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Concern for the environment and energy security is changing the way we think about energy. Grid-enabled passenger vehicles, like electric vehicles (EV) and plug-in hybrid electric vehicles (PHEV) can help address environmental and energy issues. Automakers have recognized that electric drive vehicles are critical to the future of the industry. However, some challenges exist to greater adoption: the perception of cost, EV range, access to charging, potential impacts to the grid, and lack of public awareness about the availability and practicality of these vehicles. Although the current initial price for EV's is higher, their operating costs are lower. Policies that reduce the total cost of ownership of EVs and PHEVs, compared to conventional internal combustion engine (ICE) vehicles, will lead to faster market penetration. Greater access to charging infrastructure will also accelerate public adoption. Smart grid technology will optimize the vehicle integration with the grid, allowing intelligent and efficient use of energy. By coordinating efforts and using a systems perspective, the advantages of EVs and PHEVs can be achieved using the least resources. This paper analyzes these factors, their rate of acceleration and how they may synergistically align for the electrification of vehicles.

Vehicles, both personal and commercial, have become ubiquitous forms of transportation in the developed world. The auto industry is amidst a technological transformation in identifying alternative sources of energy to power vehicles due to two driving forces: environmental pollution prevention and depletion of fuel resources. This driver for developing “smarter” solutions to create a “smarter planet” is crucial to advancing the science behind electric vehicles (EVs). EVs have been in existence since the mid-19th century, and electric locomotion has been the commonplace in many other vehicle types such as trains. The focus of this chapter is to discuss the feasibility of EVs in smart cities. In particular, the chapter explores the types of EVs, advantages and challenges faced by EVs to penetrate the market, and to outline state-of-the-art research and technologies that are driving the creation of newer and better EVs for adoption in the smart cities of tomorrow.

Electrification for commercial vehicle fleets presents opportunity to cut emissions, reduce fuel costs, and improve operational metrics. However, infrastructure limitations in urban areas often inhibit the ability to charge a significant number of electric vehicles, especially under one roof. This paper highlights a novel controls approach developed at GE Global Research in conjunction with Columbia University to fulfill the stated needs for intelligent charging of a commercial fleet of electric vehicles. This novel approach combines traditional control techniques with machine learning algorithms to adapt to customer behavior over time. The stated controls system is designed to regulate the charging rate of multiple electric vehicle supply equipment devices (EVSEs) to facilitate cost-optimal charging subject to past and predicted building load, vehicle energy requirements, and current conditions. In this embodiment, the system is primarily designed to mitigate electric demand charges that may otherwise occur due to charging at inopportune times. The system will be deployed at a New York City FedEx Express delivery depot in partnership with the local utility, Consolidated Edison Company of New York.

A high percentage of newly-constructed commercial office buildings experience energy consumption that exceeds specifications and system failures after being put into use. This problem is even worse for older buildings. We present a new approach, ‘predictive building energy optimization’, which uses machine learning (ML) and automated online evaluation of historical and real-time building data to improve efficiency and reliability of building operations without requiring large amounts of additional capital investment. Our ML approach uses a predictive model to generate accurate energy demand forecasts and automated analyses that can guide optimization of building operations. In parallel, an automated online evaluation system monitors efficiency at multiple stages in the system workflow and provides building operators with continuous feedback. We implemented a prototype of this application in a large commercial building in Manhattan. Our predictive machine learning model applies Support Vector Regression (SVR) to the building's historical energy use and temperature and wet-bulb humidity data from the building's interior and exterior in order to model performance for each day. This predictive model closely approximates actual energy usage values, with some seasonal and occupant-specific variability, and the dependence of the data on day-of-the-week makes the model easily applicable to different types of buildings with minimal adjustment. In parallel, an automated online evaluator monitors the building's internal and external conditions, control actions and the results of those actions. Intelligent real-time data quality analysis components quickly detect anomalies and automatically transmit feedback to building management, who can then take necessary preventive or corrective actions. Our experiments show that this evaluator is responsive and effective in further ensuring reliable and energy-efficient operation of building systems.

An important problem in reliability engineering is to predict the failure rate, that is, the frequency with which an engineered system or component fails. This paper presents a new method of estimating failure rate using a semiparametric model with Gaussian process smoothing. The method is able to provide accurate estimation based on historical data and it does not make strong a priori assumptions of failure rate pattern (e.g., constant or monotonic). Our experiments of applying this method in power system failure data compared with other models show its efficacy and accuracy. This method can be used in estimating reliability for many other systems, such as software systems or components.

Power companies can benefit from the use of knowledge discovery methods and statistical machine learning for preventive maintenance. We introduce a general process for transforming historical electrical grid data into models that aim to predict the risk of failures for components and systems. These models can be used directly by power companies to assist with prioritization of maintenance and repair work. Specialized versions of this process are used to produce (1) feeder failure rankings, (2) cable, joint, terminator, and transformer rankings, (3) feeder Mean Time Between Failure (MTBF) estimates, and (4) manhole events vulnerability rankings. The process in its most general form can handle diverse, noisy, sources that are historical (static), semi-real-time, or real-time, incorporates state-of-the-art machine learning algorithms for prioritization (supervised ranking or MTBF), and includes an evaluation of results via cross-validation and blind test. Above and beyond the ranked lists and MTBF estimates are business management interfaces that allow the prediction capability to be integrated directly into corporate planning and decision support; such interfaces rely on several important properties of our general modeling approach: that machine learning features are meaningful to domain experts, that the processing of data is transparent, and that prediction results are accurate enough to support sound decision making. We discuss the challenges in working with historical electrical grid data that were not designed for predictive purposes. The “rawness” of these data contrasts with the accuracy of the statistical models that can be obtained from the process; these models are sufficiently accurate to assist in maintaining New York City's electrical grid.

Ranking problems arise in a wide range of real world applications where an ordering on a set of examples is preferred to a classification model. These applications include collaborative filtering, information retrieval and ranking components of a system by susceptibility to failure. In this paper, we present an ongoing project to rank the underground primary feeders of New York City's electrical grid according to their susceptibility to outages. We describe our framework and the application of machine learning ranking methods, using scores from Support Vector Machines (SVM), RankBoost and Martingale Boosting. Finally, we present our experimental results and the lessons learned from this challenging real-world application.

Abstract Tracking the flow of oil, gas, and water in real-time while reservoirs are being drained is now possible through the emergence of rugged downhole sensors, the Internet, high bandwidth satellite and fiber communications and rapid middleware wrapping of software applications. The question is what information delivers economic return on the investment required. The good news is that all along the value chain, prices are coming down because of commercial pressures from outside the energy business. For example, Direct PC will likely drop the cost and increase the bandwidth of satellite communications to remote locations by an order of magnitude over the next few years. Application The manufacturing process by which we produce energy is being reinvented like we have never before experienced in this business. The oil and gas field of the future will be part of a much larger Information Technology (IT) network. Each field will be a wired, internet-connected, real-time monitored, remotely controlled, electronic venture. Each well, pipeline, rig, production platform, compression facility, and even the pumpers themselves, will have a IP (Internet Protocol) address. Any browser on any laptop in the company (with proper password protection) can log onto the myoilcompany.com URL at any time of the day or night -- from anywhere in the world -- and visualize production, well test, logging, and other real-time measurements coming from the 4D monitoring of field performance. Results and Observations However, there are fundamental reasons why such an integrated IT system for the oil and gas field is a difficult mission for the energy industry to pull off successfully. Principal among them is that the "last mile," as it is called in the communications industry, (the field connectivity to the manufacturing facility itself) is about as difficult an IT environment as could be imagined. Other IT savvy manufacturers around the globe also deal with multiple vendors, millions of customers, and millions of parts as we do, but our SITE of manufacture of energy products is NOT usually in a metropolitan area with communications infrastructure in place. Instead, it is found in whatever water depth or remote geography (usually rural) the oil and gas happens to be discovered beneath. And then, of course, the "very last mile" to the resource itself is under the ground and cannot be visited, or even imaged clearly. Not only must energy companies deal with a constantly changing set of IT vendors and bandwidth constraints from field to field, but each must be specified from the start to operate continuously for 30+ years, as part of an internetconnected portfolio. In the future, oil and gas fields will form an information grid with other fields, pipelines, land, and seaborne traffic, refineries and storage facilities, etc. that form the complete enterprise of the company (Figure 1).

Abstract The oil industry is still staggering from the recent price collapse, with management energy focused on cutting costs and improving return on capital employed (ROCE). The major fiscal problem of the energy business is that it is not competitive as an investment vehicle when compared to other growth industries such as computing, the internet, and biomedicine, because our ROCE is so poor. While new exploration hotspots like offshore west Africa and the ultra deepwater Gulf of Mexico offer the promise to return >30% ROCE, refining and marketing is a drag at Thus, reservoir development plans that deliver cash when it is needed for a company are required, and in all-important fields, 4D Reservoir Management becomes essential. The costs of repeated acquisition of 3D seismic surveys and continuous downhole instrumentation and monitoring become cost effective near term investments when considered in this long-term cash flow framework. There are several examples from key fields in prime offshore areas where it has already been demonstrated that 4D Reservoir Management is a key to economic success of the basin. We will review a Gulf of Mexico field where 4D seismic monitoring produced significantly different drainage patterns from those expected, and early on in the life of the field, as well. Extraction of as much of the discovered oil and gas in known reservoirs is a critical capability that will be required to balance supply/demand in the 21st century. 4D Reservoir Management returns substantial capital versus that invested, and therefore is an essential component of responsible Business Unit management in the modern age. Introduction to Portfolio Management (PM) Oil and gas production companies make money based upon on their skills in identifying a portfolio of properties and utilizing technologies to discover, produce, and sell oil and gas produced from those properties in an optimal manner. The key ingredient to improved business performance is portfolio management, which allows a company to present the performance of all its producing properties and exploration targets in a normalized way.

4-D seismic reservoir simulation combines the analysis of 4-D (time-lapse) seismic changes measured in an oil and gas field with 3D elastic seismic modeling, reservoir characterization and fluid flow simulation to better understand drainage patterns of oil, gas and water into wells and to identify bypassed pay. We have solved this poorly constrained, inverse problem and arrived at a selfconsistent reservoir simulation that minimizes error and predicts 4-D seismic changes similar in space, time, and magnitude to those observed in a complex, intertwined, turbidite channel reservoir in the South Timbalier 295 field, Gulf of Mexico. Particle flow produced from the seismic reservoir simulation predicts that drainage was complex, with a pressure drop in the reservoir to below the bubble point producing a gas cap nearby the producing wells. Brightened seismic amplitudes both downdip and updip of the wells indicate that the gas coming out of solution filled the most permeable of the tubular turbidite channels. Poor oil migration downdip suggests the need for water injection, which the operator instituted in 1997 to recover this downdip oil and repressurize the reservoir. Additional production drilling must be placed precisely to target the tubular turbidite channels not filled with gas. The seismic reservoir simulation suggests that this enhanced recovery strategy should be successful. As we get better and better at 4-D seismic reservoir simulation, we should get more and more of the original oil-in-place from complex reservoirs.

Abstract We have imaged 4D seismic amplitude and impedance differences between 3D seismic surveys conducted in 1988, prior to production, and in 1994, when about half of the recoverable hydrocarbons had been produced, to attempt to quantify the variations in fluid saturations with time within turbidite reservoirs in a Gulf of Mexico oil field. The mission of this 4D seismic monitoring project was to develop the software tools and interpretation techniques 1) to isolate and interpret seismicchanges that have occurred in the field over time in terms of oil/gas/water changes in order to better understand the drainage of the field, and 2) to identify and locate bypassed pay. Two turbidite reservoirs of the South Timbalier 295 field, offshore Louisiana, Gulf of Mexico, provide contrasts in acoustic response that point to important lessons for 4D monitoring in other fields of the Gulf of Mexico. In the K-40reservoir, a "classic" updip water sweep pattern in the oil/water contact with time was deduced from the observation of the dimming of seismic amplitudes over time in this vigorous water-drive reservoir. A "finger" of dimmed seismic amplitudes that breaks updip of even the structurally highest perforations was observed between two of the producing wells. This finger may be related to sand quality and permeability pathways and/or to acoustic shadowing from brightening that occurred within the K-8/16 reservoir directly above. The K-40 sand is two-lobed, and our region growing analysis of 4D seismic changes suggests that water had migrated farther updip in the lower than in the upper lobe by 1994. In the K-8 reservoir sequence, complex depositional channeling appears to have isolated the sand from the regional aquifer system to produce a gas depletion drive reservoir. Volumetric region-growing about high seismic amplitude "seed" pointsproved very successful in mapping the turbidite fairways, but not in isolating the K-8 from the immediately underlying K-16 sand. Seismic differences between 1988 and 1994 indicate brightening over time occurred in the K-8 sand fairways, which is thought to be associated with increased exsolution of gas. A marked increase in GOR was observed between the times of the two seismic surveys in producing wells from the K-8 reservoir. Introduction The interpretation of drainage patterns and the tracking of the migration of oil, gas and water fronts in 4D, that is volumetrically with time, are valuable optimization tools for the economic development of producing oil and gas fields1. However, current production technology is predominantly 2D. Even when a 3D seismic survey is used, 2D map-based, interpretation "horizons" are derived and used by geologists, geophysicistsand, reservoir engineers, to plan and execute production decisions in virtually all currently active oil and gas fields. This process relies upon the accumulated experience-base of these integrated teams that use seismic, logging, pressure monitoring, and production histories to determine how oil, gas, and water move within reservoirs as production proceeds.

The fifth Archie Conference, "Visualization Technology to Find and Develop More Oil and Gas," brought together 130 scientists and technologists to review current and future visualization technologies that are being developed and used in the petroleum and other industries. Visualization in the oil and gas industry can be considered a tool for characterizing and understanding surface and subsurface phenomena. In addition to allowing one to view and more easily understand large quantities of data, visualization is dramatically enhancing communications, and thus interaction, among members of integrated exploration and development teams. Current and potential end-users of visualization technology consider the most important aspects to include common formats for data interchang , greater availability to consultants and independents (perhaps through PC-based visualization hardware and software), bigger bandwidth capabilities to drive more powerful machines, and the application of sensitivity analysis to document uncertainty in visualizations at all scales. Visualization technology is in its infancy, but growing so rapidly that it promises to have major impact on many aspects of the petroleum industry, from improved day-to-day communications to better technology transfer and more powerful interpretive capabilities, all of which can ultimately lead to better economic decision making.

A novel system for extracting information from stereotyped voice traffic is described. Off-the-air recordings of commercial air traffic control communications are interpreted in order to identify the flights present and determine the scenario (e.g., takeoff, landing) that they are following. The system combines algorithms from signal segmentation, speaker segregation, speech recognition, natural language parsing, and topic classification into a single system. Initial evaluation of the algorithm on data recorded at Dallas-Fort Worth airport yields performance of 68% detection of flights with 98% precision at an operating point where 76% of the flight identifications are correctly recognized. In tower recording containing both takeoff and landing scenarios, flights are correctly classified as takeoff or landing 94% of the time. >

Using the one-dimensional cumulus model developed by Cotton, predictions of the effects of seeding cumulus clouds were performed during the month of July 1973 as part of the Experimental Meteorology Laboratory's Florida Area Cumulus Experiment 1973 experiment. In Part I we compared seedability predictions with the Miami 1200 GMT soundings and soundings taken over the center of the experimental area (Central Site) at 1400 GMT. It was found that substantial differences between the two predictions occurred on a number of days in spite of the fact that the soundings are separated in time by only 2 h and in space by only 110 km. In this paper we compare seedability predictions with the MIA 1200 GMT soundings and the CS 1800 GMT soundings. The CS 1800 GMT soundings were assumed to be representative of conditions over the experimental area during the period of operation of the experiment. We found that the predictions with the MIA 1200 GMT soundings were, on the average, more representative of condition...

We are conducting exploratory research to investigate the instructional issues and educational benefits from introducing both a new paradigm and a new area of applied mathematics into the high school curriculum. The new paradigm is experimental mathematics and the new area is mathematical chaos. By experimental mathematics we mean computer modeling of mathematical processes to gain insight into their structure and behavior so as to inform and guide mathematical inquiry. Mathematical chaos is the study of orderly and chaotic behavior in nonlinear processes and in the real world systems modelled by them. Both depend fundamentally on the use of computers and interactive graphics technology.

Electric vehicles are not penetrating the market as quickly as expected. This is due to limited driving range, time required to recharge a battery, and lack of charging infrastructure in most metropolitan cities. We propose a charge sharing network in which we use inductive power transfer to wirelessly exchange charge between vehicles. In our network, vehicles that have excess charge to share, can sell charge to vehicles needing charge to reach their destination. In this paper, we describe a game theoretic approach to offering incentives for electric vehicles to participate in the charge sharing network. We utilize Nash Bargaining theory to show that participation in the network can yield profits for the seller driving to their destination and that we can increase the number of cars reaching their destination without needing to stop for recharging.

One of the most complex problems in earth sciences is that of the determination of fluid flow behavior such as flow pathways, flow velocity and/or hydrologic conductivity in a heterogeneous subsurface. Information on fluid flow behavior (both present and predicted) is needed for the determination of the size and shape of groundwater recharge areas, the assessment of contamination impact of spills and plumes and the planning and assessment of remediation efforts. For all these problems it is recognized that a good knowledge of the 3D values of hydrologic conductivity is essential, yet most often the only knowledge available on the hydrologic conductivity is that deduced from a number of sampling wells and pumping tests experiments. These experiments only give a (poor) bulk approximation of the hydrologic conductivity, and consequently the predictions of fluid flow behavior based on these approximations are more often than not unsuccessful.