On Complexity, Energy- and Implementation-Efficiency of Channel Decoders

Future wireless communication systems require efficient and flexible baseband receivers. Meaningful efficiency metrics are key for design space exploration to quantify the algorithmic and the implementation complexity of a receiver. Most of the current established efficiency metrics are based on counting operations, thus neglecting important issues like data and storage complexity. In this paper we introduce suitable energy and area efficiency metrics which resolve the afore-mentioned disadvantages. These are decoded information bit per energy and throughput per area unit. Efficiency metrics are assessed by various implementations of turbo decoders, LDPC decoders and convolutional decoders. New exploration methodologies are presented, which permit an appropriate benchmarking of implementation efficiency, communications performance, and flexibility trade-offs. These exploration methodologies are based on efficiency trajectories rather than a single snapshot metric as done in state-of-the-art approaches.Comment: Submitted to IEEE Transactions on Communication

Metrics for driving political economy of energy and growth

Energy metrics is the development of a whole new theoretical framework for the conception and measurement of energy and economic performances, energy efficiency and productivity improvements with important political economy implications consistent with the best use of all natural and economic resources. The purpose of this research is to present some vital energy indicators based on magnitude and scale of energy weakness, GDP per barrel that is an indicator of energy productivity and barrels per capita that is an indicator of energy efficiency. Energy metrics can support policy maker to monitor energy system of countries in order to design effective strategy and political economy focused to increase the competitive advantage of countries in modern economies.Energy metrics, Energy productivity, Energy efficiency, Energy systems

Dynamic Daylight Metrics for Electricity Savings in Offices: Window Size and Climate Smart Lighting Management

Daylight performance metrics provide a promising approach for the design and optimization of lighting strategies in buildings and their management. Smart controls for electric lighting can reduce power consumption and promote visual comfort using different control strategies, based on affordable technologies and low building impact. The aim of this research is to assess the energy efficiency of these smart controls by means of dynamic daylight performance metrics, to determine suitable solutions based on the geometry of the architecture and the weather conditions. The analysis considers different room dimensions, with variable window size and two mean surface reflectance values. DaySim 3.1 lighting software provides the simulations for the study, determining the necessary quantification of dynamic metrics to evaluate the usefulness of the proposed smart controls and their impact on energy efficiency. The validation of dynamic metrics is carried out by monitoring a mesh of illuminance-meters in test cells throughout one year. The results showed that, for most rooms more than 3.00 m deep, smart controls achieve worthwhile energy savings and a low payback period, regardless of weather conditions and for worst-case situations. It is also concluded that dimming systems provide a higher net present value and allow the use of smaller window size than other control solutions

Energy Efficiency Metrics of University Data Centers

The data centers are fundamental pieces in the network and computing infrastructure, and evidently today more than ever they are relevant. Since they support the processing, analysis, assurance of the data generated in the network and by the applications in the cloud, which every day increases its volume thanks to technologies such as Internet of Things, Virtualization, and cloud computing, among others. Precisely the management of this large volume of information makes the data centers consume a lot of energy, generating great concern to owners and administrators. Green Data Centers offer a solution to this problem, reducing the impact produced by the data centers in the environment, through the monitoring and control of these. The metrics are the tools that allow us to measure in our case the energy efficiency of the data center and evaluate if it is friendly to the environment. These metrics will be applied to the data centers of the ITSA University Institution, Barranquilla and Soledad campus, and the analysis of these will be carried out. In previous research, the most common metric (PUE) was analyzed to measure the efficiency of the data centers, to verify if the University's data center is friendly to the environment. It is planned to extend this study by carrying out an analysis of several metrics to conclude which is the most efficient and which allows defining the guidelines to update or convert the data center in a friendly environment.

Energy and material efficiency metrics in foundries

Most of the current foundry processes are based on well-developed and established practices typical of mature technologies. Contemporary economic, environmental and societal developments have concurrently changed at an unprecedented rate the context where traditional metal casting methodologies have not really developed much over time. Consequently, significant challenges and opportunities arise. This work will present the founding metrics of a novel approach to metal casting with the development of a new philosophy (called “Small is Beautiful”) aimed at tackling the current pressures on the industry with a focus on energy and materials’ efficiencies and flexible production. Traditional and well-established parameters are presented and compared to new metrics defined from first principles and thermodynamic properties. All metrics are validated using industrial and scientific literature data of five sand casting plants melting different ferrous and non-ferrous alloys

Are energy poverty metrics fit for purpose? An assessment using behavioural microsimulation. ESRI Working Paper 665 May 2020.

We assess the existing battery of metrics for measuring energy affordability. We analyse expenditure-based metrics and recently-developed metrics for multidimensional poverty under simulated scenarios which allow for the introduction of carbon taxation, increased housing costs, revenue re-allocation and increases in energy efficiency. We deploy the Exact Affine Stone Index (EASI) implicit Marshallian demand system to parameterise a microsimulation model. Expenditure-based metrics used by official bodies in Europe perform very poorly in capturing the impacts of both carbon taxation and policy responses. Multidimensional poverty metrics provide more intuitive results. Evidence from these metrics show that revenue recycling can mitigate the impacts of increased energy and housing costs in the extensive and intensive margins of energy poverty, while energy efficiency can exacerbate the intensity of those already classified as “energy poor”

Review of distribution transformer energy efficiency metric

Distribution transformers play an important role in achieving the ambitious energy efficiency targets set by many countries in the world. Distribution transformers are of high value in terms of energy efficiency because of the number of installed units in each country. In this context, energy efficiency metrics have been introduced, such as efficiency at 50 % load, maximum no-load and load losses, maximum combined losses, etc. Typically, the selection of energy-efficient distribution transformer is a two-step process: a) transformers must comply with the minimum power efficiency levels (typically at 50 % load), b) selection of that transformer whose losses are economically optimal over the lifetime of the transformer. In this article, a review of the present energy performance metrics, especially the efficiency at 50 % load is presented, paying particular attention to the Australian and New Zealand market. Alternative metrics such as no-load and load loss values and peak efficiency index are investigated