Root Cause Analysis on Energy Efficiency with Transfer Entropy Flow

Energy efficiency is a big concern in industrial sectors. Finding the root cause of anomaly state of energy efficiency can help to improve energy efficiency of industrial systems and therefore save energy cost. In this research, we propose to use transfer entropy (TE) for root cause analysis on energy efficiency of industrial systems. A method, called TE flow, is proposed in that a TE flow from physical measurements of each subsystem to the energy efficiency indicator along timeline is considered as causal strength for diagnosing root cause of anomaly states of energy efficiency of a system. The copula entropy-based nonparametric TE estimator is used in the proposed method. We conducted experiments on real data collected from a compressing air system to verify the proposed method. Experimental results show that the TE flow method successfully identified the root cause of the energy (in)efficiency of the system.Comment: 13 pages, 2 figure

The VINEYARD Approach: Versatile, Integrated, Accelerator-Based, Heterogeneous Data Centres.

Emerging web applications like cloud computing, Big Data and social networks have created the need for powerful centres hosting hundreds of thousands of servers. Currently, the data centres are based on general purpose processors that provide high flexibility buts lack the energy efficiency of customized accelerators. VINEYARD aims to develop an integrated platform for energy-efficient data centres based on new servers with novel, coarse-grain and fine-grain, programmable hardware accelerators. It will, also, build a high-level programming framework for allowing end-users to seamlessly utilize these accelerators in heterogeneous computing systems by employing typical data-centre programming frameworks (e.g. MapReduce, Storm, Spark, etc.). This programming framework will, further, allow the hardware accelerators to be swapped in and out of the heterogeneous infrastructure so as to offer high flexibility and energy efficiency. VINEYARD will foster the expansion of the soft-IP core industry, currently limited in the embedded systems, to the data-centre market. VINEYARD plans to demonstrate the advantages of its approach in three real use-cases (a) a bio-informatics application for high-accuracy brain modeling, (b) two critical financial applications, and (c) a big-data analysis application

In-Memory Principal Component Analysis by Analogue Closed-Loop Eigendecomposition

Machine learning (ML) techniques such as principal component analysis (PCA) have become pivotal in enabling efficient processing of big data in an increasing number of applications. However, the data-intensive computation in PCA causes large energy consumption in conventional von Neumann computers. In-memory computing (IMC) significantly improves throughput and energy efficiency by eliminating the physical separation between memory and processing units. Here, we present a novel closed-loop IMC circuit to compute real eigenvalues and eigenvectors of a target matrix allowing IMC-based acceleration of PCA. We benchmark its performance against a commercial GPU, achieving comparable accuracy and throughput while simultaneously securing ×10000 energy and ×100÷10000 area efficiency improvements. These results support IMC as a leading candidate architecture for energy-efficient ML accelerators

Data Placement for Privacy-Aware Applications over Big Data in Hybrid Clouds

Nowadays, a large number of groups choose to deploy their applications to cloud platforms, especially for the big data era. Currently, the hybrid cloud is one of the most popular computing paradigms for holding the privacy-aware applications driven by the requirements of privacy protection and cost saving. However, it is still a challenge to realize data placement considering both the energy consumption in private cloud and the cost for renting the public cloud services. In view of this challenge, a cost and energy aware data placement method, named CEDP, for privacy-aware applications over big data in hybrid cloud is proposed. Technically, formalized analysis of cost, access time, and energy consumption is conducted in the hybrid cloud environment. Then a corresponding data placement method is designed to accomplish the cost saving for renting the public cloud services and energy savings for task execution within the private cloud platforms. Experimental evaluations validate the efficiency and effectiveness of our proposed method

Applying Big Data analytics for energy efficiency.

Global energy requirements are continuously increasing. Conventional methods of producing more energy to meet this growth pose a great threat to the environment. CO2 emissions and other bi-products of energy production and distribution processes have dire consequences for the environment. Efficient use of energy is one of the main tools to restrain energy consumption growth without compromising on the customers requirements. Improving energy efficiency requires understanding of the usage patterns and practices. Smart energy grids, pervasive computing, and communication technologies have enabled the stakeholders in the energy industry to collect large amounts of useful and highly granular energy usage data. This data is generated in large volumes and in a variety of different formats depending on its purpose and systems used to collect it. The volume and diversity of data also increase with time.\ All these data characteristics refer to the application of Big Data. This thesis focuses on harnessing the power of Big Data tools and techniques such as MapReduce and Apache Hadoop ecosystem tools to collect, process and analyse energy data and generate insights that can be used to improve energy efficiency. Furthermore, it also includes studying energy efficiency to formulate the use cases, studying Big Data technologies to present a conceptual model for an end-to-end Big Data analytics platform, implementation of a part of the conceptual model with the capacity to handle energy efficiency use cases and performing data analysis to generate useful insights. The analysis was performed on two data sets. The first data set contained hourly consumption of electricity consumed by a set of different buildings. The data was analysed to discover the seasonal and daily usage trends. The analysis also includes the classification of buildings on the basis of energy efficiency while observing the seasonal impacts on this classification. The analysis was used to build a model for segregating the energy inefficient buildings from energy efficient buildings. The second data set contained device level electricity consumption of various home appliances used in an apartment. This data was used to evaluate different prediction models to forecast future consumption on the basis of previous usage. The main purpose of this research is to provide the basis for enabling data driven decision making in organizations working to improve energy efficiency

Adaptive energy consumption optimization using IoT-based wireless sensor networks and structural health monitoring systems

Recent developments in combining sensors, communication systems, and other fields such as cloud com- puting and Big Data analysis have provided the perfect tools for researchers, developers, and industries to develop cutting edge systems for improving energy efficiency and consumption. Smart homes, smart sensors, and Internet of Things (IoT) are just a few examples of these technologies that will lead to more sustainable and more resilient energy systems. This paper presents a new system based on a network of wearable devices, customized structural monitoring systems, and other sensors which have been devel- oped to make energy consumption optimization more intelligent, adaptive, and efficient. Current indus- trial solutions for improving energy efficiency and energy consumption in the residential and industrial buildings, especially large ones, do not provide ideal energy efficiency besides having no proper level of comfort for the inhabitants. In the present work, users' location, body temperature, their feedback, and data from the other energy monitoring sensors are processed to minimize the energy consumption based on the current status of the system and to maximize the level of comfort

Power-Efficient and Highly Scalable Parallel Graph Sampling using FPGAs

Energy efficiency is a crucial problem in data centers where big data is generally represented by directed or undirected graphs. Analysis of this big data graph is challenging due to volume and velocity of the data as well as irregular memory access patterns. Graph sampling is one of the most effective ways to reduce the size of graph while maintaining crucial characteristics. In this paper we present design and implementation of an FPGA based graph sampling method which is both time- and energy-efficient. This is in contrast to existing parallel approaches which include memory-distributed clusters, multicore and GPUs. Our strategy utilizes a novel graph data structure, that we call COPRA that allows time- and memory-efficient representation of graphs suitable for reconfigurable hardware such as FPGAs. Our experiments show that our proposed techniques are 2x faster and 3x more energy efficient as compared to serial CPU version of the algorithm. We further show that our proposed techniques give comparable speedups to GPU and multi-threaded CPU architecture while energy consumption is 10x less than GPU and 2x less than CPU

Foreword

This thesis summarises the outcomes of a Big Data analysis, performed on a set of hourly district heating energy consumption data from 2012 for nearly 15 000 buildings in the City of Stockholm. The aim of the study was to find patterns and inefficiencies in the consumption data using KNIME, a big data analysis tool, and to initiate a retrofitting plan for the city to counteract these inefficiencies. By defining a number of energy saving scenarios, the potential for increased efficiency is estimated and the resulting methodology can be used by other (smart) cities and policy makers to estimate savings potential elsewhere. In addition, the influence of weather circumstances, building location and building types is studied. In the introduction, a concise overview of the concepts Smart City and Big Data is given, together with their relevance for the energy challenges of the 21st century. Thereafter, a summary of the previous studies at the foundation of this research and a brief theory review of less common methods used in this thesis are presented. The method of this thesis consisted of first understanding and describing the dataset using descriptive statistics, studying the annual fluctuations in energy consumption and clustering all consumer groups per building class according to total consumption, consumption intensity and time of consumption. After these descriptive steps, a more analytical part starts with the definition of a number of energy saving scenarios. They are used to estimate the maximal potential for energy savings, regardless of actual measures, financial or temporal aspects. This hypothetical simulation is supplemented with a more realistic retrofitting plan that explores the feasibility of Stockholm’s Climate Action Plan for 2012-2015, using a limited set of energy efficiency measures and a fixed investment horizon. The analytical part is concluded with a spatial regression that sets out to determine the influence of wind velocity and temperature in different parts of Stockholm. The conclusions of this thesis are that the potential for energy savings in the studied data set can go up to 59% or 4.6 TWh. The financially justified savings are estimated at ca. 6% using favourable investment parameters. However, these savings quickly diminish because of a high sensitivity on the input parameters. The clustering analysis has not yielded the anticipated results, but they can be used as a tool to target investments towards groups of buildings that have a high return on investment.