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This paper proposes the use of two indicators of the predictability of the load series along with an accuracy value such as mean average percentage error as standard measures of load forecasting performance. Over the last 10 years, there has been a significant increase in load forecasting models proposed in engineering journals. Most of these models provide a description of the inner design of the model, the results from applying this model to a specific data base and the conclusions drawn from this application. However, a single accuracy value may not be sufficient to describe the performance of the model when applied to other data bases. The aim of this paper is to provide researchers with a tool that is able to assess the predictability of a load series and, therefore, contextualize the forecasting accuracy reported. Nine different data bases from the U.S. have been used; all of them include hourly load and temperature data. [ABSTRACT FROM PUBLISHER]

This paper proposes a new framework in calculating the ex post prices for the spot electricity market. The proposed method finds a set of prices that are as consistent as possible with the actual outputs of resources correctly following their dispatch instructions. Two alternative consistency metrics are proposed in the paper. One measures the difference between the actual performance of an individual resource and its bid-based profit-maximizing response to the ex post prices in a perfectly competitive market. Another measures the difference between the ex post price and the price that supports the actual response of an individual resource. Compared to the existing approaches adopted in the real-time electricity markets in the U.S., the proposed method solves the ex post energy and ancillary service prices directly and simultaneously. It does not require heuristics that are hard to justify, and has a much clear definition for the pricing problem than the existing approaches do. Moreover, the proposed approach is flexible in the price control required by the market operators. Numerical examples are presented to further demonstrate the validity of this approach. [ABSTRACT FROM AUTHOR]

Human life expectancy has risen in most developed countries over several decades, causing observed demographic shifts. Many researchers have developed models to determine the expectancy of life at birth. Yet due to the complexity of the real-world mortality data, and recent global economy impacts, there is a great demand to search for new models to accurately predict the life expectancy, especially in the United States. In this paper, we focus on the analysis of mortality rate in the United States over a period of six decades (data from 1946 to 2005) with considerations of the six most common distribution functions in the mortality area. These functions are: Gompertz, Gompertz-Makeham, logistic, log logistic, loglog, and Weibull. Given complex mortality data, we develop models including algorithms to compute the mortality measures such as mortality rate, and then select the best function for predicting the life expectancy in the United States. The modeling results include life expectancy at birth, life expectancy at each age, mortality rate, and forecasting models. We also discuss a new risk-cost function with respect to life expectancy, and determine the optimal threshold lifetime level that maximizes the expected risk cost. Numerical examples are given to illustrate the expected risk-cost results. The proposed risk-cost optimization model, and the optimal lifetime threshold value can help the policy-decision makers of related healthcare organizations including insurance companies to carefully perform the tradeoff between the cost and benefits. This approach can affect both short term and long term related healthcare policies and plans. The results show that the logistic distribution uniquely out performs other distributions based on the mean square error criterion. We find that on average the life expectancy at birth in the United States in 2005 for both sexes, males, and females are 84.0, 81.9, and 84.6 years, respectively. This new result shows that the true life expectancy on average in the U.S. is significantly larger than in existing reports. Life expectancy obviously changes as one will get older. By late adulthood, a person's chances of living longer increase as is reflected by the life expectancy function. For example, although the life expectancy from birth in the United States in 2005 is 84.0 years, those who live to age 65 will have an average of 20.4 additional years left to live, making their life expectancy almost 85.4 years. Similarly, those who live to age 75 will gain an additional few years, making their life expectancy almost 87.6 years. On average, a child who will be born in the US in 2015 can expect to live 85.5 years, and in 2025 a child born can expect to live nearly 87 years. [ABSTRACT FROM AUTHOR]

Several information systems' and computer-mediated communication studies in the literature measure user's perceptions of email. The user's perceptions of email were used to develop and validate the Technology Acceptance Model (TAM). While a user's perceptions of email play an important role in literature, very few studies have focused solely on developing a construct for measuring these perceptions. In this paper, we develop a construct to measure an individual's perceptions of email as a medium for electronic communication in organizations. Using a survey of management and non management employees in Northeastern US, we empirically test our theoretical construct. The results of our research indicate that an individual's perceptions of email are a multidimensional construct with two dimensions: the individual level dimension and the organizational level. At an individual level, a person's perceptions may be impacted by email's role in improving productivity, supporting team work, and providing global reach. At an organizational level a person's perceptions may be impacted by email's role in making an organization vulnerable to viruses, exposing proprietary information, and/or encouraging unprofessional and illegal behavior. [ABSTRACT FROM AUTHOR]

This paper describes methods for automatically analyzing formal, state-based requirements specifications for some aspects of completeness and consistency. The approach uses a low-level functional formalism, simplifying the analysis process. State-space explosion problems are eliminated by applying the analysis at a high level of abstraction; i.e., instead of generating a reachability graph for analysis, the analysis is performed directly on the model. The method scales up to large systems by decomposing the specification into smaller, analyzable parts and then using functional composition rules to ensure that verified properties hold for the entire specification. The analysis algorithms and tools have been validated on TCAS II, a complex, airborne, collision-avoidance system required on all commercial aircraft with more than 30 passengers that fly in U.S. airspace. [ABSTRACT FROM AUTHOR]