Uncertainty handling using neural network-based prediction intervals for electrical load forecasting

The complexity and level of uncertainty present in operation of power systems have significantly grown due to penetration of renewable resources. These complexities warrant the need for advanced methods for load forecasting and quantifying uncertainties associated with forecasts. The objective of this study is to develop a framework for probabilistic forecasting of electricity load demands. The proposed probabilistic framework allows the analyst to construct PIs (prediction intervals) for uncertainty quantification. A newly introduced method, called LUBE (lower upper bound estimation), is applied and extended to develop PIs using NN (neural network) models. The primary problem for construction of intervals is firstly formulated as a constrained single-objective problem. The sharpness of PIs is treated as the key objective and their calibration is considered as the constraint. PSO (particle swarm optimization) enhanced by the mutation operator is then used to optimally tune NN parameters subject to constraints set on the quality of PIs. Historical load datasets from Singapore, Ottawa (Canada) and Texas (USA) are used to examine performance of the proposed PSO-based LUBE method. According to obtained results, the proposed probabilistic forecasting method generates well-calibrated and informative PIs. Furthermore, comparative results demonstrate that the proposed PI construction method greatly outperforms three widely used benchmark methods. © 2014 Elsevier Ltd.

A novel fuzzy multi-objective framework to construct optimal prediction intervals for wind power forecast

The forecasting behavior of the high volatile and unpredictable wind power energy has always been a challenging issue in the power engineering area. In this regard, this paper proposes a new multi-objective framework based on fuzzy idea to construct optimal prediction intervals (Pis) to forecast wind power generation more sufficiently. The proposed method makes it possible to satisfy both the PI coverage probability (PICP) and PI normalized average width (PINAW), simultaneously. In order to model the stochastic and nonlinear behavior of the wind power samples, the idea of lower upper bound estimation (LUBE) method is used here. Regarding the optimization tool, an improved version of particle swam optimization (PSO) is proposed. In order to see the feasibility and satisfying performance of the proposed method, the practical data of a wind farm in Australia is used as the case study.

Constructing optimal prediction intervals by using neural networks and bootstrap method

This brief proposes an efficient technique for the construction of optimized prediction intervals (PIs) by using the bootstrap technique. The method employs an innovative PI-based cost function in the training of neural networks (NNs) used for estimation of the target variance in the bootstrap method. An optimization algorithm is developed for minimization of the cost function and adjustment of NN parameters. The performance of the optimized bootstrap method is examined for seven synthetic and real-world case studies. It is shown that application of the proposed method improves the quality of constructed PIs by more than 28% over the existing technique, leading to narrower PIs with a coverage probability greater than the nominal confidence level.

Improving the quality of prediction intervals through optimal aggregation

Neural networks (NNs) are an effective tool to model nonlinear systems. However, their forecasting performance significantly drops in the presence of process uncertainties and disturbances. NN-based prediction intervals (PIs) offer an alternative solution to appropriately quantify uncertainties and disturbances associated with point forecasts. In this paper, an NN ensemble procedure is proposed to construct quality PIs. A recently developed lower-upper bound estimation method is applied to develop NN-based PIs. Then, constructed PIs from the NN ensemble members are combined using a weighted averaging mechanism. Simulated annealing and a genetic algorithm are used to optimally adjust the weights for the aggregation mechanism. The proposed method is examined for three different case studies. Simulation results reveal that the proposed method improves the average PI quality of individual NNs by 22%, 18%, and 78% for the first, second, and third case studies, respectively. The simulation study also demonstrates that a 3%-4% improvement in the quality of PIs can be achieved using the proposed method compared to the simple averaging aggregation method.

Haematological and biochemical reference intervals for farmed channel catfish

The reference intervals for biochemical variables and red blood cell indices of healthy intensively bred channel catfish Ictalurus punctatus were determined. The blood variables were determined using standardized clinical methods. The reference intervals (25th and 75th percentiles) were established using a non-parametric method. Reference intervals for plasma glucose, serum total protein, sodium, potassium, calcium, magnesium, chloride concentration, primary and secondary red blood cell indices were established. The haematological and biochemical reference intervals established may allow important clinical decisions about channel catfish. (c) 2007 the Authors Journal compilation (C) 2007 the Fisheries Society of the British Isles.

Effects of different sampling intervals on apparent protein and energy digestibility of common feed ingredients by juvenile oscar fish (Astronotus ocellatus)

O presente estudo avaliou a digestibilidade aparente da proteína e da energia de ingredientes (farelo de soja, farinha de peixe, farelo de trigo e milho) por juvenis de apaiari (Astronotus ocellatus) usando dois diferentes intervalos de coleta (30 min. e 12h). Os 160 juvenis de apaiari utilizados (22,37 ± 3,06 g de peso corporal) foram divididos em quatro tanques rede plásticos e cilíndricos, cada um colocado em um tanque de alimentação de 1.000 L. O experimento foi inteiramente casualizado em esquema fatorial 2 x 4 (2 intervalos de coleta de fezes e 4 ingredientes foram) com quatro repetições. Os testes estatísticos não detectaram efeito da interação entre o intervalo de coleta e tipo de ingrediente nos coeficientes de digestibilidade. O intervalo de coleta não afetou a digestibilidade da proteína e da energia. As características físicas das fezes dos juvenis de apaiari aparentemente as tornam menos sensíveis à perda de nutrientes por lixiviação, permitindo intervalos de coleta maiores. A digestibilidade da proteína dos ingredientes avaliados foi semelhante, mostrando que a digestibilidade aparente de ingredientes animais e vegetais por juvenis de apaiari é eficiente. Os coeficientes de digestibilidade da energia foram maiores para a farinha de peixe e o farelo de soja comparado a farelo de trigo e milho. Ingredientes ricos em carboidratos (farelo de trigo e milho) apresentaram os piores coeficientes de digestibilidade da energia e, portanto, não são usados eficientemente pelos juvenis de apaiari.

Parturition intervals and distributions of parturitions by months of buffalo in Southern and South-eastern Brazil

Reproductive rate is an important component of economic success in livestock production. Parturition interval (IEP) is a direct measure of the productivity of the animal. Long IEP reduce the number of calves produced per year. The objective this study was to determine the distribution of parturitions across month and to evaluate factors affecting IEP. The data included 7,588 parturitions of Murrah, Mediterranean and Carabobo buffalo from 10 herds in Southern and South-eastern Brazil. The analysis of distribution of parturitions evaluated the effects of month, year and their interaction on birth date of calves by using a Chi-Square test in SAS PROC FREQ (SAS Institute, Cary, NC, USA). Parturition intervals (n = 2,630) were evaluated using analysis of variance in SAS PROC GLM. The model for IEP included the fixed effects of season (December to May = 1, June to November = 2), year, season x year, sex of the preceding parturition, age of weaning of the previous calf, and herd. All sources of variation were significant (P<0.0001) except sex of the preceding parturition (P <0.85). The mean IEP was 446.7 +/- 10.4 days, for seasons 1 and 2 IEP were 419.8 +/- 11.3 and 473.6 +/- 40.7 days, respectively, a difference of 54 days. As weaning age increased there was a lengthening of IEP. Buffalo in Brazil showed seasonal parturition with calving concentrated from January to April, although the frequency by month differed across years (P<0.0001). These months also had the lowest calving interval.

The efficacy of a single chain recombinant equine luteinizing hormone (reLH) in mares: Induction of ovulation, hormone profiles, and inter-ovulatory intervals

The objectives of this study were to determine the efficacy of recombinant equine luteinizing hormone (reLH) in shortening the time to ovulation in cycling mares and to determine the effects of treatment on endogenous hormones and inter-ovulatory intervals. In study 1, mares of light horse breeds (3-20 years) were treated with either a vehicle, various doses of reLH, or human chorionic gonadotropin (hCG). Cycling mares were examined by palpation and ultrasound per rectum daily or every 12 h from the time of treatment to ovulation. In studies 2 and 3, jugular blood samples were collected daily or every 12 h from the time of treatment to ovulation for analysis of LH, follicle stimulating hormone (FSH), estradiol-17 beta (E-2), and progesterone (P-4) by radioimmunoassays (RIA). Increasing doses of reLH (0.3, 0.6, 0.75, and 0.9 mg) showed increasing effectiveness at inducing ovulation within 48 h of treatment. Treatments with the 0.75 and 0.9 mg doses of reLH resulted in 90% and 80% ovulation rates, which were similar to hCG treatment (85.7%). Except for the early rise in LH after treatment with 0.5, 0.65, and 1.0 mg of reLH, hormone profiles appeared to be similar between control and treated cycles. Inter-ovulatory intervals were similar between control and treatment cycles. In conclusion, reLH is a reliable and effective ovulatory agent that does not significantly alter endogenous hormone profiles or affect inter-ovulatory intervals.(c) 2007 Elsevier B.V. All rights reserved.

Influence of Remineralizing Gels on Bleached Enamel Microhardness In Different Time Intervals

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Joint (X)over-bar and R charts with variable sample sizes and sampling intervals

Recent studies have shown that the (X) over bar chart with variable sampling intervals (VSI) and/or with variable sample sizes (VSS) detects process shifts faster than the traditional (X) over bar chart. This article extends these studies for processes that are monitored by both the (X) over bar and R charts. A Markov chain model is used to determine the properties of the joint (X) over bar and R charts with variable sample sizes and sampling intervals (VSSI). The VSSI scheme improves the joint (X) over bar and R control chart performance in terms of the speed with which shifts in the process mean and/or variance are detected.

Genetic and environmental effects on parturition and lactation intervals in water buffaloes from Brazil

Genetic and environmental effects on parturition interval (PI) and duration of lactation (DL) were evaluated in 107 Jafarabadi, 98 Mediterranean, 1027 Murrah and 624 crossbred buffalo females (n=1856), based on data from the Buffalo Genetic Improvement Program-PROMEBUL from 1980 to 2003. The statistical model included effects of herd, parturition year and month, calf's sex, parturition order and genetic group, composing 11, 34, 12, 2, 12 and 4 classes, respectively. A significant effect over PI was observed (P<0.01) in all classes, excepting sex. Mean parturition intervals per genetic group presented significant differences through SNK test (P<0.05), with mean values of 451.29; 429.47; 406.97 and 389.78 days in Mediterranean, crossbred, Murrah and Jafarabadi, respectively. Mean DL values were 276.68; 270.33; 258.03 and 235.59 days for Mediterranean, Murrah, crossbred and Jafarabadi groups, respectively. No significant differences in DL were observed in relation to genetic groups. However, herd and parturition order, year and month significantly influenced DL (P<0.01). The herd was the main source of variation over DL, followed by parturition year and month. A regression based on parturition month in relation to PI and DL showed that females giving birth in the last months of the year presented higher PI and DL.

X̄ chart with variable sample size and sampling intervals

Recent theoretical studies have shown that the X̄ chart with variable sampling intervals (VSI) and the X̄ chart with variable sample size (VSS) are quicker than the traditional X̄ chart in detecting shifts in the process. This article considers the X̄ chart with variable sample size and sampling intervals (VSSI). It is assumed that the amount of time the process remains in control has exponential distribution. The properties of the VSSI X̄ chart are obtained using Markov chains. The VSSI X̄ chart is even quicker than the VSI or VSS X̄ charts in detecting moderate shifts in the process.