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Accurate estimation of energy savings is crucial for the effective implementation of energy conservation measures (ECMs). Simultaneously, the integration of Artificial Intelligence (AI) has revolutionized software engineering by imbuing software with intelligent capabilities and autonomy. In this paper, we propose an ensemble model for precisely estimating baseline energy consumption within the realm of AI-empowered autonomous software. The ensemble model combines predictions from three tree-based Machine Learning models, namely Random Forest, XGBoost, and LightGBM. Notably, our model emphasizes the provision of explainability, granting transparency and insights into the key factors influencing baseline energy consumption. To validate its effectiveness, we conduct experimental evaluations on a diverse cluster of real-world buildings in Latvia. The results demonstrate the superiority of our proposed ensemble model over individual models and even a deep learning network tailored for energy consumption estimation. These findings underscore the efficacy of AI-empowered models in the energy sector, offering a robust and interpretable solution for estimating energy savings. [ABSTRACT FROM AUTHOR]

• An energy-efficient event-triggered dual-rate networked control system is presented. • Kalman filtering, prediction-based and packet-based control are integrated in the control scheme. • A satisfactory performance is obtained, despite the existence of communication problems, disturbances and noises. • The robust stability of the closed-loop system is addressed via Linear Matrix Inequalities. • The control is experimentally validated in an Unmanned Ground Vehicle. In this paper, the robust stability and disturbance rejection performance analysis of an energy-efficient control is addressed in the framework of Networked Control System (NCS). The control scheme under study integrates periodic event-triggered control, packet-based control, time-varying Kalman filter, dual-rate control and prediction techniques, whose design is aimed at reducing energy consumption and bandwidth usage. The robust stability against time-varying model uncertainties is analyzed by means of a sufficient condition based on Linear Matrix Inequalities (LMI). Finally, the effectiveness of the proposed approach is experimentally validated in a tracking control for an Unmanned Ground Vehicle (UGV), which is a battery-constrained mobile device with limited computation capacities. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.cor.2017.02.014 Byline: Dileep G. Dhavale (a), Joseph Sarkis (b) Abstract: Internal rate of return (IRR) is a widely used tool in ranking capital budgeting projects and eventual accept or reject decisions. In this paper, we consider an investment decision involving a sustainable, energy efficient, greenhouse gases (GHG) reducing asset and incorporate the value of carbon emission allowances for the investing company. These allowances create cash flows that may be characterized by significant volatility and uncertainty. The methodology developed in this paper allows decision makers to integrate their knowledge of carbon trading markets and the cash flows that result from sale of emissions credits. The novel methodology utilizes a Bayesian model for IRR that uses Gibbs sampler. Analysis of the results shows that IRR is influenced by volatility and uncertainty of carbon credit cash flows. Ignoring those uncertainty characteristics and simply using the expected values of cash flows can result in significantly inaccurate investment rate of returns. When compared to deterministic IRR calculations, the results show that the occurrence of very high and very low cash flows affects IRR positively, whereas higher variability of cash flow distribution affects IRR of GHG-reducing asset negatively. In other words, frequent large or small cash flows are preferred over fluctuating cash flows. The results may also provide a rationale for the existence of an anomalous consumer behavior known as the energy efficiency gap. Author Affiliation: (a) Graduate School of Management, Clark University, 950 Main Street, Worcester, MA 01610-1477, USA (b) Foisie School of Business, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609-2280, USA Article History: Received 21 March 2015; Revised 6 November 2016; Accepted 17 February 2017

Abstract According to recent statistics, more than 1 zettabytes of data is moved over the Internet annually, which consumes several terawatt hours of electricity, and costs billions of US dollars to the world economy. Hypertext Transfer Protocol (HTTP) is used in the majority of these data transfers, accounting for 70% of the global Internet traffic. We claim that HTTP transfers, and the services based on HTTP, can become more energy efficient without any performance degradation by application-level tuning of certain protocol parameters. In this paper, we analyze several application-level parameters that affect the throughput and energy consumption in HTTP data transfers, such as the level of parallelism, concurrency, and pipelining. We introduce novel service-level-agreement (SLA) based algorithms which can decide the best combination of these parameters considering user-defined energy efficiency and performance criteria. Our experimental results show that up to 80% energy savings can be achieved at the client and server hosts during HTTP data transfers, while increasing the end-to-end transfer throughput at the same time. [ABSTRACT FROM AUTHOR]

Abstract: Computational Fluid Dynamics (CFD) is used to gain insight in the aerodynamic performance of a venturi-shaped roof (called VENTEC roof). The simulations are performed with the 3D steady Reynolds-averaged Navier–Stokes (RANS) equations and the Renormalisation Group k–ε model. A detailed analysis is conducted of the influence of the so-called venturi-effect and the wind-blocking effect on the aerodynamic performance of the VENTEC roof. The specific roof configuration is intended to create a negative pressure in the narrowest roof section (contraction) which can be used to partly or completely drive the natural ventilation of the building zones. The CFD simulations are based on a detailed grid-sensitivity analysis and on successful validation of the grid-independent results by comparison with experiments in an atmospheric boundary layer wind tunnel. The simulations show that the aerodynamic performance of the roof is governed by the balance between the so-called venturi-effect on the one hand and the wind-blocking effect on the other hand. The venturi-effect cannot act to its full extent because the flow is non-confined. The wind-blocking effect refers to the effect of the resistance exerted by the roof contraction on the air flow and the resulting tendency of the approaching wind to flow around and over the roof, rather than only being forced through the roof contraction. The results indicate that because of the wind-blocking effect, the highest contraction ratio does not provide the best aerodynamic performance and the largest negative pressure, which is a counter-intuitive result. The paper also provides a parametric analysis to optimise the roof contraction height and contraction ratio. The study in this paper illustrates the use of CFD to increase insight in building aerodynamics and to support sustainable building design. [Copyright &y& Elsevier]

In recent years, the light-energy consumption accounts for quite a large proportion of total electricity consumption. In this paper, an optimal lighting control strategy is designed for a lighting system with multiple lighting sources, to decrease electric energy consumption and increase energy efficiency. In the proposed control strategy, a novel multi-variable fractional-order extremum seeking control (FO ESC) strategy is implemented in minimizing the light-energy consumption by separately regulating the brightness of multi-lighting sources, while a PID method is applied to guarantee the desired lighting level. The proposed scheme is presented to not only raise the convergence rate and enhance the control accuracy, but also to improve the search efficiency of the minimum light-energy consumption by manipulating the fractional-order. Experimental results including comparison with the corresponding integer-order (IO) ESC show that the light-energy consumption under the proposed strategy can approach a smaller neighborhood of the minimum lighting-energy point more quickly. [ABSTRACT FROM AUTHOR]

This paper studies a large-scale heterogeneous cellular network (HCN) consisting of ultra-dense small cells and macro cells. Each small cell base station (SBS) serves a dedicated user with a constant distance in a random direction. To reduce electricity consumption without degrading network performance, a hybrid SBS deployment strategy is considered, in which both on-grid and off-grid SBSs are placed in the network for data transmissions. The on-grid SBSs are connected to the electric grid and powered by stable energy, while the off-grid SBSs are powered by harvesting the ambient radio frequency energy broadcasted by the macro base stations (MBSs) and the on-grid SBSs. Using tools from stochastic geometry, the outage probabilities of both cellular network users and dedicated users are analyzed. On this basis, the tradeoff between the energy efficiency and throughput of the HCN is characterized and the optimal ratio of on-grid and off-grid SBSs is also analyzed. These results can provide useful insights and guidelines for the practical design of the next generation energy harvesting-based HCNs. [ABSTRACT FROM AUTHOR]

Paper presents computer simulations conducted to study the influence of the configuration of an energy harvesting system with tunnel mechanical characteristics in terms of its energy generation efficiency. In this context, numerical simulation experiments were carried out to assess the impact of physical properties and initial conditions on the occurrence of coexisting solutions and its energy efficiency. Chaotic motion zones were identified on the basis of Lyapunov exponent. To be able to choose the best solution in terms of energy efficiency, studies on the use of impulse excitation to change the orbit of the solution were also presented. Results show that modifying the energy harvesting system design with an impulse excitation subsystem can significantly improve (up to tenfold) the amount of energy harvested from vibrating mechanical devices. • A new energy harvesting system has been designed (TEEH). • The parameters' domains have been identified where the optimal energy harvesting is achieved. • The method of impulse excitation to change the unfavorable orbits is developed. • Basins of attraction of coexisting solutions are identified. [ABSTRACT FROM AUTHOR]

Reconstruction of the tridimensional geometry of a visual scene using the binocular disparity information is an important issue in computer vision and mobile robotics, which can be formulated as a Bayesian inference problem. However, computation of the full disparity distribution with an advanced Bayesian model is usually an intractable problem, and proves computationally challenging even with a simple model. In this paper, we show how probabilistic hardware using distributed memory and alternate representation of data as stochastic bitstreams can solve that problem with high performance and energy efficiency. We put forward a way to express discrete probability distributions using stochastic data representations and perform Bayesian fusion using those representations, and show how that approach can be applied to diparity computation. We evaluate the system using a simulated stochastic implementation and discuss possible hardware implementations of such architectures and their potential for sensorimotor processing and robotics. [ABSTRACT FROM AUTHOR]

Keywords Automobile markets; New products; Consumer benefits Highlights * Estimates benefits generated by the introduction of the hybrid electric vehicle. * Gains in social surplus of $1.2 billion in 2007. * Environmental benefits make up only 0.2% of the net benefits. * 31% of social surplus attributed to hybrid technology independent of fuel efficiency gains. * All income groups benefit from hybrid introduction with higher income households gaining the most. Abstract This study models demand and supply in the market for new automobiles to estimate the economic benefits generated by the introduction of the hybrid electric vehicle. We estimate our model using all new vehicle registrations in California along with detailed demographic data. Our counterfactual analysis reveals that the addition of hybrids to the consumer choice set generates gains in social surplus that peak at roughly $1.2 billion annually in 2007, but environmental benefits make up 0.2% of the net benefits. We find that 31% of the social surplus in 2008 can be attributed directly to the hybrid vehicles independently from gains in fuel efficiency, with the remaining 69% attributed to the gains in fuel efficiency. We further show that all income groups benefit from the introduction of the hybrids, with the top 50% of the income distribution receiving the most. Author Affiliation: (a) Managing Economist, Bates White Economic Consulting, 2001 K Street NW, North Building, Suite 500, Washington, DC 20036, United States (b) Yale University and NBER, 195 Prospect Street, New Haven, CT 06511, United States * Corresponding author. Article History: Received 1 November 2020; Revised 30 November 2022; Accepted 7 December 2022 (footnote)[white star] The authors gratefully acknowledge Steve Berry and Phil Haile for their guidance and suggestions in the development of this article. We also thank Julian Chan for assistance with model specification and runtime optimization, and the participants at several seminars. An earlier draft of this paper was part of Rich Langford's dissertation at Yale. All errors are our own. (footnote)1 Professor. Byline: Richard P. Langford [richard.langford@bateswhite.com] (*,a), Kenneth Gillingham [kenneth.gillingham@yale.edu] (1,b)

To access, purchase, authenticate, or subscribe to the full-text of this article, please visit this link: http://dx.doi.org/10.1016/j.cose.2018.05.014 Byline: Eduardo Viegas [eduardo.viegas@ppgia.pucpr.br] (a), Altair Santin [santin@ppgia.pucpr.br] (a,*), Luiz Oliveira [lesoliveira@inf.ufpr.br] (b), Andre Franca [andref@alunos.utfpr.edu.br] (c), Ricardo Jasinski [rjasinski@ieee.org] (c), Volnei Pedroni [pedroni@utfpr.edu.br] (c) Keywords Classifier design and evaluation; Feature evaluation and selection; Machine learning; Energy-aware systems; Network-level security and protection; System-on-a-chip; Field-programmable gate array Abstract Embedded systems (electronic systems with a dedicated purpose that are part of larger devices) are increasing their relevance with the rise of the Internet of Things (IoT). Such systems are often resource constrained, battery powered, connected to the internet, and exposed to an increasing number of threats. An approach to detect such threats is through an anomaly-based intrusion detection with machine-learning techniques. However, most of these techniques were not created with energy efficiency in mind. This paper presents an anomaly-based method for network intrusion detection in embedded systems. The proposed method maintains the classifier reliability even when network traffic contents changes. The reliability is achieved through a new rejection mechanism and a combination of classifiers. The proposed approach is energy-efficient and well suited for hardware implementation. The experiments presented in this paper show that the hardware versions of the machine learning algorithms consume 46% of the energy used by their software counterparts, and the feature extraction and packet capture modules consume 58% and 37% of their respective software counterparts. Author Affiliation: (a) Pontifical Catholic University of Parana, Curitiba 80215-901, Brazil (b) Federal University of Parana, Curitiba 80060-000, Brazil (c) Federal Technological University of Parana, Curitiba 80230-901, Brazil * Corresponding author. Article History: Received 8 October 2017; Revised 23 May 2018; Accepted 28 May 2018

One important way to extend the lifetime of wireless sensor networks is to deploy the sensors in a dense manner. The redundancy among the sensed data demonstrates that it is not efficient to collect raw data from all nodes in the network if we further consider that the data is generally spatial-correlated. The node scheduling strategy aims at selecting a set of representative nodes to provide the required data service in a periodic manner with accuracy guarantee. This strategy can effectively reduce the number of active nodes and the amount of messages in the network, and extend the network lifetime accordingly. In this paper, we firstly introduce how to model the spatial correlation among sensed data by Markov Random Field (MRF) model. Secondly, we formulate the problem definitions, namely, the data amendment problem which maximizes the data coverage range for a given node by amending the raw noise-corrupted data from the neighbors, while the representative nodes selection problem focuses on reducing the number of representative nodes and covering all nodes in the network, and the node scheduling problem aims at maximizing the network lifetime. Thirdly, we propose a novel Data Amendment Procedure (DAP), Representative node Selection Procedure (RSP) and energy-efficient Node Scheduling Algorithm (NSA) respectively for these above problems. Finally, extensive experiments demonstrate that the proposed node scheduling algorithm can significantly improve the network lifetime compared with related works with an average increment of about 80%. [ABSTRACT FROM AUTHOR]

To achieve high energy efficiency and low response time in big data stream computing environments, it is required to model an energy-efficient resource scheduling and optimization framework. In this paper, we propose a real-time and energy-efficient resource scheduling and optimization framework, termed the Re-Stream. Firstly, the Re-Stream profiles a mathematical relationship among energy consumption, response time, and resource utilization, and obtains the conditions to meet high energy efficiency and low response time. Secondly, a data stream graph is modeled by using the distributed stream computing theories, which identifies the critical path within the data stream graph. Such a methodology aids in calculating the energy consumption of a resource allocation scheme for a data stream graph at a given data stream speed. Thirdly, the Re-Stream allocates tasks by utilizing an energy-efficient heuristic and a critical path scheduling mechanism subject to the architectural requirements. This is done to optimize the scheduling mechanism online by reallocating the critical vertices on the critical path of a data stream graph to minimize the response time and system fluctuations. Moreover, the Re-Stream consolidates the non-critical vertices on the non-critical path so as to improve energy efficiency. We evaluate the Re-Stream to measure energy efficiency and response time for big data stream computing environments. The experimental results demonstrate that the Re-Stream has the ability to improve energy efficiency of a big data stream computing system, and to reduce average response time. The Re-Stream provides an elegant trade-off between increased energy efficiency and decreased response time effectively within big data stream computing environments. [ABSTRACT FROM AUTHOR]

Radio Frequency Identification (RFID) has attracted much research attention in recent years. RFID can support automatic information tracing and management during the management process in many fields. A typical field that uses RFID is modern warehouse management, where products are attached with tags and the inventory of products is managed by retrieving tag IDs. Many practical applications require searching a group of tags to determine whether they are in the system or not. The existing studies on tag searching mainly focused on improving the time efficiency but paid little attention to energy efficiency which is extremely important for active tags powered by built-in batteries. To fill in this gap, this paper investigates the tag searching problem from the energy efficiency perspective. We first propose an E nergy-efficient tag S earching protocol i n M ultiple reader RFID systems, namely ESiM, which pushes per tag energy consumption to the limit as each tag needs to exchange only one bit data with the reader. We then develop a time efficiency enhanced version of ESiM, namely TESiM, which can dramatically reduce the execution time while only slightly increasing the transmission overhead. Extensive simulation experiments reveal that, compared to state-of-the-art solution in the current literature, TESiM reduces per tag energy consumption by more than one order of magnitude subject to comparable execution time. In most considered scenarios, TESiM even reduces the execution time by more than 50%. [ABSTRACT FROM AUTHOR]

Abstract: All-to-all broadcast is a communication pattern in which every node initiates a broadcast. In this paper, we investigate the problem of building a unique cast tree of minimum total energy, which we call Minimum Unique Cast (MUC) tree, to be used for all-to-all broadcast. The MUC tree is unoriented and unrooted. We study three known heuristics for the minimum-energy broadcast problem: the Broadcast Incremental Power (BIP) algorithm, the Wireless Multicast Advantage-conforming Minimum Spanning Tree (WMA-conforming MST) algorithm, and the Iterative Maximum-Branch Minimization (IMBM) algorithm. Experimental results conducted on various types of networks are reported. We show that neither of these methods is best overall for building all-to-all broadcast trees. [Copyright &y& Elsevier]

Abstract: Energy efficiency has become one of the most important challenges in designing future computing systems, and the storage system is one of the largest energy consumers within them. This paper proposes an Energy Efficient Disk (EED) drive architecture which integrates a relatively small-sized NAND flash memory into a traditional disk drive to explore the impact of the flash memory on the performance and energy consumption of the disk. The EED monitors data access patterns and moves the frequently accessed data from the magnetic disk to the flash memory. Due to the data migration, most of the data accesses can be satisfied with the flash memory, which extends the idle period of the disk drive and enables the disk drive to stay in a low power state for an extended period of time. Because flash memory consumes considerably less energy and the read access is much faster than a magnetic disk, the EED can save significant amounts of energy while reducing the average response time. Real trace driven simulations are employed to validate the proposed disk drive architecture. An energy coefficient, which is the product of the average response time and the average energy consumption, is proposed as a performance metric to measure the EED. The simulation results, along with the energy coefficient, show that the EED can achieve an 89.11% energy consumption reduction and a 2.04% average response time reduction with cello99 trace, a 7.5% energy consumption reduction and a 45.15% average response time reduction with cello96 trace, and a 20.06% energy consumption reduction and a 6.02% average response time reduction with TPC-D trace, respectively. Traditionally, energy conservation and performance improvement are contradictory. The EED strikes a good balance between conserving energy and improving performance. [Copyright &y& Elsevier]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.cose.2017.07.001 Byline: Abdul Razaque, Syed S. Rizvi Abstract: The existing secure data aggregation approaches for wireless sensor networks were not designed for authorization, energy efficiency and proper security, leaving them prone to attacks. In this paper, we introduce the secure data aggregation using the access control and authentication (SDAACA) protocol. Using this protocol, we aim to detect sinkhole and Sybil attacks that are difficult to detect by existing cryptographic approaches. The proposed SDAACA protocol consists of two novel algorithms: the secure data fragmentation (SDF) and the node joining authorization (NJA). The SDF algorithm hides the data from the adversary by fragmenting it into small pieces. In the NJA algorithm, an authorization process is initiated before allowing any new node to join the network. Both algorithms help improve the Quality of Service (QoS) parameters. Moreover, we propose an access control scheme that supports accuracy, energy efficiency, freshness and authentication by reducing the communication overhead and guaranteeing the communication authenticity process. Furthermore, the proposed protocol is mapped on the oil-refinery plant to prevent and detect both sinkhole and Sybil attacks in the presence of static and mobile sensor nodes. Finally, we show the effectiveness of our proposed protocol through extensive simulations and a comparative study of other known secure data aggregation protocols. Article History: Received 20 January 2017; Revised 24 May 2017; Accepted 3 July 2017

Byline: Andrew Burlinson [A.C.Burlinson@lboro.ac.uk] (a), Monica Giulietti [M.Giulietti@lboro.ac.uk] (a), Giuliana Battisti [Giuliana.Battisti@wbs.ac.uk] (b,*) Keywords Consumers; Technology adoption; Energy efficiency; Inattention; Heuristics; District heating Highlights * We rely on traditional and behavioural theories to explore the decision-making processes of energy consumers. * We estimate an internal discount rate of around 36 per cent for homeowners, a signal that consumers undervalue future energy costs. * Neglecting consumer inattention and heuristics can lead to biases which cast doubt on the existence of the energy efficiency paradox. * Behavioural biases that affect the attitudes towards impure public goods have repercussions for environmental policies. * Our results provide policy insights on how to stimulate the adoption of sustainable technologies. Abstract This paper contributes to the debate on the energy efficiency paradox according to which consumers fail to adopt cost-effective, energy efficient technologies over less efficient technologies and therefore fail to reduce energy consumption. Both traditional and behavioural theories are used to investigate the decision-making process of residential consumers with empirical evidence based upon a specially designed quasi-experimental survey of 784 households on the decision to connect to a district-heating system, a more energy efficient alternative to individual heating systems. The results suggest an internal discount rate of around 36 per cent for homeowners, a signal that consumers undervalue future energy costs. We also find the household's decision to be negatively affected by years of payback up to around 7--8 years. Our findings suggest that neglecting consumer inattention and heuristics can lead to biases which cast doubt on the existence of the energy efficiency paradox. We believe that these results help to explain why some consumers are unlikely to invest in energy efficient technology, particularly those on low incomes. Author Affiliation: (a) School of Business and Economics, Loughborough University, Leicestershire, UK (b) Warwick Business School, University of Warwick, Coventry, CV4 7AL, UK * Corresponding author. Article History: Received 17 September 2017; Revised 18 May 2018; Accepted 29 June 2018

To access, purchase, authenticate, or subscribe to the full-text of this article, please visit this link: http://dx.doi.org/10.1016/j.respol.2017.02.004 Byline: Valeria Costantini [valeria.costantini@uniroma3.it] (a,b,*), Francesco Crespi (a,c), Alessandro Palma (d,e) Keywords Eco-innovation; Policy mix; Balance; Comprehensiveness; Policy spillovers; Energy efficiency; Residential sector Highlights * The effects of policy mix design on innovation in energy efficiency are analysed. * A balanced use of pull and push instruments fosters innovation performance. * A more comprehensive policy mix enhances innovation performance. * An excessive amount of policy instruments may reduce policy mix effectiveness. * The similarity with foreign policy mixes positively affects innovation performance. Abstract This paper provides an empirical investigation of the role played by selected characteristics of the policy mix in inducing innovation in energy efficiency technologies. An original dataset covering 23 OECD countries over the period 1990--2010 combines the full set of policies in the energy efficiency domain for the residential sector with data on patents applied over the same period in this specific technological sector. The econometric results suggest that when the policy mix is characterised by a more balanced use in demand-pull and technology-push instruments, its positive effects on eco-innovation tend to be greater. Moreover, a more comprehensive policy mix is shown to be able to enhance innovation activities for the generation of new energy efficient technologies. However, the simple addition of an indiscriminate number of simultaneous policy instruments may reduce policy mix effectiveness. Finally, the empirical findings confirm previous evidence on the importance of policy spillover effects, and suggest that policy similarity between countries may represent an important aspect to be accounted for in policy mix design. Author Affiliation: (a) Roma Tre University, Rome, Italy (b) SEEDS, Italy (c) BRICK, Collegio Carlo Alberto, Turin, Italy (d) CEIS -- University of Tor Vergata, Rome, Italy (e) IEFE -- Bocconi University, Milan, Italy * Corresponding author at: Department of Economics, Roma Tre University, Rome, Italy. Article History: Received 17 July 2015; Revised 6 February 2017; Accepted 16 February 2017