Reliable and accurate planning and scheduling of wind farms and power grids to ensure sustainable use of wind energy can be better achieved with the use of precise and accurate prediction models. However, due to the highly chaotic, intermittent and stochastic behavior of wind, which means a high level of difficulty when predicting wind speed and, consequently, wind power, the evolution of models capable of narrating data of such a complexity is an emerging area of research. A thorough review of literature, present research overviews, and information about possible expansions and extensions of models play a significant role in the enhancement of the potential of accurate prediction models. The last few decades have experienced a remarkable breakthrough in the development of accurate prediction models. Among various physical, statistical and artificial intelligent models developed over this period, the models hybridized with pre-processing or/and post-processing methods have seen promising prediction results in wind applications. The present review is focused on hybrid empirical mode decomposition (EMD) or ensemble empirical mode decomposition (EEMD) models with their advantages, timely growth and possible future in wind speed and power forecasting. Over the years, the practice of EEMD based hybrid models in wind data predictions has risen steadily and has become popular because of the robust and accurate nature of this approach. In addition, this review is focused on distinct attributes including the evolution of EMD based methods, novel techniques of treating Intrinsic Mode Functions (IMFs) generated with EMD/EEMD and overview of suitable error measures for such studies.

Missing observations are common in time series data and several methods are available to impute these values prior to analysis. Variation in statistical characteristics of univariate time series can have a profound effect on characteristics of missing observations and, therefore, the accuracy of different imputation methods. The imputeTestbench package can be used to compare the prediction accuracy of different methods as related to the amount and type of missing data for a user-supplied dataset. Missing data are simulated by removing observations completely at random or in blocks of different sizes depending on characteristics of the data. Several imputation algorithms are included with the package that vary from simple replacement with means to more complex interpolation methods. The testbench is not limited to the default functions and users can add or remove methods as needed. Plotting functions also allow comparative visualization of the behavior and effectiveness of different algorithms. We present example applications that demonstrate how the package can be used to understand differences in prediction accuracy between methods as affected by characteristics of a dataset and the nature of missing data.

The use of pressure-reducing valves is an efficient pressure management technique for leakage reduction in a water distribution system. It is recommended to place an optimized number and location of pressure-reducing valves in the water distribution system for better sustainability and management. A modified reference pressure algorithm is adopted from the literature for identifying the optimized localization of valves using a simplified algorithm. The modified reference pressure algorithm fails to identify the optimal valve localization in a large-scale water pipeline network. Nodal matrix analysis is proposed for further improvement of the modified reference pressure algorithm. The proposed algorithm provides the preferred pipeline for valve location among all the pressure-reducing valve candidate locations obtained from the modified reference algorithm in complex pipeline networks. The proposed algorithm is utilized for pressure management in a real water network located in Piracicaba, Brazil, called Campos do Conde II. It identifies four pipeline locations as optimal valve candidate locations, compared to 22 locations obtained from the modified reference pressure algorithm. Thus, the presented technique led to a better optimal localization of valves, which contributes to better network optimization, sustainability, and management. The results of the current study evidenced that the adoption of the proposed algorithm leads to an overall reduction in water leakages by 20.08% in the water network.

Objectives: To observe the road traffic accidents pattern across the states in India and to conclude whether the expenditure on road and transportation infrastructure is really justified with the traffic safety across the country. To decide the road safety policies across the states in India using backward elimination approach of regression model. Methods/Analysis: This study attempts to show statistical relations between road traffic accidents with predictive variables; number of vehicles registered, total length of roads, total number of road accidents and total expenditure on transportation infrastructure in the states of India. Time series data covering the period 2001 to 2013 was used on yearly road traffic accidents and other predictive variables for all 28 states of India. Findings: In recent decades, road accidents are becoming one of the most growing concerns in India. As mentioned in report of Ministry of Road Transport & Highways Government of India, road accidents impose significant costs which are about 3% GDP for India since 1999 to 2000. In 2004, India was 9th leading cause of death and expected to be 5th leading cause of death by 2030 worldwide. These facts are enough to catch attention of Government agencies and researchers towards safer traffic solutions. The results from the analysis shows few findings like expenditure on transportation infrastructure may not be an important predictive variable for road traffic accidents for pan India situations. Finally, if all the states of India partitioned in different clusters according to the behavior of states, the study states that there exists a strong positive correlation between road traffic accidents, number of vehicles registered and total length of roads. The relationship found to be statistically significant at 5% level of significance for each cluster of states. Applications/Improvements: The results obtained in study can assist transportation departments and Government bodies to implement better strategies across the country to ensure road traffic safety and reduce losses in terms of money and infrastructure costs.

This paper describes the R package imputeTestbench that provides a testbench for comparing imputation methods for missing data in univariate time series. The imputeTestbench package can be used to simulate the amount and type of missing data in a complete dataset and compare filled data using different imputation methods. The user has the option to simulate missing data by removing observations completely at random or in blocks of different sizes. Several default imputation methods are included with the package, including historical means, linear interpolation, and last observation carried forward. The testbench is not limited to the default functions and users can add or remove additional methods using a simple two-step process. The testbench compares the actual missing and imputed data for each method with different error metrics, including RMSE, MAE, and MAPE. Alternative error metrics can also be supplied by the user. The simplicity of use and significant reduction in time to compare imputation methods for missing data in univariate time series is a significant advantage of the package. This paper provides an overview of the core functions, including a demonstration with examples.