Fernando Luiz Cyrino Oliveira

Optimal operation of hydrothermal systems with Hydrological Scenario Generation through Bootstrap and Periodic Autoregressive Models

In electrical power systems with strong hydro generation, the use of adequate techniques to generate synthetic hydrological scenarios is extremely important for the evaluation of the ways the system behaves in order to meet the forecast energy demand. This paper proposes a new model to generate natural inflow energy scenarios in the long-term operation planning of large-sized hydrothermal systems. This model is based on the Periodic Autoregressive Model, PAR (p), where the identification of the p orders is based on the significance of the Partial Autocorrelation Function (PACF) estimated via Bootstrap, an intensive computational technique. The scenarios generated through this new technique were applied to the operation planning of the Brazilian Electrical System (BES), using the previously developed methodology of Stochastic Dynamic Programming based on Convex Hull algorithm (SDP-CHull). The results show that identification via Bootstrap is considerably more parsimonious, leading to the identification of lower orders models in most cases which retains the statistical characteristics of the original series. Additionally it presents a closer total mean operation cost when compared to the cost obtained via historic series.

A new approach to identify the structural order of par (p) models

The periodic autoregressive model, a particular structure of the Box & Jenkins family, denoted by PAR (p), is employed to model the series of hydrological streamflow used for estimating the operational costs of the Brazilian hydro-thermal optimal dispatch. Recently, some aspects of this approachbegan to be studied and several researches on this topic are being developed. This paper focuses on the identification stage of the orders p of these models. Nowadays, the identification is based on evaluating the significance of the coefficients of the partial autocorrelation function (PACF), based on the asymptoticresults of Quenouille. The purpose of this study is on the application of the computer-intensive Bootstraptechnique to estimate the significance of such coefficients. The results show that identification via Bootstrap is considerably more parsimonious, leading to the identification of lower orders in most cases andcorroborating some points raised in previous studies on the traditional approach.

No-shows in Appointment Scheduling – a Systematic Literature Review

No-show appointments significantly impact the functioning of healthcare institutions, and much research has been performed to uncover and analyze the factors that influence no-show behavior. In spite of the growing body of literature on this issue, no synthesis of the state-of-the-art is presently available and no systematic literature review (SLR) exists that encompasses all medical specialties. This paper provides a SLR of no-shows in appointment scheduling in which the characteristics of existing studies are analyzed, results regarding which factors have a higher impact on missed appointment rates are synthetized, and comparisons with previous findings are performed. A total of 727 articles and review papers were retrieved from the Scopus database (which includes MEDLINE), 105 of which were selected for identification and analysis. The results indicate that the average no-show rate is of the order of 23%, being highest in the African continent (43.0%) and lowest in Oceania (13.2%). Our analysis also identified patient characteristics that were more frequently associated with no-show behavior: adults of younger age; lower socioeconomic status; place of residence is distant from the clinic; no private insurance. Furthermore, the most commonly reported significant determinants of no-show were high lead time and prior no-show history.

Systematic review and meta-regression analysis of technical efficiency in dairy farms

Purpose - – The purpose of this paper is to complement and extend previous literature reviews on Technical Efficiency (TE) in dairy farms, analysing the effects of different methodologies and study-specific characteristics on Mean TE (MTE). Design/methodology/approach - – The researchers independently conducted a systematic review of more than 400 abstracts and 85 full-text papers. Original keywords were applied to seven key electronic databases. Results from a meta-regression analysis of 85 published papers totalling 443 TE distributions in dairy farms worldwide are discussed. Findings - – The variation in the MTE indexes reported in the literature can be explained by the methodology of estimations (method of estimation, functional form of frontier models, model dimensionality), the farms geographical location and farm size. Additionally, the results suggest that, given the state of technology prevailing in each country at the time that the studies on TE were conducted, dairy farmers in the sample could increase milk output by 20.9 per cent (level of inefficiency), on average, if they produce on their frontiers. Originality/value - – This study makes two important contributions: first, it updates and compares previous works on frontier estimation of TE in dairy farms; and second, it adds two dimensions of dairy farms, size (herd and land area) and economic development, to the known differentials of TE measurement.

A Parsimonious Bootstrap Method to Model Natural Inflow Energy Series

The Brazilian energy generation and transmission system is quite peculiar in its dimension and characteristics. As such, it can be considered unique in the world. It is a high dimension hydrothermal system with huge participation of hydro plants. Such strong dependency on hydrological regimes implies uncertainties related to the energetic planning, requiring adequate modeling of the hydrological time series. This is carried out via stochastic simulations of monthly inflow series using the family of Periodic Autoregressive models, PAR(), one for each period (month) of the year. In this paper it is shown the problems in fitting these models by the current system, particularly the identification of the autoregressive order “” and the corresponding parameter estimation. It is followed by a proposal of a new approach to set both the model order and the parameters estimation of the PAR() models, using a nonparametric computational technique, known as Bootstrap. This technique allows the estimation of reliable confidence intervals for the model parameters. The obtained results using the Parsimonious Bootstrap Method of Moments (PBMOM) produced not only more parsimonious model orders but also adherent stochastic scenarios and, in the long range, lead to a better use of water resources in the energy operation planning.

INTRODUCING A CAUSAL PAR( p ) MODEL TO EVALUATE THE INFLUENCE OF CLIMATE VARIABLES IN RESERVOIR INFLOWS: A BRAZILIAN CASE

The Brazilian electricity energy matrix is essentially formed by hydraulic sources which currently account for 70% of the installed capacity. One of the most important characteristics of a generation system with hydro predominance is the strong dependence on the inflow regimes. Nowadays, the Brazilian power sector uses the PAR(p) model to generate scenarios for hydrological inflows. This approach does not consider any exogenous information that may affect hydrological regimes. The main objective of this paper is to infer on the influence of climatic events in water inflows as a way to improve the model’s performance. The proposed model is called “causal PAR(p)” and considers exogenous variables, such as El Nino and Sunspots, to generate scenarios for some Brazilian reservoirs. The result shows that the error measures decrease approximately 3%. This improvement indicates that the inclusion of climate variables to model and simulate the inflows time series is a valid exercise and should be taken into consideration.

Technical efficiency in dairy farms: Research framework, literature classification and research agenda

This systematic literature review integrates the findings of existing studies regarding technical efficiency (TE) in dairy farms. The purpose of this paper is to offer a research framework that assembles TE descriptors, a classification of previous literature that provides the basis for the synthesis and research agenda.,This paper systematically reviews 86 survey research studies using rigorous and reproducible procedures. The review is applied to published survey research.,The framework relates context, inputs, outputs and metrics of TE. There is no agreement among the authors on the context and determinants of TE. The main determinants of TE are geographical location, farm size, investments in veterinary care, feeding and milking practice, TE model estimation techniques, public policy, and management-related variables. This paper offers ten propositions for future research on the controversial results on the determinants of TE. The authors also explore the reasons for the discrepant results based on the Debreu-Farrell’s definition of TE, the contingency theory and the resource-based view of the firm, elucidating the literature and serving as a basis for future investigation. Implications for dairy farmers and researchers close the review.,Meta-analysis and meta-regression studies were long at the forefront of reviews in the TE of dairy farms. This paper offers a novel qualitative research synthesis with frameworks and the classification of previous literature and a research agenda, which provides a new and different perspective for analysis, by innovating over the available quantitative procedures to combine statistical results.

Wind speed time series analysis using TBATS decomposition and moving blocks bootstrap

The nature of the so called renewable energy (i.e. wind, solar, wave and tidal) led studies to refer to it as a stochastic generation processes and its dynamic behavior strengthens the need for robust and accurate analysis tools integrated to energy systems. The steadily increasing penetration of wind energy around the world raises a demand to better understand the behavior of historical data to infer indispensable information for the future market. This paper proposes an approach to analyze a wind speed time series using a recent decomposition method called TBATS and Moving Blocks Bootstrap for scenario generation. Results indicated complex multi-seasonality patterns as the simulated scenarios could represent statistically the mean; however, only 75% of the variance on the best scenario was explained, suggesting nonlinearity.

Another look at SSA.Boot forecast accuracy

A new approach that jointly uses Singular Spectrum Analysis, Bootstrap and the automatic procedures ets and auto.arima, called SSA.Boot, were successfully presented in previous works. Then, this works applies the SSA.Boot procedure to a new set of series to validate the method. The results founded, consistent with the approach presentation paper, shows that there is gain by incorporating synthetical series before forecasting. For all the six tested series the errors measures were small in the case where the SSA.Boot were used confirming that this is a promising methodology.

Forecast of Long-term Electricity Consumption of the Industrial Sub-sector of Pulp and Paper in Brazil Using a Bottom-up Approach☆

Abstract The subsector of pulp and paper presented in 2013 an electric power consumption of 9.3% of electricity consumption in the industrial sector in Brazil. It experienced a significant growth of production in the last 20 years, exceeding 160%, and their consumption of electricity increased by 130%, reaching the level of 19,594GWh. Due to its characteristics, with a known structure and production processes, and its importance in the countrys industrial sector, this subsector was selected for study in this paper, to evaluate the behavior of its long-term annual electricity demand, at the horizon of 20 years ahead, in energy efficiency scenarios through bottom-up approach. This approach is data intensive and requires data with good quality; it consists of a hierarchical structure that enables to check how specific actions can affect the results at each level and its effect on higher levels.