Hybrid technique of ant colony and particle swarm optimization for short term wind energy forecasting

Wind farms are producing a considerable portion of the world renewable energy. Since the output power of any wind farm is highly dependent on the wind speed, the power extracted from a wind park is not always a constant value. In order to have a non-disruptive supply of electricity, it is important to have a good scheduling and forecasting system for the energy output of any wind park. In this paper, a new hybrid swarm technique (HAP) is used to forecast the energy output of a real wind farm located in Binaloud, Iran. The technique consists of the hybridization of the ant colony optimization (ACO) and particle swarm optimization (PSO) which are two meta-heuristic techniques under the category of swarm intelligence. The hybridization of the two algorithms to optimize the forecasting model leads to a higher quality result with a faster convergence profile. The empirical hourly wind power output of Binaloud Wind Farm for 364 days is collected and used to train and test the prepared model. The meteorological data consisting of wind speed and ambient temperature is used as the inputs to the mathematical model. The results indicate that the proposed technique can estimate the output wind power based on the wind speed and the ambient temperature with an MAPE of 3.513%.

Prediction intervals for short-term wind farm power generation forecasts

Quantification of uncertainties associated with wind power generation forecasts is essential for optimal management of wind farms and their successful integration into power systems. This paper investigates two neural network-based methods for direct and rapid construction of prediction intervals (PIs) for short-term forecasting of power generation in wind farms. The lower upper bound estimation and bootstrap methods are used to quantify uncertainties associated with forecasts. The effectiveness and efficiency of these two general methods for uncertainty quantification is examined using twenty four month data from a wind farm in Australia. PIs with a confidence level of 90% are constructed for four forecasting horizons: five, ten, fifteen, and thirty minutes. Quantitative measures are applied for objective evaluation and unbiased comparison of PI quality. Demonstrated results indicate that reliable PIs can be constructed in a short time without resorting to complicate computational methods or models. Also quantitative comparison reveals that bootstrap PIs are more suitable for short prediction horizon, and lower upper bound estimation PIs are more appropriate for longer forecasting horizons.

Short term load forecasting using interval type-2 fuzzy logic systems

Accurate Short Term Load Forecasting (STLF) is essential for a variety of decision making processes. However, forecasting accuracy may drop due to presence of uncertainty in the operation of energy systems or unexpected behavior of exogenous variables. This paper proposes the application of Interval Type-2 Fuzzy Logic Systems (IT2 FLSs) for the problem of STLF. IT2 FLSs, with extra degrees of freedom, are an excellent tool for handling prevailing uncertainties and improving the prediction accuracy. Experiments conducted with real datasets show that IT2 FLS models appropriately approximate future load demands with an acceptable accuracy. Furthermore, they demonstrate an encouraging degree of accuracy superior to feedforward neural networks used in this study.

Short term load forecasting using a hybrid neural network

Short Term Load Forecasting (STLF) is very important from the power systems grid operation point of view. STLF involves forecasting load demand in a short term time frame. The short term time frame may consist of half hourly prediction up to weekly prediction. Accurate forecasting would benefit the utility in terms of reliability and stability of the grid ensuring adequate supply is present to meet with the load demand. Apart from that it would also affect the financial performance of the utility company. An accurate forecast would result in better savings while maintaining the security of the grid. This paper outlines the STLF using a novel hybrid online learning neural network, known as the Gaussian Regression (GR). This new hybrid neural network is a combination of two existing online learning neural networks which are the Gaussian Adaptive Resonance Theory (GA) and the Generalized Regression Neural Network (GRNN). Both GA and GRNN implemented online learning, but each of them suffers from limitation. Originally GA is used for unsupervised clustering by compressing the training samples into several categories. A supervised version of GA is available, namely Gaussian ARTMAP (GAM). However, the GAM is still not capable on solving regression problem. On the other hand, GRNN is designed for solving real value estimation (regression) problem, but the learning process would involve of memorizing all training samples, hence high computational cost. The hybrid GR is considered an enhanced version of GRNN with compression ability while still maintains online learning properties. Simulation results show that GR has comparable prediction accuracy and has less prototype as compared to the original GRNN as well as the Support Vector Regression.

Short-term load and wind power forecasting using neural network-based prediction intervals

Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time.

Construction of neural network-based prediction intervals for short-term electrical load forecasting

Short-term load forecasting (STLF) is of great importance for control and scheduling of electrical power systems. The uncertainty of power systems increases due to the random nature of climate and the penetration of the renewable energies such as wind and solar power. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in datasets. To quantify these potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for construction of prediction intervals (PIs). A newly proposed method, called lower upper bound estimation (LUBE), is applied to develop PIs using NN models. The primary multi-objective problem is firstly transformed into a constrained single-objective problem. This new problem formulation is closer to the original problem and has fewer parameters than the cost function. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Two case studies from Singapore and New South Wales (Australia) historical load datasets are used to validate the PSO-based LUBE method. Demonstrated results show that the proposed method can construct high quality PIs for load forecasting applications.

A distributed spatial-temporal weighted model on MapReduce for short-term traffic flow forecasting

Accurate and timely traffic flow prediction is crucial to proactive traffic management and control in data-driven intelligent transportation systems (D2ITS), which has attracted great research interest in the last few years. In this paper, we propose a Spatial-Temporal Weighted K-Nearest Neighbor model, named STW-KNN, in a general MapReduce framework of distributed modeling on a Hadoop platform, to enhance the accuracy and efficiency of short-term traffic flow forecasting. More specifically, STW-KNN considers the spatial-temporal correlation and weight of traffic flow with trend adjustment features, to optimize the search mechanisms containing state vector, proximity measure, prediction function, and K selection. urthermore, STW-KNN is implemented on a widely adopted Hadoop distributed computing platform with the MapReduce parallel processing paradigm, for parallel prediction of traffic flow in real time. inally, with extensive experiments on real-world big taxi trajectory data, STW-KNN is compared with the state-of-the-art prediction models including conventional K-Nearest Neighbor (KNN), Artificial Neural Networks (ANNs), Naïve Bayes (NB), Random orest (R), and C4.. The results demonstrate that the proposed model is superior to existing models on accuracy by decreasing the mean absolute percentage error (MAPE) value more than 11.9% only in time domain and even achieves 89.71% accuracy improvement with the MAPEs of between 4% and 6.% in both space and time domains, and also significantly improves the efficiency and scalability of short-term traffic flow forecasting over existing approaches.

A short-term, high-fat diet up-regulates lipid metabolism and gene expression in human skeletal muscle

Background: Dietary fatty acids may be important in regulating gene expression. However, little is known about the effect of changes in dietary fatty acids on gene regulation in human skeletal muscle.
Objective: The objective was to determine the effect of altered dietary fat intake on the expression of genes encoding proteins necessary for fatty acid transport and ß-oxidation in skeletal muscle.
Design: Fourteen well-trained male cyclists and triathletes with a mean (± SE) age of 26.9 ± 1.7 y, weight of 73.7 ± 1.7 kg, and peak oxygen uptake of 67.0 ± 1.3 mL ˙ kg-1 ˙ min-1 consumed either a high-fat diet (HFat: > 65% of energy as lipids) or an isoenergetic high-carbohydrate diet (HCho: 70–75% of energy as carbohydrate) for 5 d in a crossover design. On day 1 (baseline) and again after 5 d of dietary intervention, resting muscle and blood samples were taken. Muscle samples were analyzed for gene expression [fatty acid translocase (FAT/CD36), plasma membrane fatty acid binding protein (FABPpm), carnitine palmitoyltransferase I (CPT I), ß-hydroxyacyl-CoA dehydrogenase (ß-HAD), and uncoupling protein 3 (UCP3)] and concentrations of the proteins FAT/CD36 and FABPpm.
Results: The gene expression of FAT/CD36 and &szlig; -HAD and the gene abundance of FAT/CD36 were greater after the HFat than after the HCho diet (P < 0.05). Messenger RNA expression of FABPpm, CPT I, and UCP-3 did not change significantly with either diet.
Conclusions: A rapid and marked capacity for changes in dietary fatty acid availability to modulate the expression of mRNA-encoding proteins is necessary for fatty acid transport and oxidative metabolism. This finding is evidence of nutrient-gene interactions in human skeletal muscle.

Effect of short-term training on GLUT-4 mRNA and protein expression in human skeletal muscle

Six untrained, male subjects (23 ± 1 years old, 84 ± 5 kg, V_O2peak= 3.7 ± 0.8 l min^–1) exercised for 60 min at 75 ± 1%V_O2peak on 7 consecutive days.  Muscle samples were obtained before the start of cycle exercise training and 24 h after the first and seventh exercise sessions and analysed for citrate synthase activity, glycogen and glucose transporter 4 (GLUT-4) mRNA and protein expression. Exercise training increased (P < 0.05) citrate synthase by ~20% and muscle glycogen concentration by ~40%. GLUT-4 mRNA levels 24 h after the first and seventh exercise sessions were similar to those  measured before the start of exercise training. In contrast, GLUT-4 protein expression was increased after 7 days of exercise training (12.4 ± 1.5 versus 3.4 ± 1.0 arbitray units (a.u.), P < 0.05) and although it tended to be higher 24 h after the first exercise session (6.0 ± 3.0 versus 3.4 ± 1.0 a.u.), this was not significantly different (P= 0.09). These results support the suggestion that the adaptive increase in skeletal muscle GLUT-4 protein expression with short-term exercise training arises from the repeated, transient increases in GLUT-gene transcription following each exercise bout leading to a gradual accumulation of GLUT-4 protein, despite GLUT-4 mRNA returning to basal levels between exercise stimuli.

Motor unit synchronization measured by cross-correlation is not influenced by short-term strength training of a hand muscle

The purpose of the study was to quantify the strength of motor unit synchronization and coherence from pairs of concurrently active motor units before and after short-term (4–8 weeks) strength training of the left first dorsal interosseous (FDI) muscle. Five subjects (age 24.8 ± 4.3 years) performed a training protocol three times/week that consisted of six sets of ten maximal isometric index finger abductions, whereas three subjects (age 27.3 ± 6.7 years) acted as controls. Motor unit activity was recorded from pairs of intramuscular electrodes in the FDI muscle with two separate motor unit recording sessions obtained before and after strength training (trained group) or after 4 weeks of normal daily activities that did not involve training (control group). The training intervention resulted in a 54% (45.2 ± 8.3 to 69.5 ± 13.8 N, P = 0.001) increase in maximal index finger abduction force, whereas there was no change in strength in the control group. A total of 163 motor unit pairs (198 single motor units) were examined in both subject groups, with 52 motor unit pairs obtained from 10 recording sessions before training and 51 motor unit pairs from 10 recording sessions after training. Using the cross-correlation procedure, there was no change in the strength of motor unit synchronization following strength training (common input strength index; 0.71 ± 0.41 to 0.67 ± 0.43 pulses/s). Furthermore, motor unit coherence z scores at low (0–10 Hz; 3.9 ± 0.3 before to 4.4 ± 0.4 after) or high (10–30 Hz; 1.7 ± 0.1 before to 1.9 ± 0.1 after) frequencies were not influenced by strength training. These motor unit data indicate that increases in strength following several weeks of training a hand muscle are not accompanied by changes in motor unit synchronization or coherence, suggesting that these features of correlated motor unit activity are not important in the expression of muscle strength.

The effects of short-term sprint training on MCT expression in moderately endurance-trained runners

The purpose of this study was to assess the effects of short-term sprint training on transient changes in monocarboxylate lactate transporter 1 (MCT1) and MCT4 protein and mRNA content. Seven moderately endurance-trained runners (mean ± SE; age 27.7±2.9 years, body mass 81.1±5.9 kg, VO₂ max 58.1±2.0 ml kg⁻¹ min⁻¹) completed a VO₂ max and a supramaximal running test to exhaustion (RTE) before and after a 6-week period of sprint training. The sprint training was progressive and consisted of 18 sessions of near maximal short duration (5–15 s) sprints to compliment the athlete’s endurance training. Prior to the training period there was a significant (P<0.05) increase in MCT1, but not MCT4 protein, 2 h after the RTE. This occurred without any change in corresponding mRNA levels. After the training period, there was a significant increase in MCT1 protein but no significant change in the MCT4 isoform. Both MCT1 and MCT4 mRNA was significantly lower at rest and 2 h post-RTE after the completion of the training period. After the training period, there was a significant increase in the time to exhaustion and distance covered during the RTE. This study demonstrates that sprint training of this length and type results in an upregulation of MCT1 protein, but not MCT4 content. Additionally, this study shows conflicting adaptations in MCT1 and MCT4 protein and mRNA levels following training, which may indicate post-transcriptional regulation of MCT expression in human muscle.

Increased insulin-stimulated Akt pSer473 and cytosolic SHP2 protein abundance in human skeletal muscle following acute exercise and short-term training.

The purpose of the present study was to determine in human skeletal muscle whether a single exercise bout and 7 days of consecutive endurance (cycling) training 1) increased insulin-stimulated Akt pSer473and 2) altered the abundance of the protein tyrosine phosphatases (PTPases), PTP1B and SHP2. In healthy, untrained men (n = 8; 24 ± 1 yr), glucose infusion rate during a hyperinsulinemic euglycemic clamp, when compared with untrained values, was not improved 24 h following a single 60-min bout of endurance cycling but was significantly increased (~30%; P < 0.05) 24 h following completion of 7 days of exercise training. Insulin-stimulated Akt pSer473was ~50% higher (P < 0.05) 24 h following the acute bout of exercise, with this effect remaining after 7 days of training (P < 0.05). Insulin-stimulated insulin receptor and insulin receptor substrate-1 tyrosine phosphorylation were not altered 24 h after acute exercise and short-term training. Insulin did not acutely regulate the localization of the PTPases, PTP1B or SHP2, although cytosolic protein abundance of SHP2 was increased (P < 0.05; main effect) 24 h following acute exercise and short-term training. In conclusion, insulin-sensitive Akt pSer473and cytosolic SHP2 protein abundance are higher after acute exercise and short-term training, and this effect appears largely due to the residual effects of the last bout of prior exercise. The significance of exercise-induced alterations in cytosolic SHP2 and insulin-stimulated Akt pSer473on the improvement in insulin sensitivity requires further elucidation.