Bayesian model averaging of load demand forecasts from neural network models

Creating a set of a number of neural network (NN) models in an ensemble and accumulating them can achieve better overview capability as compared to single neural network. Neural network ensembles are designed to provide solutions to particular problems. Many researchers and academicians have adopted this NN ensemble technique, especially in machine learning, and has been applied in various fields of engineering, medicine and information technology. This paper present a robust aggregation methodology for load demand forecasting based on Bayesian Model Averaging of a set of neural network models in an ensemble. This paper estimate a vector of coefficient for individual NN models' forecasts using validation data-set. These coefficients, also known as weights, are equal to posterior probabilities of the models generating the forecasts. These BMA weights are then used in combining forecasts generated from NN models with test data-set. By comparing the Bayesian results with the Simple Averaging method, it was observed that benefits are obtained by utilizing an advanced method like BMA for forecast combinations.

The impact of working memory load on task execution and online plan adjustment during multitasking in a virtual environment

Three experiments investigated the impact of working memory load on online plan adjustment during a test of multitasking in young, nonexpert, adult participants. Multitasking was assessed using the Edinburgh Virtual Errands Test (EVET). Participants were asked to memorize either good or poor plans for performing multiple errands and were assessed both on task completion and on the extent to which they modified their plans during EVET performance. EVET was performed twice, with and without a secondary task loading a component of working memory. In Experiment 1, articulatory suppression was used to load the phonological loop. In Experiment 2, oral random generation was used to load executive functions. In Experiment 3, spatial working memory was loaded with an auditory spatial localization task. EVET performance for both good- and poor-planning groups was disrupted by random generation and sound localization, but not by articulatory suppression. Additionally, people given a poor plan were able to overcome this initial disadvantage by modifying their plans online. It was concluded that, in addition to executive functions, multiple errands performance draws heavily on spatial, but not verbal, working memory resources but can be successfully completed on the basis of modifying plans online, despite a secondary task load.

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.

Phase and crystal orientation study of a TRansformation Induced Plasticity steel subjected to cyclic load induced fatigue

An electron backscatter diffraction (EBSD) study of the microstructure of TRIP steel during fatigue failure. Phase and crystal orientation study of a TRIP steel subjected to cyclic load induced fatigue. The relative fractions of austenite, ferrite and martensite are quantified within the strain field of a fatigue crack tip. This data is a subset of data supporting a wider study of the fatigue properties of multiphase steels used in the automotive industry. The different microstructural phases present in these steels can influence the strain life and cyclic stabilized strength of the material due to the way in which these phases accommodate the applied cyclic strain. Fully reversed strain-controlled low-cycle fatigue tests have been used to determine the mechanical fatigue performance of a dual-phase (DP) 590 and transformation induced plasticity (TRIP) 780 steel, with transmission electron microscopy (TEM) and scanning electron microscopy (SEM-EBSD) used to examine the deformed microstructures .

Influence of turbulence on bed load sediment transport

This paper summarizes the results of an experimental study on the influence of an external turbulence field on the bed load sediment transport in an open channel. The external turbulence was generated by (1) a horizontal pipe placed halfway through the depth h; (2) a series of grids with a clearance of about one-third of the depth from the bed, and extending over a finite length of the flume; and (3) a series of grids with a clearance in the range (0.1−1.0)h from the bed, but extending over the entire length of the flume. Two kinds of experiments were conducted: plane-bed experiments and ripple-covered-bed experiments. In the former case, the flow in the presence of the turbulence generator was adjusted so that the mean bed shear stress was the same as in the case without the turbulence generator in order to single out the effect of the external turbulence on the sediment transport. In the ripple-covered-bed case, the mean and turbulence quantities of the streamwise component of the velocity were measured, and the Shields parameter, due to skin friction, was determined. The Shields parameter, together with the RMS value of the streamwise velocity fluctuations, was correlated with the sediment transport rate. The sediment transport increases markedly with increasing turbulence level.

Dynamic load variation and stability analysis in distribution networks with distributed generators

In this paper, the modeling of the distribution network is done in a different way where the distributed generator and dynamic loads are considered. Based on this modeling, this paper presents an analysis to investigate the dynamic and static load variation effect on the distribution network. Graphical interface industry software is used to conduct all the aspects of model implementation and carry out the extensive simulation studies. Here also focuses on the worst case scenario and the different fault effect on the generator. Finally, this paper presents the voltage profile for different penetration with different network configurations.