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.

Impact of dynamic PHEVs load on renewable sources based distribution system

In this paper, charging effect of dynamic Plug in Hybrid Electric Vehicle (PHEV) is presented in a renewable energy based electricity distribution system. For planning and designing a distribution system, PHEVs are one of the most important factor as it is going to be a spinning reserve of energy, and also a major load for distribution network. A dynamic load model of PHEVs is introduced here based on third order battery model. To determine the system adequacy, it is necessary to do a micro level analysis to know the PHEVs load impact on grid. Scope of such analysis will cover the performance of wind and solar generation with dynamic PHEVs load, as well as the stability analysis of the power grid to demonstrate that it is important to consider the dynamics of PHEVs load in a renewable energy based distribution network.

Effects of load modeling in power distribution system with distributed wind generation

This paper presents the impact of different types of load models in distribution network with distributed wind generation. The analysis is carried out for a test distribution system representative of the Kumamoto area in Japan. Firstly, this paper provides static analysis showing the impact of static load on distribution system. Then, it investigates the effects of static as well as composite load based on the load composition of IEEE task force report [1] through an accurate time-domain analysis. The analysis shows that modeling of loads has a significant impact on the voltage dynamics of the distribution system with distributed generation.

Stable operation of distribution networks using D-STATCOM considering uncertainties in load compositions

This paper presents a novel control design for D-STATCOM to ensure grid code-compatible performance of distributed wind generators. The approach considered in this paper is to find the smallest upper bound on the H norm of the uncertain system and to design an optimal linear quadratic Gaussian (LQG) controller based on this bound. The change in the model due to variations of induction motor (IM) load compositions in the composite load is considered as an uncertain term in the design algorithm. The performance of the designed controller is demonstrated on a distribution test system representative of the Kumamoto area in Japan. It is found that the proposed controller enhances voltage stability of the distribution system under varying operating conditions

Improved contact load-bearing capacity of ultra-fine grained titanium : Multilayering and grading

The contact load-bearing response and surface damage resistance of multilayered hierarchical structured (MHSed) titanium were determined and compared to monolithic nanostructured titanium. The MHS structure was formed by combining cryorolling with a subsequent Surface Mechanical Attrition Treatment (SMAT) producing a surface structure consisted of an outer amorphous layer containing nanocrystals, an inner nanostructured layer and finally an ultra-fine grained core. The combination of a hard outer layer, a gradual transition layer and a compliant core results in reduced indentation depth, but a deeper and more diffuse sub-surface plastic deformation zone, compared to the monolithic nanostructured Ti. The redistribution of surface loading between the successive layers in the MHS Ti resulted in the suppression of cracking, whereas the monolithic nanograined (NG) Ti exhibited sub-surface cracks at the boundary of the plastic strain field. Finite element models with discrete layers and mechanically graded layersrepresenting the MHS system confirmed the absence of cracking and revealed a 38% decrease in shear stress in the sub-surface plastic strain field, compared to the monolithic NG Ti. Further, the mechanical gradation achieves a more gradual stress distribution which mitigates the interface failure and increases the interfacial toughness, thus providing strong resistance to loading damage. © 2014 Elsevier Ltd.

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.

Electricity load and price forecasting with influential factors in a deregulated power industry

With the emergence of smart power grid and distributed generation technologies in recent years, there is need to introduce new advanced models for forecasting. Electricity load and price forecasts are two primary factors needed in a deregulated power industry. The performances of the demand response programs are likely to be deteriorated in the absence of accurate load and price forecasting. Electricity generation companies, system operators, and consumers are highly reliant on the accuracy of the forecasting models. However, historical prices from the financial market, weekly price/load information, historical loads and day type are some of the explanatory factors that affect the accuracy of the forecasting. In this paper, a neural network (NN) model that considers different influential factors as feedback to the model is presented. This model is implemented with historical data from the ISO New England. It is observed during experiments that price forecasting is more complicated and hence less accurate than the load forecasting.

Motivation and cognitive load in the flipped classroom : definition, rationale and a call for research

Flipped classroom approaches remove the traditional transmissive lecture and replace it with active in-class tasks and pre-/post-class work. Despite the popularity of these approaches in the media, Google search, and casual hallway chats, there is very little evidence of effectiveness or consistency in understanding what a flipped classroom actually is. Although the flipped terminology is new, some of the approaches being labelled ‘flipped’ are actually much older. In this paper, we provide a catch-all definition for the flipped classroom, and attempt to retrofit it with a pedagogical rationale, which we articulate through six testable propositions. These propositions provide a potential agenda for research about flipped approaches and form the structure of our investigation. We construct a theoretical argument that flipped approaches might improve student motivation and help manage cognitive load. We conclude with a call for more specific types of research into the effectiveness of the flipped classroom approach.

Investigation of hydraulic fracture propagation using a post-peak control system coupled with acoustic emission

This study investigates the fracture mechanism of fluid coupled with a solid resulting from hydraulic fracture. A new loading machine was designed to improve upon conventional laboratory hydraulic fracture testing and to provide a means of better understanding fracture behavior of solid media. Test specimens were made of cement mortar. An extensometer and acoustic emission (AE) monitoring system recorded the circumferential deformation and crack growth location/number during the test. To control the crack growth at the post-peak stage the input fluid rate can be adjusted automatically according to feedback from the extensometer. The complete stress-deformation curve, including pre- and post-peak stages, was therefore obtained. The crack extension/growth developed intensively after the applied stress reached the breakdown pressure. The number of cracks recorded by the AE monitoring system was in good agreement with the amount of deformation (expansion) recorded by the extensometer. The results obtained in this paper provide a better understanding of the hydraulic fracture mechanism which is useful for underground injection projects. © 2014 Springer-Verlag Wien.

An investigation for improved home energy management

 Increasing household energy consumption and increasing primary energy cost urged to improve home energy efficiency. Improved energy management can suggest the ways to improve home energy efficiency. Various home appliances are the prime cause to the increased power demand. Appliance's energy rating information helps to develop awareness and reduce energy consumption. Load shifting can help to reduce overall cost of used energy bill by shifting peak time load to off-peak time. However most of the present appliances remains in standby mode (active or passive) for a significant part of the day, and load shifting cannot reduce the total energy consumption. Therefore investigation is required to identify any possible scopes to improve energy management at home. This paper investigated several home appliances and monitored daily time of use power consumption. It was found that by controlling standby power from a daily home load of 4.482 kWh, power demand can be reduced 12.56% moreover energy related greenhouse gas (GHG) emission can be reduced 133.08kg/year.