Screening of energy efficient technologies for industrial buildings' retrofit

This chapter discusses screening of energy efficient technologies for industrial buildings' retrofit

Thermally Aware, Energy-Based Techniques for Improving Data Center Energy Efficiency

This work investigates the practical implementation of so-called thermally aware, energy optimized load placement in air-cooled, raised floor data centers to reduce the overall energy consumption, while maintaining the reliability of the IT equipment. The work takes a systematic approach to modeling the data center\u27s airflow, thermodynamic and heat transfer characteristics - beginning with simplified, physics-inspired models and eventually developing a high-fidelity, experimentally validated thermo-hydraulic model of the data center\u27s cooling and power infrastructure. The simplified analysis was able to highlight the importance of considering the trade-off between low air supply temperature and increased airflow rate, as well as the deleterious effect of temperature non-uniformity at the inlet of the racks on the data center\u27s cooling infrastructure power consumption. The analysis enabled the development of a novel approach to reducing the energy consumption in enclosed aisle data centers using bypass recirculation. The development and experimental validation of a high-fidelity thermo-hydraulic model proceeded using the insights gained from the simple analysis. Using these tools, the study of optimum load placement is undertaken using computational fluid dynamics as the primary tool for analyzing the complex airflow and temperature patterns in the data center and is used to develop a rich dataset for the development of a reduced order model using proper orthogonal decomposition. The outcome of this work is the development of a robust set of rules that facilitate the energy efficient placement of the IT load amongst the operating servers in the data center and operation of the cooling infrastructure. The approach uses real-time temperature measurements at the inlet of the racks to remove IT load from the servers with the warmest inlet temperature (or add load to the servers with the coldest inlet temperature). These strategies are compared to conventional load placement techniques and show superior performance by considering the holistic optimization of the data center and cooling infrastructure for a range of data center IT utilization levels, operating strategies and ambient conditions

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Analysis of Research Topics and Scientific Collaborations in Energy Saving Using Bibliometric Techniques and Community Detection

Concern about everything related to energy is increasingly latent in the world and therefore the use of energy saving concepts has been increasing over the past several years. The interest in the subject has allowed a conceptual evolution in the scientific community regarding the understanding of the adequate use of energy. The objective of this work is to determine the contribution made by international institutions to the specialized publications in the area of energy-saving from 1939 to 2018, using Scopus Database API Interface. The methodology followed in this research was to perform a bibliometric analysis of the whole scientific production indexed in Scopus. The world’s scientific production has been analysed in the following domains: First the trend over time, types of publications and countries, second, the main subjects and keywords, third, main institutions and their main topics, and fourth, the main journals and proceedings that publish on this topic. Then, these data are presented using community detection algorithms and graph visualization software. With these techniques, it is possible to determine the main areas of research activity as well as to identify the structures of the collaboration network in the field of renewable energy. The results of the work show that the literature in this field have substantially increased during the last 10 years

FPGA Energy Efficiency by Leveraging Thermal Margin

Cutting edge FPGAs are not energy efficient as conventionally presumed to be, and therefore, aggressive power-saving techniques have become imperative. The clock rate of an FPGA-mapped design is set based on worst-case conditions to ensure reliable operation under all circumstances. This usually leaves a considerable timing margin that can be exploited to reduce power consumption by scaling voltage without lowering clock frequency. There are hurdles for such opportunistic voltage scaling in FPGAs because (a) critical paths change with designs, making timing evaluation difficult as voltage changes, (b) each FPGA resource has particular power-delay trade-off with voltage, (c) data corruption of configuration cells and memory blocks further hampers voltage scaling. In this paper, we propose a systematical approach to leverage the available thermal headroom of FPGA-mapped designs for power and energy improvement. By comprehensively analyzing the timing and power consumption of FPGA building blocks under varying temperatures and voltages, we propose a thermal-aware voltage scaling flow that effectively utilizes the thermal margin to reduce power consumption without degrading performance. We show the proposed flow can be employed for energy optimization as well, whereby power consumption and delay are compromised to accomplish the tasks with minimum energy. Lastly, we propose a simulation framework to be able to examine the efficiency of the proposed method for other applications that are inherently tolerant to a certain amount of error, granting further power saving opportunity. Experimental results over a set of industrial benchmarks indicate up to 36% power reduction with the same performance, and 66% total energy saving when energy is the optimization target.Comment: Accepted in IEEE International Conference on Computer Design (ICCD) 201

HSO: A Hybrid Swarm Optimization Algorithm for Re-Ducing Energy Consumption in the Cloudlets

Mobile Cloud Computing (MCC) is an emerging technology for the improvement of mobile service quality. MCC resources are dynamically allocated to the users who pay for the resources based on their needs. The drawback of this process is that it is prone to failure and demands a high energy input. Resource providers mainly focus on resource performance and utilization with more consideration on the constraints of service level agreement (SLA). Resource performance can be achieved through virtualization techniques which facilitates the sharing of resource providers’ information between different virtual machines. To address these issues, this study sets forth a novel algorithm (HSO) that optimized energy efficiency resource management in the cloud; the process of the proposed method involves the use of the developed cost and runtime-effective model to create a minimum energy configuration of the cloud compute nodes while guaranteeing the maintenance of all minimum performances. The cost functions will cover energy, performance and reliability concerns. With the proposed model, the performance of the Hybrid swarm algorithm was significantly increased, as observed by optimizing the number of tasks through simulation, (power consumption was reduced by 42%). The simulation studies also showed a reduction in the number of required calculations by about 20% by the inclusion of the presented algorithms compared to the traditional static approach. There was also a decrease in the node loss which allowed the optimization algorithm to achieve a minimal overhead on cloud compute resources while still saving energy significantly. Conclusively, an energy-aware optimization model which describes the required system constraints was presented in this study, and a further proposal for techniques to determine the best overall solution was also made