Forecasting the state of health of electric vehicle batteries to evaluate the viability of car sharing practices

Car sharing practices are introducing electric vehicles into their fleet. However, literature suggests that at this point shared electric vehicle systems are failing to reach satisfactory commercial viability. Potential reason for this is the effect of higher vehicle usage which is characteristic for car sharing, and the implication on the battery state of health. In this paper, we forecast state of health for two identical electric vehicles shared by two different car sharing practices. For this purpose, we use real life transaction data from charging stations and different electric vehicles’ sensors. The results indicate that insight into users’ driving and charging behaviour can provide valuable point of reference for car sharing system designers. In particular, the forecasting results show that the moment when electric vehicle battery reaches its theoretical end of life can differ in as much as ¼ of time when vehicles are shared under different conditions

Results from the centers for disease control and prevention's predict the 2013-2014 Influenza Season Challenge

Background: Early insights into the timing of the start, peak, and intensity of the influenza season could be useful in planning influenza prevention and control activities. To encourage development and innovation in influenza forecasting, the Centers for Disease Control and Prevention (CDC) organized a challenge to predict the 2013-14 Unites States influenza season. Methods: Challenge contestants were asked to forecast the start, peak, and intensity of the 2013-2014 influenza season at the national level and at any or all Health and Human Services (HHS) region level(s). The challenge ran from December 1, 2013-March 27, 2014; contestants were required to submit 9 biweekly forecasts at the national level to be eligible. The selection of the winner was based on expert evaluation of the methodology used to make the prediction and the accuracy of the prediction as judged against the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). Results: Nine teams submitted 13 forecasts for all required milestones. The first forecast was due on December 2, 2013; 3/13 forecasts received correctly predicted the start of the influenza season within one week, 1/13 predicted the peak within 1 week, 3/13 predicted the peak ILINet percentage within 1 %, and 4/13 predicted the season duration within 1 week. For the prediction due on December 19, 2013, the number of forecasts that correctly forecasted the peak week increased to 2/13, the peak percentage to 6/13, and the duration of the season to 6/13. As the season progressed, the forecasts became more stable and were closer to the season milestones. Conclusion: Forecasting has become technically feasible, but further efforts are needed to improve forecast accuracy so that policy makers can reliably use these predictions. CDC and challenge contestants plan to build upon the methods developed during this contest to improve the accuracy of influenza forecasts. © 2016 The Author(s)

A new mixed multiplicative-additive model for seasonal adjusment

Usually, seasonal adjustment is based on time series models which decompose an unadjusted series into the sum or the product of four unobservable components (trendcycle, seasonal, working-day and irregular components). In the case of clearly weatherdependent output in the west German construction industry, traditional considerations lead to an additive model. However, this results in an over-adjustment of calendar effects. An alternative is a multiplicative-additive mixed model, the estimation of which is illustrated using X-12-ARIMA. Finally, the relevance of the new model is shown by analysing selected time series for different countries. --Seasonal adjustment,calendar adjustment,over-adjustment,multiplicative-additive model,X-12-ARIMA

Whither human survival and longevity or the shape of things to come

With the continuing increases in life expectancy, populations are ageing rapidly. Governments are concerned for the future of pensions and health care for which population forecasts are an important component for planning purposes. In this paper we focus on human survival rather than mortality rates which are the more usual starting point when estimating future populations. Using a simple model we link basic measures of life expectancy to the shape of the human survival function and consider its various forms. We then use the simple model as the basis for investigating actual survival in England and Wales from 1841 onwards and investigate the concept of a ‘maximum age’. We show how the model can be used in a predictive sense and demonstrate in two tests that show our model would have given more accurate results than comparable government forecasts using the same base information. We then go on to show that, based on trends in life expectancy, official population forecasts could undershoot the population at age 50+ by 0.6m, with consequent financial implications for pensions, health and social care

The miracle of the Septuagint and the promise of data mining in economics

This paper argues that the sometimes-conflicting results of a modern revisionist literature on data mining in econometrics reflect different approaches to solving the central problem of model uncertainty in a science of non-experimental data. The literature has entered an exciting phase with theoretical development, methodological reflection, considerable technological strides on the computing front and interesting empirical applications providing momentum for this branch of econometrics. The organising principle for this discussion of data mining is a philosophical spectrum that sorts the various econometric traditions according to their epistemological assumptions (about the underlying data-generating-process DGP) starting with nihilism at one end and reaching claims of encompassing the DGP at the other end; call it the DGP-spectrum. In the course of exploring this spectrum the reader will encounter various Bayesian, specific-to-general (S-G) as well general-to-specific (G-S) methods. To set the stage for this exploration the paper starts with a description of data mining, its potential risks and a short section on potential institutional safeguards to these problems.Data mining, model selection, automated model selection, general to specific modelling, extreme bounds analysis, Bayesian model selection

The ECB survey of professional forecasters (SPF) – A review after eight years’ experience

Eight years have passed since the European Central Bank (ECB) launched its Survey of Professional Forecasters (SPF). The SPF asks a panel of approximately 75 forecasters located in the European Union (EU) for their short- to longer-term expectations for macroeconomic variables such as euro area inflation, growth and unemployment. This paper provides an initial assessment of the information content of this survey. First, we consider shorter-term (i.e., one- and two-year ahead rolling horizon) forecasts. The analysis suggests that, over the sample period, in common with other private and institutional forecasters, the SPF systematically under-forecast inflation but that there is less evidence of such systematic errors for GDP and unemployment forecasts. However, these findings, which generally hold regardless of whether one considers the aggregate SPF panel or individual responses, should be interpreted with caution given the relatively short sample period available for the analysis. Second, we consider SPF respondents’ assessment of forecast uncertainty using information from heir probability distributions. The results suggest that, particularly at the individual level, SPF respondents do not seem to fully capture the overall level of macroeconomic uncertainty. Moreover, even at the aggregate level, a more sophisticated evaluation of the SPF density forecasts using the probability integral transform largely confirms this assessment. Lastly, we consider longer-term macroeconomic expectations from the SPF, where, as expectations cannot yet be assessed against so few actual realisations, we provide a mainly qualitative assessment. With regard to inflation, the study suggests that the ECB has been successful at anchoring longterm expectations at rates consistent with its primary objective to ensure price stability over the medium term. Long-term GDP expectations – which should provide an indication of the private sector’s assessment of potential growth – have declined over the sample period and the balance of risks reported by respondents has generally been skewed to the downside.

Optimal Rotational Load Shedding via Bilinear Integer Programming

This paper addresses the problem of managing rotational load shedding schedules for a power distribution network with multiple load zones. An integer optimization problem is formulated to find the optimal number and duration of planned power outages. Various types of damage costs are proposed to capture the heterogeneous load shedding preferences of different zones. The McCormick relaxation along with an effective procedure feasibility recovery is developed to solve the resulting bilinear integer program, which yields a high-quality suboptimal solution. Extensive simulation results corroborate the merit of the proposed approach, which has a substantial edge over existing load shedding schemes.Comment: 6 pages, 11 figures. To appear at the conference of APSIPA ASC 201