Energy Efficiency Metrics Research Articles

Evaluation of Primary User Power Impact for Joint Optimization of Energy Efficiency in Cognitive Radio Networks

Energy efficiency (EE) is of great concern in cognitive radio networks since the throughput and energy consumption of secondary users (SUs) vary with the sensing time. However, the conditions of the detection probability and false alarm probability should be respected to better protect primary users (PUs) and to improve the sensing performance of SUs. Additionally, the PUs’ minimum averaged power provision should also be regarded as a key problem of interactive linking to SUs. Therefore, an integrated design between the PU and SUs is desired for the coordination of the whole cognitive radio system, especially regarding the satisfaction of EE and performance metrics. This study formulates sensing constraints in a unified way and calculates the minimum SNR of SUs, based on which the essential PU power provision is computed. Furthermore, EE is proved as a decreasing function with the PU’s active ratio, where the maximum EE is obtained corresponding to the minimum QoS requirements of the sensing process. Hence, a bisection-based method is proposed to maximize EE, which is considered as a concave function of SUs’ sensing time and has only one unique optimum. EE’s optimization was analyzed under different fusion rules for diverse SNR conditions. The optimum was also studied with sensing performance targets for various cases of PU power provision.

The Contribution of Bottom-Up Energy Models to Support Policy Design of Electricity End-Use Efficiency for Residential Buildings and the Residential Sector: A Systematic Review

Bottom-up energy models are considered essential tools to support policy design of electricity end-use efficiency. However, in the literature, no study analyzes their contribution to support policy design of electricity end-use efficiency, the modeling techniques used to build them, and the policy instruments supported by them. This systematic review fills that gap by identifying the current capability of bottom-up energy models to support specific policy instruments. In the research, we review 192 publications from January 2015 to June 2020 to finally select 20 for further examination. The articles are analyzed quantitatively in terms of techniques, model characteristics, and applied policies. The findings of the study reveal that: (1) bottom-up energy models contribute to the support of policy design of electricity end-use efficiency with the application of specific best practices (2) bottom-up energy models do not provide a portfolio of analytical methods which constraint their capability to support policy design (3) bottom-up energy models for residential buildings have limited policy support and (4) bottom-up energy models’ design reveals a lack of inclusion of key energy efficiency metrics to support decision-making. This study’s findings can help researchers and energy modelers address these limitations and create new models following best practices.

Coupled Calculations of Data Center Cooling and Power Distribution Systems

Abstract Physics-based modeling aids in designing efficient data center power and cooling systems. These systems have traditionally been modeled independently under the assumption that the inherent coupling of effects between the systems has negligible impact. This study tests the assumption through uncertainty quantification of models for a typical 300 kW data center supplied through either an alternating current (AC)-based or direct current (DC)-based power distribution system. A novel calculation scheme is introduced that couples the calculations of these two systems to estimate the resultant impact on predicted power usage effectiveness (PUE), computer room air conditioning (CRAC) return temperature, total system power requirement, and system power loss values. A two-sample z-test for comparing means is used to test for statistical significance with 95% confidence. The power distribution component efficiencies are calibrated to available published and experimental data. The predictions for a typical data center with an AC-based system suggest that the coupling of system calculations results in statistically significant differences for the cooling system PUE, the overall PUE, the CRAC return air temperature, and total electrical losses. However, none of the tested metrics are statistically significant for a DC-based system. The predictions also suggest that a DC-based system provides statistically significant lower overall PUE and electrical losses compared to the AC-based system, but only when coupled calculations are used. These results indicate that the coupled calculations impact predicted general energy efficiency metrics and enable statistically significant conclusions when comparing different data center cooling and power distribution strategies.

Effect of in‐row cooling units numbers/locations on thermal and energy management of data centers servers

The current research investigates the effects of numbers and arrangements of the cooling units on the thermal and energy management of in-row cooling architectures of data centers. Three different configurations with different numbers of cooling units; namely one rack between each two cooling units (Configuration A), two racks between each two cooling units (Configuration B), and three racks between each two cooling units (Configuration C), are numerically investigated. A scaled physical module of data center rack is experimentally constructed and tested for model validation. Two thermal evaluation indices (Supply Heat Index SHI and Index of Mixing IOM) and two energy efficiency metrics (Beta Index β and Energy Utilization Index ηr) are used to investigate, evaluate, and compare the thermal management and energy efficiency of the three configurations. The results show that the three configurations have good thermal homogeneity and the same good values of the thermal and energy efficiency metrics at the intermediate racks. However, problems of hot air circulations with hot spots and bad performances indices were noticed at terminal racks of the racks array. The energy utilization index is in the optimal range (about 2) for the intermediate rack and drop to 1.5 for the terminal racks. Economic study shows that configuration B has the minimum capital cost and configuration A has the minimum needed white area with a capital cost very close to that of B. Accordingly, configuration B is economically recommended, and configuration A is recommended for limited white area.

Improving Energy Efficiency on SDN Control-Plane Using Multi-Core Controllers

Software-defined networks have become more common in data centers. The programmability of these networks is a great feature that allows innovation to be deployed fast, following the increasing number of new applications. This growth comes with a cost of more processing power and energy consumption. Many researchers have tackled this issue using existing routing techniques to dynamically adjust the network forwarding plane to save energy. On the control-plane, researchers have found algorithms for positioning the controller in a way to reduce the number of used links, thus reducing energy. These strategies reduce energy consumption at the expense of processing power of the controllers. This paper proposes a novel approach to energy efficiency focused on the network’s control-plane, which is complementary to the many already existing data-plane solutions. It takes advantage of the parallel processing capabilities of modern off-the-shelf multicore processors to split the many tasks of the controller among the cores. By dividing the tasks among homogeneous cores, one can lower the frequency of operations, lowering the overall energy consumption while keeping the same quality of service level. We show that a multicore controller can use an off-the-shelf multicore processor to save energy while keeping the level of service. We performed experiments based on standard network measures, namely latency and throughput, and standard energy efficiency metrics for data centers such as the Communication Network Energy Efficiency (CNEE) metric. Higher energy efficiency is achieved by a parallel implementation of the controller and lowering each core’s frequency of operation. In our experiments, we achieved a drop of 28% on processor energy use for a constant throughput scenario when comparing with the single-core approach.

An Energy-Efficient and Reliable Opportunistic Routing for Wireless Sensor Networks

Introduction: Routing protocols in Wireless Sensor Networks (WSN) are of major concern because of the important factors like energy efficiency and reliability. Opportunistic Routing (OR) is the simplest and reliable routing technique for ad-hoc networks and WSN. The OR protocol guarantees data delivery in the network. As WSN need energy-efficient routing, therefore, energy- efficient OR protocols gained popularity in the last three years. Objectives: Opportunistic routing improves the performance of the network by utilizing the broadcasting abilities of wireless channels. In this paper, a new energy-efficient and reliable routing protocol has been proposed that is based on the opportunistic routing technique. Methods: The proposed protocol is based on the energy-efficient routing metric. The proposed OR protocol prioritizes among the candidate forwarders on the basis of energy-efficient routing metric and this will lead to the achievement of improved network lifetime. Results: The end-to-end delay is reduced because of the selection of candidate forwarders from the neighbor list of each node. The proposed protocol uses selective acknowledgment mechanisms. The simulation results of the proposed protocol depict the improved performance as compared to other recent OR protocols. Conclusion: The proposed protocol can directly be used with existing WSN applications. Further improvements can be made by utilizing more parameters like distance and the quality of transmission.

Are we moving toward an energy-efficient low-carbon economy? An input\u2013output LMDI decomposition of CO_{2} emissions for Spain and the EU28

Spain is on a path toward the decarbonization of the economy. This is mainly due to structural changes in the economy, where less energy-intensive sectors are gaining more relevance, and due to a higher use of less carbon-intensive primary energy products. This decarbonization trend is in fact more accentuated than that observed in the EU28, but there is still much to be done in order to reverse the huge increases in emissions that occurred in Spain prior to the 2007 crisis. The technical energy efficiency is improving in the Spanish economy at a higher rate than in the EU28, although all these gains are offset by the losses that the country suffers due to the inefficient use of the energy equipment. There is an installed energy infrastructure (in the energy-consumer side) in the Spanish economy that is not working at its maximum rated capacity, but which has very high fixed energy costs that reduce the observed energy efficiency and puts at risk the achievement of the emissions and energy consumption targets set by the European institutions. We arrive to these findings by developing a hybrid decomposition approach called input–output logarithmic mean Divisia index (IO-LMDI) decomposition method. With this methodological approach, we can provide an allocation diagram scheme for assigning the responsibility of primary energy requirements and carbon-dioxide emissions to the end-use sectors, including both economic and non-productive sectors. In addition, we analyze more potential influencing factors than those typically examined, we proceed in a way that reconciles energy intensity and energy efficiency metrics, and we are able to distinguish between technical and observed end-use energy efficiency taking into account potential rebound effects and other factors.

Spiking Neural Network Discovers Energy-Efficient Hexapod Motion in Deep Reinforcement Learning

In Deep Reinforcement Learning (DRL) for robotics application, it is important to find energy-efficient motions. For this purpose, a standard method is to set an action penalty in the reward to find the optimal motion considering the energy expenditure. This method is widely used for the simplicity of implementation. However, since the reward is a linear sum, if the penalty is too large, the system will fall into local minima and no moving solution can be obtained. In contrast, if the penalty is too small, the effect may not be sufficient. Therefore, it is necessary to adjust the amount of the penalty so that the agent always moves dynamically, and the energy-saving effect is sufficient. Nevertheless, since adjusting the hyperparameters is computationally expensive, we need a learning method that is robust to the penalty setting problem. We investigated on the Spiking Neural Network (SNN), which has been attracting attention for its computational efficiency and neuromorphic architecture. We conducted gait experiments using a hexapod agent while varying the energy penalty settings in the simulation environment. By applying SNN to the conventional state-of-the-art DRL algorithms, we examined whether the agent could explore for an optimal gait with a larger penalty variation and obtain an energy-efficient gait verified with Cost of Transport (CoT), a metric of energy efficiency for gait. Soft Actor-Critic (SAC)+SNN resulted in a CoT of 1.64, Twin Delayed Deep Deterministic policy gradient (TD3)+SNN resulted in a CoT of 2.21, and Deep Deterministic policy gradient (DDPG)+SNN resulted in a CoT of 2.08 (1.91 for normal SAC, 2.38 for TD3, and 2.40 for DDPG). DRL combined with SNN succeeded in learning more energy efficient gait with lower CoT.

Are We Moving Towards an Energy-efficient Low-carbon Economy? An Input-output LMDI Decomposition of CO2 Emissions for Spain and the EU28

Spain is on a path towards the decarbonization of the economy. This is mainly due to structural changes in the economy, where less energy-intensive sectors are gaining more relevance, and due to a higher use of less carbon-intensive primary energy products. This decarbonization trend is in fact more accentuated than that observed in the EU28, but there is still much to be done in order to reverse the huge increases in emissions that occurred in Spain prior to the 2007 crisis. The technical energy efficiency is improving in the Spanish economy at a higher rate than in the EU28, although all these gains are offset by the losses that the country suffers due to the inefficient use of the energy equipment. There is an installed energy infrastructure (in the energy-consumer side) in the Spanish economy that is not working at its maximum rated capacity, but which has very high fixed energy costs that reduce the observed energy efficiency and puts at risk the achievement of the emissions and energy consumption targets set by the European institutions. We arrive to these findings by developing a hybrid decomposition approach called «input-output logarithmic mean Divisia index» (IO-LMDI) decomposition method. With this methodological approach, we can provide an allocation diagram scheme for assigning the responsibility of primary energy requirements and carbon-dioxide emissions to the end-use sectors, including both economic and non-productive sectors. In addition, we analyze more potential influencing factors than those typically examined, we proceed in a way that reconciles energy intensity and energy efficiency metrics, and we are able to distinguish between technical and observed end-use energy efficiency taking into account potential rebound effects and other factors.

Energy‐Spectral Efficiency Trade‐Offs in Full‐Duplex MU‐MIMO Cloud‐RANs with SWIPT

The present paper investigates the trade‐offs between the energy efficiency (EE) and spectral efficiency (SE) in the full‐duplex (FD) multiuser multi‐input multioutput (MU‐MIMO) cloud radio access networks (CRANs) with simultaneous wireless information and power transfer (SWIPT). In the considered network, the central unit (CU) intends to concurrently not only transfer both energy and information toward downlink (DL) users using power splitting structures but also receive signals from uplink (UL) users. This communication is executed via FD radio units (RUs) which are distributed nearby users and connected to the CU through limited capacity fronthaul (FH) links. In order to unveil interesting trade‐offs between the EE and SE metrics, we first introduce three conventional single‐objective optimization problems (SOOPs) including (i) system sum rate maximization, (ii) total power minimization, and (iii) fractional energy efficiency maximization. Then, by making use of the multiobjective optimization (MOO) framework, the MOO problem (MOOP) with the objective vector of the achievable rate and power consumption is addressed. All considered problems are nonconvex with respect to designing variables comprising precoding matrices, compression matrices, and DL power splitting factors; thus, it is extremely intractable to solve these problems directly. To overcome these issues, we develop iterative algorithms by utilizing the sequential convex approximation (SCA) approach for the first two SOO problems and the SCA‐based Dinkelbach method for the fractional EE problem. Regarding the MOOP, we first rewrite it as an SOOP by applying the modified weighted Tchebycheff method and, then, propose the iterative algorithm‐based SCA to find its optimal Pareto set. Various numerical simulations are conducted to study the system performance and appealing EE‐SE trade‐offs in the considered system.

Energy-Efficient Spatially-Correlated Hardware Impaired Massive MIMO FD Relaying

Most of the existing massive multi-input-multi-output (mMIMO) relaying works assume ideal transceiver hardware and spatially uncorrelated channels. A practical mMIMO relay, however, due to limited antenna spacing and the use of cost-effective equipment, usually experiences spatially-correlated channels and hardware impairments, respectively. We consider a hardware-impaired two-way full-duplex (FD) spatially-correlated mMIMO relaying with multiple MIMO FD user-pairs, and derive a closed-form spectral efficiency (SE) expression which is valid for a practical number of finite relay antennas. We use this expression to develop a quadratic transformation approach to optimize the non-convex global energy efficiency (GEE) and weighted sum energy efficiency (WSEE) metrics, which are fractional functions of optimization variables. The proposed approach first converts the fractional problem into its equivalent fraction-free counterpart, and then uses novel transformations to make the problem concave, and solve it using an iterative algorithm. We use this optimization to i) investigate the impact of spatial correlation and hardware impairments on GEE; ii) decide hardware impairment values for a tolerable reduction in GEE; and iii) investigate the impact of weights in WSEE on the users energy efficiency.

Optimization of energy absorption performance of polymer honeycombs by density gradation

Density gradation has been analytically and experimentally proven to enhance the load-bearing and energy absorption efficiency of cellular solids. This paper focuses on the analytical optimization (by virtual experiments) of polymeric honeycomb structures made from thermoplastic polyurethane to achieve density-graded structures with combined desired mechanical properties. The global stress-strain curves of single-density honeycomb structures are used as input to an analytical model that enables the characterization of the constitutive response of density-graded hexagonal honeycombs with discrete and continuous gradations and for various gradients. The stress-strain outputs are used to calculate the specific energy absorption, efficiency, and ideality metrics for all density-graded structures. The analytical results are shown to be in good agreement with previous experimental measurements. Our findings suggest that the choice of an optimal gradient depends on the specific application and design criteria. For example, graded structures wherein low density layers are dominant are shown to outperform high density uniform honeycombs in terms of specific energy absorption capacity while possessing higher strength compared with low density uniform structures.

Optimizing Weighted-Sum Energy Efficiency in Downlink and Uplink NOMA Systems

In this paper, weighted sum energy efficiency (WSEE) in uplink and downlink of a multi-user non-orthogonal multiple access (NOMA) system is considered. we adopt a more realistic power consumption model where signal processing power is modeled as a linear function of transmit power. Rather than the well-known network-centric global energy efficiency (GEE) metric, which is a pseudo-concave (PC) function, the weighted sum energy efficiency metric is considered, which is not PC in general. To find the optimum user power allocation Dinkelbach-like algorithm is adopted, by which each individual fractional EE function is converted to a parametric function where under some conditions on the weights falls into a class of convex optimization problems and it is solved in the dual domain. The dual variables are updated using a sub-gradient and cutting plane-based algorithm, which here ellipsoid method is used. Since the optimum solution restricts user weights, a low complexity suboptimum algorithm that does not consider any condition on user's weights is proposed. The problem is non-convex in general; hence, epigraph form followed by successive convex approximation (SCA) is used to deal with that problem. Results demonstrate that with the user-oriented metric, one can provide different priorities to users, and by choosing proper weights entail fairness among users.

Statistical Energy Efficiency Characterization for Wireless Transmission Over Fading Channels

Future Internet of Things (IoT) applications demand highly energy-efficient wireless transmission solutions. In this article, we characterize the energy efficiency (EE) of wireless transmission over fading channels from individual transmission session perspective. Specifically, we apply a statistical EE metric to analyze and optimize ideal continuous rate adaptation (CRA) and continuous power adaptation (CPA) transmission schemes. The resulting statistical characterization establishes an EE performance limit for data transmissions over fading channels and facilitates a direct tradeoff analysis between maximal achievable EE and instantaneous latency requirement.

Energy-Efficient Massive MIMO Multi-Relay NOMA Systems With CSI Errors

We consider a massive multi-input multi-output (mMIMO) system where in a base station (BS) serves its users via multiple relays by employing non-orthogonal multiple access (NOMA). We practically model this system by considering channel estimation errors both at the BS and at the users, which consequently perform imperfect successive interference cancellation. We consider these two artifacts, and derive a lower bound on the spectral efficiency (SE) of this multi-relay NOMA system, which is valid for arbitrary number of BS antennas. We then jointly allocate the BS and relay powers to optimize non-convex global energy efficiency (GEE) and the weighted sum energy efficiency (WSEE) metrics, which are fractional functions of sum-of-products/ratios of optimization variables. These two optimizations require extension of an existing fractional programming framework, which we do by proposing two novel transformations. We analytically prove the monotonic convergence of the transformed problems to a stationary point of their respective original counterparts. We numerically show that i) a multi-relay mMIMO NOMA system outperforms its orthogonal counterpart only with accurate channel information; and ii) the proposed GEE and WSEE algorithms significantly improve the energy efficiency by avoiding additional power use, after attaining optimal value.

Energy Efficiency Maximization of Relay Aerial Robotic Networks

This paper proposes a new method of maximising the energy efficiency of Aerial Robotic (AR) networks in order to prolong AR battery life. An AR network is composed of an unmanned aircraft (drone) fitted with a wireless relay network. The AR network provides a connection server between two spread ground nodes. It is able to hover at any point of its flight path, which enables the AR network to function as a Mobile Base Station (MBS). An MBS can increase the range and flexibility of a communication network, but this ability increases the AR’s energy consumption, which in turn increases drone battery drain. Therefore, it is essential to select the optimal MBS location so that the drone battery drain is minimised. In this paper we propose a method for evaluating the MBS’s energy consumption at any point on the drone’s flight path and thus select the hovering location of minimum energy consumption as the optimal MBS location. The proposed method optimises the source and relay power allocations on the drone’s flight path. The power allocations are then combined at each MBS location to maximize the energy efficiency metric. Since the power allocations can also influence positively the AR throughput, this enables the AR throughput to be enhanced as well as the energy efficiency. The proposed analytical results are verified by using the MATLAB source code. It is shown that the proposed method can considerably improve both of AR network energy efficiency and throughput compared with conventional designs.

On Exploiting Millimeter-Wave Spectrum Trading in Countrywide Mobile Network Operators for High Spectral and Energy Efficiencies in 5G/6G Era.

In this paper, we propose a dynamic exclusive-use spectrum access (DESA) method to improve the overall licensed millimeter-wave (mmWave) spectrum utilization of all mobile network operators (MNOs) in a country. By exploiting secondary spectrum trading, the proposed DESA method shares partly and exclusively the licensed mmWave spectrum of one MNO to another in a dynamic and on-demand basis for a certain agreement term. We formulate the proposed DESA method for an arbitrary number of MNOs in a country. We then present an iterative algorithm to find the optimal amount of shared spectrum for each MNO, which is updated at each agreement term. We derive average capacity, spectral efficiency, energy efficiency, and cost efficiency performance metrics for all MNOs countrywide and present extensive numerical and simulation results and analyses for an example scenario of a country with four MNOs each assigned statically with an equal amount of 28-GHz mmWave spectrum. By applying DESA, we show that MNOs with a lack of minimum licensed spectra to serve their data traffic can lease at the cost of payment of the required additional spectra from other MNOs having unused or under-utilized licensed spectra. Moreover, it is shown that the overall countrywide average capacity, spectral efficiency, energy efficiency, and cost efficiency can be improved, respectively, by 25%, 25%, 17.5%, and 20%. Furthermore, we show that, by applying DESA to all MNOs countrywide, the expected spectral efficiency and energy efficiency requirements for sixth-generation (6G) mobile systems can be achieved by reusing the same mmWave spectrum to 20% fewer buildings of small cells. Finally, using the statistics of subscribers of all MNOs, we present a case study for fifth-generation (5G) networks to demonstrate the application of the proposed DESA method to an arbitrary country of four MNOs.

On Maximizing Energy and Spectral Efficiencies Using Small Cells in 5G and Beyond Networks.

Addressing high capacity at low power as a key design goal envisages achieving high spectral efficiency (SE) and energy efficiency (EE) for the next-generation mobile networks. Because most data are generated in indoor environments, an ultra-dense deployment of small cells (SCs), particularly within multistory buildings in urban areas, is revealed as an effective technique to improve SE and EE by numerous studies. In this paper, we present a framework exploiting the four most interconnected-domain, including, power, time, frequency, and space, in the perspectives of SE and EE. Unlike existing literature, the framework takes advantage of higher degrees of freedom to maximize SE and EE using in-building SCs for 5G and beyond mobile networks. We derive average capacity, SE, and EE metrics, along with defining the condition for optimality of SE and EE and developing an algorithm for the framework. An extensive system-level evaluation is performed to show the impact of each domain on SE and EE. It is shown that employing multiband enabled SC base stations (SBSs) to increase operating spectrum in frequency-domain, reusing spectrum to SBSs more than once per building in spatial-domain, switching on and off each in-building SBS based on traffic availability to reduce SBS power consumption in power-domain, and using eICIC to avoid co-channel interference due to sharing spectrum with SBSs in time-domain can achieve massive SE and EE. Finally, we show that the proposed framework can satisfy SE, EE, as well as user experience data rate requirements for 5G and beyond mobile networks.

Procedural tool for analysing building energy performance: structural equation modelling protocol

Building energy performance assessment technique has become a new paradigm that plays a significant part in reducing world energy demand and greenhouse gas emissions. However, there exists a global proliferation of diverse models for assessing and benchmarking buildings. This paper proposes a single building energy performance assessment model that considered several factors that affect office building energy efficiency performances in two different countries. It aimed to develop a model that could identify building energy performance critical factors as a new technique for aggregating energy efficiency metrics for commercial buildings. It examined the relationship and interdependency between the variables as it affects buildings’ performance as a basis for developing its theoretical model. Survey questions were derived from variables obtained from the existing literature using this theoretical paper proposition. A self-administered questionnaire was used to gather data from occupants of office buildings in Nigeria and the UK. Exploratory factor analysis and structural equation modelling via confirmatory factor analysis were used to analyse the explanatory power of the measured variables and their constructs. The results identified management, strategic and operational issues as critical factors that affect building energy performance in both countries. It confirmed the relationships and interdependency of the study factors and developed a new strategy that gives them proper considerations in the operations and management of building energy. Data collected support the theoretical model, and the measurement model fits into the conceptual model. The model gives a quantitative approach that identified critical factors for improving energy management and auditing efficiency of buildings.

Flexible, Brain-Inspired Communication in Massive Wireless Networks.

In this paper, a new perspective of using flexible, brain-inspired, analog and digital wireless transmission in massive future networks, is presented. Inspired by the nervous impulses transmission mechanisms in the human brain which is highly energy efficient, we consider flexible, wireless analog and digital transmission on very short distances approached from the energy efficiency point of view. The energy efficiency metric is compared for the available transmission modes, taking the circuit power consumption model into account. In order to compare the considered systems, we assume that the transmitted data comes from analog sensors. In the case of the digital transmission scheme, the decoded data are converted back to analog form at the receiving side. Moreover, different power consumption models from the literature and the digital transmission schemes with different performance are analyzed in order to examine if, for some applications and for some channel conditions, the analog transmission can be the energy-efficient alternative of digital communication. The simulation results show that there exist some cases when the analog or simplified digital communication is more energy efficient than digital transmission with modulation.