of biogas slurry (BGS): applications, challenges, and solutions.

The residual slurry obtained from the anaerobic digestion (AD) of biogas feed substrates such as livestock dung is known as BGS. BGS is a rich source of nutrients and bioactive compounds having an important role in establishing diverse microbial communities, accelerating nutrient use efficiency, and promoting overall soil and plant health management. However, challenges such as lower C/N transformation rates, ammonia volatilization, high pH, and bulkiness limit their extensive applications.Here we review the strategies of BGS valorization through microbial and organomineral amendments. Such cohesive approaches can serve dual purposes viz. green organic inputs for sustainable agriculture practices and value addition of biomass waste. The literature survey has been conducted to identify the knowledge gaps and critically analyze the latest technological interventions to upgrade the BGS for potential applications in agriculture fields.The major points are as follows: (1) Bio/nanotechnology-inspired approaches could serve as a constructive platform for integrating BGS with other organic materials to exploit microbial diversity dynamics through multi-substrate interactions. (2) Advancements in next-generation sequencing (NGS) pave an ideal pathway to study the complex microflora and translate the potential information into bioprospecting of BGS to ameliorate existing bio-fertilizer formulations. (3) Nanoparticles (NPs) have the potential to establish a link between syntrophic bacteria and methanogens through direct interspecies electron transfer and thereby contribute towards improved efficiency of AD. (4) Developments in techniques of nutrient recovery from the BGS facilities’ negative GHGs emissions and energy-efficient models for nitrogen removal. (5) Possibilities of formulating low-cost substrates for mass-multiplication of beneficial microbes, bioprospecting of such microbes to produce bioactive compounds of anti-phytopathogenic activities, and developing BGS-inspired biofertilizer formulations integrating NPs, microbial inoculants, and deoiled seed cakes have been examined.Graphical abstract

Energy Efficiency Modeling Using Whale Optimization Algorithm and Ensemble Model

machinery enterprises can benefit greatly from including energy efficiency models into their energy management and conservation efforts. Due to a lack of theoretical formulations, this paper integrates machining parameters and configuration parameters into energy efficiency models, with ML methods applied to increase generality. A three-year data set from a manufacturing facility serves as the basis for a comparison examination of two scenarios, with an emphasis on evaluating forecast precision, stability, and computing efficiency. To estimate future energy efficiency in Scenario 1, only cross-sectional data is utilized, completely discounting the wear and tear on spindle motors and cutting tools. In this study, we use five error measures to compare and contrast three classic ML algorithms: artificial neural networks, support vector regression, and Gaussian process regression. In Case 2, we build the a voting ensemble model in a more realistic setting, taking into account the dynamic characteristics of the aging spindle motor and tool wear. It is clear from the comparison that all of the Case 1 models experience performance erosion, but the proposed voting ensemble model is able to produce a sustainable increase in accuracy.

Green Building Energy Efficiency Evaluation and Optimization

This paper analyzes the influencing factors of energy saving benefits of green buildings, and summarizes six types of influencing factors of energy saving benefits, including lighting performance and ventilation performance. Through Bayesian network machine learning, the Bayesian network evaluation model of green building energy efficiency is constructed, and the comprehensive evaluation index system of green building energy efficiency is established. Combined with practical case analysis, the core influencing factors with high degree of influence are obtained by Bayesian network reasoning.

Urban energy efficiency assessment models from an AI and big data perspective: Tools for policy makers

• Defines the energy efficiency characteristics of AI and bigdata according to literature evidence and existing standards. • Defines a taxonomy for urban energy efficiency under the lens of AI and big data. • Examines urban energy efficiency assessment models from an AI and big data perspectives. • Approaches a unified assessment model for AI and big data processing within the urban space. • Proposes policies for policy makers and city managerswhere AI and big data solutions are applied. Although energy efficiency is quite a cliché term, it is a topic that attracts an increasing attention the last decade, especially in the context of cities and as a means to address emerging challenges like sustainability and climate change. Several models have been introduced to conceptualize and calculate the urban energy system, and to demonstrate the variants that calibrate the local energy efficiency. Nevertheless, cutting-edge technologies like blockchain, electrical -and even autonomous- vehicles, smart building systems, Artificial Intelligence (AI) and big data etc. are growing within cities and question the identified urban energy efficiency, since they demand enormous amounts of power. In this regard, policy makers are concerned of the emerging technologies’ energy efficiency and their impact on the urban energy system and they attempt to introduce corresponding standards for their development. This article focuses on the impact of AI and big data in city's energy efficiency. More specifically, a literature analysis is performed and returned a taxonomy of existing energy efficiency assessment models under the lens of AI and big data. Moreover, the definition of a unified assessment model for AI and big data energy efficiency is approached.

A Hybrid Model for Reliability Aware and Energy-Efficiency in Multicore Systems

Recently, Multicore systems use Dynamic Voltage/Frequency Scaling (DV/FS) technology to allow the cores to operate with various voltage and/or frequencies than other cores to save power and enhance the performance. In this paper, an effective and reliable hybrid model to reduce the energy and makespan in multicore systems is proposed. The proposed hybrid model enhances and integrates the greedy approach with dynamic programming to achieve optimal Voltage/Frequency (Vmin/F) levels. Then, the allocation process is applied based on the available workloads. The hybrid model consists of three stages. The first stage gets the optimum safe voltage while the second stage sets the level of energy efficiency, and finally, the third is the allocation stage. Experimental results on various benchmarks show that the proposed model can generate optimal solutions to save energy while minimizing the makespan penalty. Comparisons with other competitive algorithms show that the proposed model provides on average 48% improvements in energy-saving and achieves an 18% reduction in computation time while ensuring a high degree of system reliability.

Optimization Analysis of Full Life Cycle Energy Efficiency Multi-Objective Parallel Pump Sets

Aiming at the problem that the current single-objective control model of parallel pumping groups only focuses on the optimization of energy efficiency of operating conditions and operating costs, and cannot adjust the real-time working conditions of the pump group according to the comprehensive energy efficiency state of the pump group in the whole life cycle and adjust the pump group operation strategy accordingly, A multi-objective optimal control model for energy efficiency of a pump set is proposed, which can adjust the weight coefficients of three objective functions autonomously according to the current energy efficiency state of the pump set in the whole life cycle. In this way, the high energy efficiency of the parallel pump group in the low wear stage, that is, the low target deviation and specific energy consumption, and the high reliability in the high wear stage, that is, the lower impeller load improves the efficiency of the pump group in the whole life cycle and extend the service life of the pump group. Determine the multi-objective energy efficiency optimization control model of the pump group, use the main function linear and geometric weighting method, the ideal point value, and the distance deviation method to determine the objective function, and solve the multi-objective ideal point model with the help of LINGO, and obtain the optimal solution of the highest system total efficiency, the lowest pump group specific energy consumption and the highest system reliability. Pareto Frontier Comparison is used to study the transversality of the ideal point model solution set. Experimental results show that through the distribution of the model solution in the indicator space, the model can adjust the control strategy according to the real-time state of the pump group by adjusting the target weight combination.

Review of energy modelling, energy efficiency models improvement and carbon dioxide emissions mitigation options for the cement industry in South Africa

The cement sector in South Africa contributes approximately 1 % to the South African national greenhouse gas inventory. In line with the country’s GDP increasing which will cause population to increase, the demand for housing, public and private structural infrastructures continues to increase. It is assumed that the demand for cement will also increase proportionally as well as its associated CO2 emission. The purpose of this study is to firstly; review previous energy modelling strategies in South Africa and specifically in the cement industry if any in order to examine the alignment of the cement industry’s strategies with that of the South African government since becoming part of the Paris agreement signatories. Previous studies of energy efficiency, modelling, and GHG emissions were analyzed and reviewed for the understanding of the readiness of South Africa’s cement industry to tackle her energy and global warming issues. South Africa is in a position to tackle the energy efficiency and GHG emissions problem through redesigning and consolidated critical data collection will, driven by the government and responsible stakeholders. Secondly; if any previous strategies are in place but in isolation, to examine the potential for long-term energy efficiency strategies and CO2 mitigation options for the cement sector in South Africa; as these two elements have a distinguishable link. Cement production is an energy-intensive process that consists of combustion-and process-related emissions. This study will employ-two modelling frameworks to examine strategies for reducing cement sector energy demand and associated CO2 emission for the period 2015 to 2050 (35 years) with 5-year intervals parallel to the Paris agreement. After reviewing energy models, the study will employ the low emissions analysis platform (LEAP) to model possible energy saving methods in the combustion process and other forms of energy supply in the context of South African cement sector. The existing reviewed methods and results will be compared and the LEAP will provide a second set of comparable results in order to influence policy and inform decision-making. For process-related emissions in the sector, the study will develop an excel-based model to examine possible strategies for reducing CO2 emission in the sector. The results will be examined in terms of cumulative energy savings, GHG emissions, and marginal abatement cost of carbon.

Testing Solar-MAODV energy efficient model on various modulation techniques in wireless sensor and optical networks

Testing Solar-MAODV energy efficient model on various modulation techniques in wireless sensor and optical networks

Mobility Induced Multi-Hop LEACH Protocol in Heterogeneous Mobile Network

Heterogeneous mobile networks are a vital cue for Internet of Things (IoT) networks and their applications. Therefore, an optimal routing protocol is essential for the heterogeneous network to realize energy efficiency and robustness. However, designing an optimal routing protocol is more complex due to uneven energy depletion and dynamic event generation. The complexity further increases with the node mobility. In this context, the multi-hop routing approach with a controlled mobility pattern provides an energy efficient network by improving memory and storage utilization. This work proposes a new energy-efficient model for heterogeneous network, considering the effect of mobility and residual energy of nodes for cluster head selection in multi-hop-LEACH (M-LEACH). The performance of the proposed mobility induced multi-hop LEACH (Mob M-LEACH) protocol is further compared with existing baseline methods. The robustness of the network is evaluated considering static and dynamic environments for multiple network technologies. The simulation results show that the proposed method reduces network latency by 25% and enhances the throughput by 75%, improving energy consumption and network lifetime when compared with the state-of-art methods.

Energy Efficient UAV Flight Path Model for Cluster Head Selection in Next-Generation Wireless Sensor Networks.

Wireless sensor networks (WSNs) are one of the fundamental infrastructures for Internet of Things (IoTs) technology. Efficient energy consumption is one of the greatest challenges in WSNs because of its resource-constrained sensor nodes (SNs). Clustering techniques can significantly help resolve this issue and extend the network’s lifespan. In clustering, WSN is divided into various clusters, and a cluster head (CH) is selected in each cluster. The selection of appropriate CHs highly influences the clustering technique, and poor cluster structures lead toward the early death of WSNs. In this paper, we propose an energy-efficient clustering and cluster head selection technique for next-generation wireless sensor networks (NG-WSNs). The proposed clustering approach is based on the midpoint technique, considering residual energy and distance among nodes. It distributes the sensors uniformly creating balanced clusters, and uses multihop communication for distant CHs to the base station (BS). We consider a four-layer hierarchical network composed of SNs, CHs, unmanned aerial vehicle (UAV), and BS. The UAV brings the advantage of flexibility and mobility; it shortens the communication range of sensors, which leads to an extended lifetime. Finally, a simulated annealing algorithm is applied for the optimal trajectory of the UAV according to the ground sensor network. The experimental results show that the proposed approach outperforms with respect to energy efficiency and network lifetime when compared with state-of-the-art techniques from recent literature.