Energy-based Accounting Model for Heterogeneous Supercomputers

The article is devoted to consideration of issues related to assessing the energy efficiency of a coal mining enterprise. Coal mining enterprises are characterized by a large volume of energy intensive ore mining operations. The article presents possible ways of energy saving in the mining industry. During the research, a system of energy efficiency indicators for coal mining enterprises is presented. Materials and methods. The article proposes a system of indicators for coal enterprises based on existing methods and approaches to assessing energy efficiency. Energy consumption dependence graphs, benchmarking, and pinch analysis are considered as a starting point. Results. The study presents a system of energy efficiency indicators for coal mining enterprises. The proposed system has several levels. The existing methods and approaches for assessing this important indicator for industrial enterprises in the coal industry as a whole are considered. The author’s approach to formalizing the system of energy efficiency indicators for coal mining enterprises is proposed. The significance of energy audit as a key assessment measure is indicated and its stages are highlighted. The influencing factors determining energy costs and their reduction for a coal mining enterprise are systematized, barriers preventing the practical implementation of energy saving measures are analyzed. Discussion. The proposed metrics system provides a comprehensive framework for assessing the energy efficiency of coal mining enterprises at various levels. Its flexibility allows it to be applied to the analysis of individual equipment units, production processes, and overall enterprise performance. Its multi-level approach provides a detailed understanding of energy consumption, which allows for informed decision-making to optimize operations. At the equipment level, the metrics system evaluates the energy consumption and efficiency of individual machines and devices. At the production process level, the metrics system analyzes the energy consumption and efficiency of various stages of coal mining. At the enterprise level, the metrics system provides a comprehensive assessment of overall energy efficiency. It takes into account energy consumption at all levels and allows enterprises to compare their performance with each other and with industry benchmarks. Conclusion. Energy efficiency assessment of a coal mining enterprise allows to understand how and where energy is used, where its use can be improved, and how to reduce its costs. To conduct an energy efficiency assessment, the article proposes a system of indicators, highlighting the features of an energy audit. The main advantage of the proposed system of indicators is that it covers a wide range of energy consumption indicators, which are carefully selected and located at different levels of detail. In general, a balanced approach to the level of detail and generalization in the system of indicators provides flexibility in adapting indicators to specific research questions and helps to interpret the results in the context of structural differences between energy systems. Resume. Overall, the proposed metrics system offers a comprehensive approach to analyzing the energy efficiency of coal mining enterprises, covering all levels of operations. Its use allows enterprises to improve energy efficiency, reduce operating costs, and contribute to a more sustainable coal mining industry. Suggestions for practical applications and directions for future research. The use and implementation of the developed system at a coal mining company allows it to improve energy efficiency, reduce operating costs and contribute to a more sustainable coal mining industry.

Research on the building energy efficiency design strategy of Chinese universities based on green performance analysis

Highlights on energy efficiency improvements: a case of a small brewery

The realization of sustainable development depends on the efficient use of energy resources. Energy consumption productivity and efficiency are among important indices that play a critical role in explaining the behavior of energy consumption structure of different economic sectors, which in turn, plays a key role in policy making. From among economic sectors, the agriculture sector, as an important sector, depends on the consumption of energy as a factor underpinning agricultural production to a great extent. Thus, the present study tries to firstly calculate energy efficiency and productivity of agricultural sector, and then, investigate the relationship between production and energy consumption of New Zealand’s agricultural sector using simultaneous equations system and two-stage least squares method. The annual data of New Zealand agricultural economics are used for the period of 1990-2017. The results showed that agricultural sector’s energy consumption efficiency in New Zealand was mostly optimum according to energy efficiency index. In addition, estimation of energy equation revealed that the added value of agricultural sector had a positive influence on energy consumption growth. Among the other positively affecting variables, agricultural sector’s labour and energy consumption in the prior period could be mentioned. The negative sign of capital stock in the equation reflects the fact that the higher the investment in agricultural sector, the lower the energy consumption.

The effect of applying an energy pulse to the heart during ventricular fibrillation is described by the probability of successful defibrillation or success rate. Seven to ten (8.60 +/- 0.84: mean +/- standard deviation) defibrillation trials per energy were randomly attempted at energies which span the defibrillation success rate versus energy curve. We obtained 70.0 +/- 8.4 episodes per dog. We fit the defibrillation success rate versus energy relationship from ten dogs (20.5 +/- 1.5 kg) to four types of curves: linear, exponential, probit transformed linear, and logit transformed linear. The correlation coefficients for each fit are 0.917 +/- 0.057, 0.944 +/- 0.014, 0.926 +/- 0.51, and 0.889 +/- 0.098, respectively. We therefore conclude that the exponential curve best describes the DSRE relationship. This suggests the existence of an energy below which defibrillation does not occur. At higher energies, the exponential curve asymptotically approaches a 100% success rate, which indicates that increasing the energy produces a diminishing benefit to defibrillation success rate. The estimated energies with a 0% defibrillation success rate are surprisingly consistent among dogs, with 2.072 +/- 0.553 J. The estimated energy with an 80% defibrillation success rate is 5.217 +/- 1.091 J. The estimated defibrillation success rate corresponding to the defibrillation threshold of 3.59 +/- 1.06 J is consistent with 0.516 +/- 0.144. The estimated energies with a 0% success rate correlate well with the defibrillation thresholds with R = 0.772; P = 0.0088. Since implantable defibrillators have a limited energy supply, we determined energy efficiency by dividing defibrillation success rate by the applied energy and energy consumption by dividing the applied energy by the defibrillation success rate. The most efficient defibrillation energy occurs at the maximum energy efficiency and the minimum energy consumption. The most efficient defibrillation energy of 4.34 +/- 0.97 J determined from the exponential fit has a success rate of 0.70 +/- 0.06. The most efficient defibrillation energy can be predicted from the defibrillation threshold. Clinically, a 70% success rate may not be adequate. We, therefore, compared the energy efficiency and consumption of energies with 90% and 95% success rates to the most efficient defibrillation energy. About a 50% increase in energy from the most efficient defibrillation energy is necessary for a 90% success rate which results in about a 13% loss in energy efficiency and about a 16% increase in energy consumption. About an 84% energy increase is necessary for a 95% success rate which results in about a 24% loss in energy efficiency and about a 33% increase in energy consumption.(ABSTRACT TRUNCATED AT 400 WORDS)

Purpose – This paper aims to describe the creation of innovative and intelligent systems to optimise energy efficiency in manufacturing. The systems monitor energy consumption using ambient intelligence (AmI) and knowledge management (KM) technologies. Together they create a decision support system as an innovative add-on to currently used energy management systems. Design/methodology/approach – Energy consumption data (ECD) are processed within a service-oriented architecture-based platform. The platform provides condition-based energy consumption warning, online diagnostics of energy-related problems, support to manufacturing process lines installation and ramp-up phase and continuous improvement/optimisation of energy efficiency. The systems monitor energy consumption using AmI and KM technologies. Together they create a decision support system as an innovative add-on to currently used energy management systems. Findings – The systems produce an improvement in energy efficiency in manufacturing small- and medium-sized enterprises (SMEs). The systems provide more comprehensive information about energy use and some knowledge-based support. Research limitations/implications – Prototype systems were trialled in a manufacturing company that produces mooring chains for the offshore oil and gas industry, an energy intensive manufacturing operation. The paper describes a case study involving energy-intensive processes that addressed different manufacturing concepts and involved the manufacture of mooring chains for offshore platforms. The system was developed to support online detection of energy efficiency problems. Practical implications – Energy efficiency can be optimised in assembly and manufacturing processes. The systems produce an improvement in energy efficiency in manufacturing SMEs. The systems provide more comprehensive information about energy use and some knowledge-based support. Social implications – This research addresses two of the most critical problems in energy management in industrial production technologies: how to efficiently and promptly acquire and provide information online for optimising energy consumption and how to effectively use such knowledge to support decision making. Originality/value – This research was inspired by the need for industry to have effective tools for energy efficiency, and that opportunities for industry to take up energy efficiency measures are mostly not carried out. The research combined AmI and KM technologies and involved new uses of sensors, including wireless intelligent sensor networks, to measure environment parameters and conditions as well as to process performance and behaviour aspects, such as material flow using smart tags in highly flexible manufacturing or temperature distribution over machines. The information obtained could be correlated with standard ECD to monitor energy efficiency and identify problems. The new approach can provide effective ways to collect more information to give a new insight into energy consumption within a manufacturing system.

Energy consumption and efficiency technology measures in European non-residential buildings

Multimedia applications are among the most dominant computing workloads driving innovations in high performance and cost effective systems. In this regard, modern general-purpose microprocessors have included multimedia extensions (e.g., MMX, SSE, VIS, MAX, ALTIVEC) to their instruction set architectures to improve the performance of multimedia with little added cost to microprocessors. Whereas prior studies of multimedia extensions have primarily focused on a single processor, this paper quantitatively evaluates the impact of multimedia extensions on system performance and efficiency for different number of processing elements (PEs) within an integrated multiprocessor array. This paper also identifies the optimal PE granularity for the array system and implementation technology in terms of throughput, area efficiency, and energy efficiency using architectural and workload simulation. Experimental results with cycle accurate simulation and technology modeling show that MMX-type instructions (a representative Intel’s multimedia extensions) achieve an average speedup ranging from 1.24( (at a 65,536 PE system) to 5.65( (at a 4 PE system) over the baseline performance. In addition, the MMX-enhanced processor array increases both area and energy efficiency over the baseline for all the configurations and programs. Moreover, the highest area and energy efficiency are achieved at the number of PEs between 256 and 1,024. These evaluation techniques composed of performance simulation and technology modeling can provide solutions to the design challenges in a new class of multiprocessor array systems for multimedia.

Hybrid neural networks (hybrid-NNs) have been widely used and brought new challenges to NN processors. Thinker is an energy efficient reconfigurable hybrid-NN processor fabricated in 65-nm technology. To achieve high energy efficiency, three optimization techniques are proposed. First, each processing element (PE) supports bit-width adaptive computing to meet various bit-widths of neural layers, which raises computing throughput by 91% and improves energy efficiency by 1.93× on average. Second, PE array supports on-demand array partitioning and reconfiguration for processing different NNs in parallel, which results in 13.7% improvement of PE utilization and improves energy efficiency by 1.11×. Third, a fused data pattern-based multi-bank memory system is designed to exploit data reuse and guarantee parallel data access, which improves computing throughput and energy efficiency by 1.11× and 1.17×, respectively. Measurement results show that this processor achieves 5.09-TOPS/W energy efficiency at most.

This study examines how energy consumption influences Ethiopia’s economic growth, focusing on its implications for sustainable development. It analyses the mediating role of energy efficiency and the moderating effect of renewable energy adoption. Using a quantitative design and data from 403 energy-related firms, the findings reveal that energy use significantly drives economic performance, with energy efficiency serving as a key moderating factor enhancing sustainability. The objective is to assess how improvements in energy consumption and efficiency drive economic productivity, while the adoption of renewable energy enhances sustainability. The quantitative research approach was used in the investigation. Structured questionnaires were used to gather data from 403 energy-related businesses. Results show that economic growth is significantly impacted by energy use, with energy efficiency acting as a crucial moderator. Furthermore, by reducing the negative economic and environmental cost of non-renewable energy, renewable energy eliminates this connection. Improved energy efficiency, for example, boosts economic production, while consuming renewable energy enhances long-term growth by lessening the burden on the environment. These findings highlight the urgent need for Ethiopia to diversify its energy resource base, prioritising renewable sources such as wind, solar, and hydroelectric power. Policymakers are invited to encourage technological innovation and infrastructure development to improve energy efficiency, invest in renewable energy sources, and promote fair access to energy. Through combining energy policy with sustainability goals, this study emphasises the fundamental role of energy in Ethiopia’s future economic stability and environmental sustainability.

In buildings, a serious waste of energy exists in HVAC, and refrigeration stations account for the most of energy consumption. Because the coupling exists between devices of refrigeration stations, the traditional evaluating indicator based on independent device can not reflect the energy consumption characteristics and the optimal energy consumption of refrigeration stations toward different load. Drawing lessons from the definition of coefficient of performance of refrigerator, we bring forward the concept of energy efficiency of refrigeration stations, define the expression of energy efficiency and energy consumption of refrigeration stations, give out relation between energy efficiency in different cooling water temperature and variation of load, discuss the trend of energy consumption and energy efficiency of devices of refrigeration stations and the method to improve the energy efficiency. In order to compare the energy efficiency of refrigeration stations in buildings of different style, we bring in rated energy efficiency as parameter to represent the real-time energy efficiency when using actual operation data to calculate the real-time energy efficiency of refrigeration stations in buildings of different style. The energy efficiency can not only reflect both the operating efficiency and the potential of energy-saving of refrigeration stations, but also be used qualitatively for analyzing the base energy consumption in buildings.

In addition to cutting down on energy use, the green cloud-based service also drastically cuts down on operating expenses. Strongly coupled data centers demand controlled energy, constant performance, and overall optimization of excess energy consumption in order to do considerable calculations, which in turn give entire processing influence from a wide collection of resources. An energy-saving scheduling method is the focus of the research, which makes use of green cloud technologies. In recent years, cloud computing has been integrated into several domains, including computing, research, industry, and business. The need for specialized hardware along with additional resources is rendered obsolete by the provision of many services across the internet through cloud computing. Problems with energy efficiency, resource heterogeneity, and resource usage are only a few of the issues that cloud computing systems encounter. Consolidation approaches like as task scheduling as well as virtual machines (VMs) are used to address these concerns. There has been a plethora of research on task scheduling. Researchers have examined the issue using a variety of metrics and aims. The essay delves into the topic of virtualized cloud data centers' energy usage and effective resource use. Task categorization and thresholds form the basis of the proposed method, which aims to improve resource use and scheduling efficiency. Every company needs a highly efficient structure that is both flexible and homogeneous across all of their cloud environments. This research aims to reduce energy consumption in green cloud systems by using a hybrid scheduling strategy that combines a minimal completion time with a priority-based weighted round-robin. An Artificial Neural Network (ANN) based load balancing method is presented in this study. ANN makes demand predictions and then distributes resources accordingly. This method uses less energy than the cautious over-provisioning strategy since it constantly adjusts the number of active servers based on current demand. In addition, a server that is operating at a high utilization may handle more task with the same amount of power, but it will use more power overall. At last, we take a look at the current state of load balancing in cloud computing and compare it to others using different metrics.

Long-Term Energy Solutions: The Truth Behind the Silent Lie

Reinforcement Learning (RL) has shown great performance in solving sequential decision-making and control in dynamic environments problems. Despite its achievements, training Deep Neural Network (DNN) based RL is expensive in terms of time and power because of the large number of episodes required to train agents with high dimensional image representations. At the deployment also, the massive energy footprint of deep neural networks can be a major drawback. Embedded devices as the main deployment platform, are intrinsically resource-constrained and deploying DNN on them is challenging. Consequently, reducing the number of actions taken by the RL agent to learn desired policy, along with the development of efficient hardware architectures for RL is crucial. In this paper, we propose a novel hardware architecture for RL agents based on the learning hierarchical policies method. We show that hierarchical learning with several levels of control improves RL agents training efficiency and the agent converges faster compared to a none hierarchical model and therefore using less power. This is especially true as the environment becomes more complex with multiple objective sub-goals. Our method is important for efficient learning of policies for RL agent, especially when the target platform is a resource constraint embedded device. By performing a systematic neural network architecture search and hardware design space exploration, we implemented an energy-efficient scalable hardware accelerator for the hierarchical RL. Hardware factors of merit such as the latency, throughput, and energy consumption of the accelerator are evaluated with the various processing elements, and model parameters. The most energy-efficient configuration achieves 139 fps throughput with 5.8 mJ energy consumption per classification on Xilinx Artix-7 FPGA. Compared to similar works our design shows up to 3x better energy efficiency.

Objective. The goal of the study is to identify ways to improve energy efficiency by conducting a comprehensive analysis of engineering systems of a house, taking into account the existing model of energy supply and by monitoring the indicators of influence on the efficiency of engineering and technical equipment. Methods. The main method is based on a survey of the level of energy efficiency of heating and hot water supply systems. Results. The article discusses the concept of energy efficiency of a building, energy efficiency class, main utility systems of a residential building, indicators of influence on the state of systems, engineering component and ways to improve it. The article addresses issues related to a full analysis of existing systems in order to assess the implementation of necessary energy-saving measures, as well as their risks and benefits. The possibilities of controlling energy consumption and efficiency of works carried out due to the reconstruction of utility systems of the building have been determined. The results of the analysis of energy-efficient measures, and the feasibility of implementing each of them have been determined in order to identify optimal solutions. Conclusion. Increasing the energy efficiency of building utility systems is one of the priority tasks for creating comfortable living conditions. The energy efficiency class of building utility systems is established according to the state of a system, takes into account all quality indicators for energy consumption, and the energy certificate fully shows the level of energy efficiency.