A hybrid PSO-ANFIS approach for short-term wind power prediction in Portugal

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Wind power prediction plays a key role in tackling these challenges. The contribution of this paper is to propose a new hybrid approach, combining particle swarm optimization and adaptive-network-based fuzzy inference system, for short-term wind power prediction in Portugal. Significant improvements regarding forecasting accuracy are attainable using the proposed approach, in comparison with the results obtained with five other approaches.

An artificial neural network approach for short-term wind power forecasting in Portugal

This paper presents an artificial neural network approach for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. The accuracy of the wind power forecasting attained with the proposed approach is evaluated against persistence and ARIMA approaches, reporting the numerical results from a real-world case study.

Nonlinear optimization method for short-term hydro scheduling considering head-dependency

This paper is on the problem of short-term hydro scheduling, particularly concerning head-dependent reservoirs under competitive environment. We propose a new nonlinear optimization method to consider hydroelectric power generation as a function of water discharge and also of the head. Head-dependency is considered on short-term hydro scheduling in order to obtain more realistic and feasible results. The proposed method has been applied successfully to solve a case study based on one of the main Portuguese cascaded hydro systems, providing a higher profit at a negligible additional computation time in comparison with a linear optimization method that ignores head-dependency.

Short-term electricity prices forecasting in a competitive market by a hybrid PSO-ANFIS approach

In this paper, a novel hybrid approach is proposed for electricity prices forecasting in a competitive market, considering a time horizon of 1 week. The proposed approach is based on the combination of particle swarm optimization and adaptive-network based fuzzy inference system. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications, to demonstrate its effectiveness regarding forecasting accuracy and computation time. Finally, conclusions are duly drawn. (C) 2012 Elsevier Ltd. All rights reserved.

Application of adaptive neuro-fuzzy inference for wind power short-term forecasting

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. In this paper, an adaptive neuro-fuzzy inference approach is proposed for short-term wind power forecasting. Results from a real-world case study are presented. A thorough comparison is carried out, taking into account the results obtained with other approaches. Numerical results are presented and conclusions are duly drawn. (C) 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Análise de desempenho em redes móveis Long Term Evolution (Parceria CELFINET)

A rede móvel Long Term Evolution (LTE) é uma tecnologia que está a ser fortemente implementada, não só em Portugal mas no resto do mundo. A adoção do LTE deve-se em grande parte à maior capacidade e à baixa latência oferecidas, para além de ser expansível ao LTE-Advanced. O trabalho apresentado tem por objetivo a análise do desempenho de uma rede LTE piloto e comparar os resultados com o teoricamente expectável. Foi adotada uma metodologia de planeamento em LTE e comprovada através das medidas empíricas realizadas. Dessas medições são também sugeridos dois novos modelos de propagação para LTE nos 2,6 GHz. Para distâncias inferiores a 1 km sugere-se o modelo LTE-PL. Para distâncias superiores a 1 km foi feita uma adaptação ao modelo Okumura-Hata para que se aproximasse aos resultados obtidos. Das medições efetuadas observou-se que em boas condições rádio, os débitos bináriossão bastante próximos dos máximos teóricos. Além disso foi obtido o desvio padrão em LTE de uma área Urbano Denso de 12 dB. Foi ainda possível definir uma margem para as perdas de penetração in-car de 2,7 dB. Efetuou-se uma análise de vários Key Performance Indicators que permitem avaliar o desempenho do LTE, tendo também sido definidas categorias de qualidade de serviço. Por último foi avaliado o impacto da velocidade e da distância, pelas medidas realizadas.

Hybrid Wavelet-PSO-ANFIS Approach for Short-Term Electricity Prices Forecasting

A novel hybrid approach, combining wavelet transform, particle swarm optimization, and adaptive-network-based fuzzy inference system, is proposed in this paper for short-term electricity prices forecasting in a competitive market. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Finally, conclusions are duly drawn.

Short-term electricity prices forecasting in a competitive market by a hybrid intelligent approach

In this paper, a hybrid intelligent approach is proposed for short-term electricity prices forecasting in a competitive market. The proposed approach is based on the wavelet transform and a hybrid of neural networks and fuzzy logic. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Conclusions are duly drawn. (C) 2010 Elsevier Ltd. All rights reserved.

Short-term Feature Space and Music Genre Classification

In music genre classification, most approaches rely on statistical characteristics of low-level features computed on short audio frames. In these methods, it is implicitly considered that frames carry equally relevant information loads and that either individual frames, or distributions thereof, somehow capture the specificities of each genre. In this paper we study the representation space defined by short-term audio features with respect to class boundaries, and compare different processing techniques to partition this space. These partitions are evaluated in terms of accuracy on two genre classification tasks, with several types of classifiers. Experiments show that a randomized and unsupervised partition of the space, used in conjunction with a Markov Model classifier lead to accuracies comparable to the state of the art. We also show that unsupervised partitions of the space tend to create less hubs.

Short-term wind power forecasting in Portugal by neural networks and wavelet transform

This paper proposes artificial neural networks in combination with wavelet transform for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. Results from a real-world case study are presented. A comparison is carried out, taking into account the results obtained with other approaches. Finally, conclusions are duly drawn. (C) 2010 Elsevier Ltd. All rights reserved.

The adjusted effect of maternal body mass index, energy and macronutrient intakes during pregnancy, and gestational weight gain on body composition of full-term neonates

Objective - To evaluate the effect of prepregnancy body mass index (BMI), energy and macronutrient intakes during pregnancy, and gestational weight gain (GWG) on the body composition of full-term appropriate-for-gestational age neonates. Study Design - This is a cross-sectional study of a systematically recruited convenience sample of mother-infant pairs. Food intake during pregnancy was assessed by food frequency questionnaire and its nutritional value by the Food Processor Plus (ESHA Research Inc, Salem, OR). Neonatal body composition was assessed both by anthropometry and air displacement plethysmography. Explanatory models for neonatal body composition were tested by multiple linear regression analysis. Results - A total of 100 mother-infant pairs were included. Prepregnancy overweight was positively associated with offspring weight, weight/length, BMI, and fat-free mass in the whole sample; in males, it was also positively associated with midarm circumference, ponderal index, and fat mass. Higher energy intake from carbohydrate was positively associated with midarm circumference and weight/length in the whole sample. Higher GWG was positively associated with weight, length, and midarm circumference in females. Conclusion - Positive adjusted associations were found between both prepregnancy BMI and energy intake from carbohydrate and offspring body size in the whole sample. Positive adjusted associations were also found between prepregnancy overweight and adiposity in males, and between GWG and body size in females.

Does the intake of LC-PUFAS during pregnancy affect the body composition of term neonates? Preliminary results

Background: The effect of the intake of polynsaturated long chain fatty acids (LCPUFAs) during pregnancy on fetal body composition has been assessed by studies using mostly neonatal anthropometry. Their results have been inconsistent, probably because neonatal anthropometry has several validity limitations. Air displacement plethismography (ADP) is a recently validated non-invasive method for assessing body composition in neonates. Objective: To determine the effect of the intake of LCPUFAs during pregnancy on the body composition of term neonates, measured by ADP. Methods: Cross-sectional study of a convenience sample of healthy full-term neonates and their mothers. The diet during pregnancy was assessed using a validated semi-quantitative food frequency questionnaire; Food Processor Plus® was used to convert food intake into nutritional values. Body composition was estimated by anthropometry and measured by ADP using Pea Pod™ Life Measurements Inc (fat mass - FM, fat-free mass and %FM) within the first 72h after birth. Univariate and multivariate analysis (linear regression model) were performed. Results: 54 mother-neonate pairs were included. Multivariate analysis adjusted to the maternal body mass index shows positive association between LCPUFAs intake and neonatal mid-arm circumference (= 0,610, p = 0,019) and negative association between n-6:n-3 ratio intake and neonatal %FM (= -2,744, p=0,066). Conclusion: To the best of our knowledge, this is the first study on this subject using ADP and showing a negative association between LCPUFAs n-6:n-3 ratio intake in pregnancy and neonatal %FM. This preliminary finding requires confirmation increasing the study power with a greater sample and performing interventional studies.

The effect of long-chain polyunsaturated fatty acids intake during pregnancy on adiposity of healthy full-term offspring at birth

Objective - The adjusted effect of long-chain polyunsaturated fatty acid (LCPUFA) intake during pregnancy on adiposity at birth of healthy full-term appropriate-for-gestational age neonates was evaluated. Study Design - In a cross-sectional convenience sample of 100 mother and infant dyads, LCPUFA intake during pregnancy was assessed by food frequency questionnaire with nutrient intake calculated using Food Processor Plus. Linear regression models for neonatal body composition measurements, assessed by air displacement plethysmography and anthropometry, were adjusted for maternal LCPUFA intakes, energy and macronutrient intakes, prepregnancy body mass index and gestational weight gain. Result - Positive associations between maternal docosahexaenoic acid intake and ponderal index in male offspring (β=0.165; 95% confidence interval (CI): 0.031–0.299; P=0.017), and between n-6:n-3 LCPUFA ratio intake and fat mass (β=0.021; 95% CI: 0.002–0.041; P=0.034) and percentage of fat mass (β=0.636; 95% CI: 0.125–1.147; P=0.016) in female offspring were found. Conclusion - Using a reliable validated method to assess body composition, adjusted positive associations between maternal docosahexaenoic acid intake and birth size in male offspring and between n-6:n-3 LCPUFA ratio intake and adiposity in female offspring were found, suggesting that maternal LCPUFA intake strongly influences fetal body composition.

Wind-CSP short-term coordination by MILP approach

This paper is on the maximization of total profit in a day-ahead market for a price-taker producer needing a short-term scheduling for wind power plants coordination with concentrated solar power plants, having thermal energy storage systems. The optimization approach proposed for the maximization of profit is a mixed-integer linear programming problem. The approach considers not only transmission grid constraints, but also technical operating constraints on both wind and concentrated solar power plants. Then, an improved short-term scheduling coordination is provided due to the more accurate modelling presented in this paper. Computer simulation results based on data for the Iberian wind and concentrated solar power plants illustrate the coordination benefits and show the effectiveness of the approach.

Short-term Value for the Water Stored in Headsensitivity Power System Reservoirs

As it is well known, competitive electricity markets require new computing tools for generation companies to enhance the management of its resources. The economic value of the water stored in a power system reservoir is crucial information for enhancing the management of the reservoirs. This paper proposes a practical deterministic approach for computing the short-term economic value of the water stored in a power system reservoir, emphasizing the need to considerer water stored as a scarce resource with a short-term economic value. The paper addresses a problem concerning reservoirs with small storage capacities, i.e., the reservoirs considered as head-sensitivity. More precisely, the respective hydro plant is head-dependent and a pure linear approach is unable to capture such consideration. The paper presents a case study supported by the proposed practical deterministic approach and applied on a real multi-reservoir power system with three cascaded reservoirs, considering as input data forecasts for the electric energy price and for the natural inflow into the reservoirs over the schedule time horizon. The paper presents various water schedules due to different final stored water volume conditions on the reservoirs. Also, it presents the respective economic value of the water for the reservoirs at different stored water volume conditions.