Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

The Compact Muon Solenoid (CMS) detector is described. The detector operates at the Large Hadron Collider (LHC) at CERN. It was conceived to study proton-proton (and lead-lead) collisions at a centre-of-mass energy of 14 TeV (5.5 TeV nucleon-nucleon) and at luminosities up to 10(34)cm(-2)s(-1) (10(27)cm(-2)s(-1)). At the core of the CMS detector sits a high-magnetic-field and large-bore superconducting solenoid surrounding an all-silicon pixel and strip tracker, a lead-tungstate scintillating-crystals electromagnetic calorimeter, and a brass-scintillator sampling hadron calorimeter. The iron yoke of the flux-return is instrumented with four stations of muon detectors covering most of the 4 pi solid angle. Forward sampling calorimeters extend the pseudo-rapidity coverage to high values (vertical bar eta vertical bar <= 5) assuring very good hermeticity. The overall dimensions of the CMS detector are a length of 21.6 m, a diameter of 14.6 m and a total weight of 12500 t.

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The CMS experiment at the CERN LHC

An Introduction to MultiAgent Systems.

The Smart Grid, regarded as the next generation power grid, uses two-way flows of electricity and information to create a widely distributed automated energy delivery network. In this article, we survey the literature till 2011 on the enabling technologies for the Smart Grid. We explore three major systems, namely the smart infrastructure system, the smart management system, and the smart protection system. We also propose possible future directions in each system. colorred{Specifically, for the smart infrastructure system, we explore the smart energy subsystem, the smart information subsystem, and the smart communication subsystem.} For the smart management system, we explore various management objectives, such as improving energy efficiency, profiling demand, maximizing utility, reducing cost, and controlling emission. We also explore various management methods to achieve these objectives. For the smart protection system, we explore various failure protection mechanisms which improve the reliability of the Smart Grid, and explore the security and privacy issues in the Smart Grid.

/pdf/smart-grid-the-new-and-improved-power-grid-a-survey-4jypbbgv3j.pdf

Smart Grid — The New and Improved Power Grid: A Survey

Smart Grid - The New and Improved Power Grid:

Smart grid heavily relies on Information and Communications Technology (ICT) to manage the energy usage. The concept of smart grid implies the use of “smart” devices, such as smart meters or Remote Terminal Units (RTUs), that require extensive information to optimize the power grid. As the communication network is based on TCP/IP and Ethernet technology, new cyber vulnerabilities are introduced that can be exploited by malicious attackers. Cyber security has become a serious concern due to various intrusion incidents. Cyber attacks can make a significant impact on the grid, which will involve not only steady-state but also dynamic behaviors. A cyber-power system approach has been established that explicitly models the interaction between ICT and the power system. New technologies are under development to enhance the ICT vulnerability assessment and evaluate the impact of cyber attacks on system operation. This paper presents the cyber security issues in a smart grid environment and cyber attack/mitigation scenarios using a testbed at University College Dublin (UCD).

Cyber-power system security in a smart grid environment

In a market environment, profitability is the main objective for market players. This paper describes an environment in which suppliers sell electricity via a single auction system, e.g., the UK market. The suppliers submit bids to sell electricity. The market is cleared by maximizing social welfare, subject to power system operational constraints. The bidding decision is optimized from a single supplier's viewpoint. A probabilistic model is proposed for competing suppliers. Their bids are described by probability distributions constructed from decision-maker's observations. The single supplier's decision-making problem is solved by stochastic optimization. The expected value of the profit is calculated over the decision options. This study also shows that in a "multiplecommodity second price auction", the suppliers have an incentive to bid at marginal costs.

Decision making of an electricity supplier's bid in a spot market

Comprehensive, cross-disciplinary coverage of Smart Grid issues from global expert researchers and practitioners. This definitive reference meets the need for a large scale, high quality work reference in Smart Grid engineering which is pivotal in the development of a low-carbon energy infrastructure. Including a total of 83 articles across 3 volumes. The Smart Grid Handbook is organized in to 6 sections: Vision and Drivers, Transmission, Distribution, Smart Meters and Customers, Information and Communications Technology, and Socio-Economic Issues.

Smart Grid Handbook

Earlier work has discussed the potential for strategic bidding in deregulated electricity markets, and shown specially how generators can take advantage of congestion in their strategy. The authors show that it is also possible for even mid-price suppliers to create congestion problems through gaming in a noncongestive system. Under auction mechanisms such as in the United Kingdom, this can be profitable, at the consumer's expense. The optimal auction prevents profitable gaming, but requires the simultaneous handling of market clearing and system dispatch, making it harder to ensure the neutrality of system operations.

Analysis of electricity market rules and their effects on strategic behavior in a noncongestive grid

Disruption of electric power operations can be catastrophic on the national security and economy. Due to the complexity of widely dispersed assets and the interdependency between computer, communication, and power systems, the requirement to meet security and quality compliance on the operations is a challenging issue. In recent years, NERC's cybersecurity standard was initiated to require utilities compliance on cybersecurity in control systems - NERC CIP 1200. This standard identifies several cyber-related vulnerabilities that exist in control systems and recommends several remedial actions (e.g., best practices). This paper is an overview of the cybersecurity issues for electric power control and automation systems, the control architectures, and the possible methodologies for vulnerability assessment of existing systems.

Chen-Ching Liu

Papers

Cyber-power system security in a smart grid environment

Decision making of an electricity supplier's bid in a spot market

Smart Grid Handbook

Analysis of electricity market rules and their effects on strategic behavior in a noncongestive grid

Cybersecurity for electric power control and automation systems