Search results for: load modeling

Authors: Ioannis P. Panapakidis, Marios N. Moschakis

Abstract:

With the continuous increment of smart meter installations across the globe, the need for processing of the load data is evident. Clustering-based load profiling is built upon the utilization of unsupervised machine learning tools for the purpose of formulating the typical load curves or load profiles. The most commonly used algorithm in the load profiling literature is the K-means. While the algorithm has been successfully tested in a variety of applications, its drawback is the strong dependence in the initialization phase. This paper proposes a novel modified form of the K-means that addresses the aforementioned problem. Simulation results indicate the superiority of the proposed algorithm compared to the K-means.

Keywords: clustering, load profiling, load modeling, machine learning, energy efficiency and quality

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Authors: S. A. Che Osmi, W. M. F. W. Ishak, S. F. Che Osmi

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Nowadays the issues of water quality and water pollution have been a major problem across the country. A lot of management attempt to develop their own TMDL database in order to control the river pollution. Over the past decade, the mathematical modeling has been used as the tool for the development of TMDL. This paper presents the application of water quality modeling to develop the total maximum daily load (TMDL) information. To obtain the reliable database of TMDL, the appropriate water quality modeling should choose based on the available data provided. This paper will discuss on the use of several water quality modeling such as QUAL2E, QUAL2K, and EFDC to develop TMDL. The attempts to integrate several modeling are also being discussed in this paper. Based on this paper, the differences in the application of water quality modeling based on their properties such as one, two or three dimensional are showing their ability to develop the modeling of TMDL database.

Keywords: TMDL, water quality modeling, QUAL2E, EFDC

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Authors: Atef Ahmad Bleibel

Abstract:

Most codes of practice, like ACI 318-14, require the use of strut-and-tie modeling to analyze and design reinforced concrete deep beams. Though, investigations that conducted on deep beams do not include ring deep beams of influential parameters. This work presents an analytical parametric study using strut-and-tie modeling stated by ACI 318-14 to predict load capacity of 20 reinforced concrete ring deep beam specimens with different parameters. The parameters that were under consideration in the current work are ring diameter (Dc), number of supports (NS), width of ring beam (bw), concrete compressive strength (f'c) and width of bearing plate (Bp). It is found that the load capacity decreases by about 14-36% when ring diameter increases by about 25-75%. It is also found that load capacity increases by about 62-189% when number of supports increases by about 33-100%, while the load capacity increases by about 25-75% when the beam ring width increases by about 25-75%. Finally, it is found that load capacity increases by about 24-76% when compressive strength increases by about 24-76%, while the load capacity increases by about 5-16% when Bp increases by about 25-75%.

Keywords: load parameters, reinforced concrete, ring deep beam, strut and tie

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Authors: Slim Chokri

Abstract:

Load forecasting is one of the central functions in power systems operations. Electricity cannot be stored, which means that for electric utility, the estimate of the future demand is necessary in managing the production and purchasing in an economically reasonable way. A majority of the recently reported approaches are based on neural network. The attraction of the methods lies in the assumption that neural networks are able to learn properties of the load. However, the development of the methods is not finished, and the lack of comparative results on different model variations is a problem. This paper presents a new approach in order to predict the Tunisia daily peak load. The proposed method employs a computational intelligence scheme based on the Fuzzy neural network (FNN) and support vector regression (SVR). Experimental results obtained indicate that our proposed FNN-SVR technique gives significantly good prediction accuracy compared to some classical techniques.

Keywords: neural network, load forecasting, fuzzy inference, machine learning, fuzzy modeling and rule extraction, support vector regression

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Authors: R. Ziaie Moayed, E. Ghanbari Alamouty

Abstract:

Deep soil mixing is one of the improvement methods in geotechnical engineering which is widely used in soft soils. This article investigates the consolidation behavior of a soft clay soil which is improved by soil-cement column (SCC) by numerical modeling using Plaxis2D program. This behavior is simulated under vertical static and cyclic load which is applied on the soil surface. The static load problem is the simulation of a physical model test in an axisymmetric condition which uses a single SCC in the model center. The results of numerical modeling consist of settlement of soft soil composite, stress on soft soil and column, and excessive pore water pressure in the soil show a good correspondence with the test results. The response of soft soil composite to the cyclic load in vertical direction also compared with the static results. Also the effects of two variables namely the cement content used in a SCC and the area ratio (the ratio of the diameter of SCC to the diameter of composite soil model, a) is investigated. The results show that the stress on the column with the higher value of a, is lesser compared with the stress on other columns. Different rate of consolidation and excessive pore pressure distribution is observed in cyclic load problem. Also comparing the results of settlement of soil shows higher compressibility in the cyclic load problem.

Keywords: area ratio, consolidation behavior, cyclic load, numerical modeling, soil-cement column

Procedia PDF Downloads 129

Authors: Hueiwang Anna Jeng, Norou Diawara, Nancy Welch, Cynthia Jackson, Rekha Singh, Kyle Curtis, Raul Gonzalez, David Jurgens, Sasanka Adikari

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Modeling effort is needed to predict the COVID-19 trends for developing management strategies and adaptation measures. The objective of this study was to assess whether SARS-CoV-2 viral load in wastewater could serve as a predictor for forecasting COVID-19 cases, hospitalization cases, and death cases using copula-based time series modeling. SARS-CoV-2 RNA load in raw wastewater in Chesapeake VA was measured using the RT-qPCR method. Gaussian copula time series marginal regression model, incorporating an autoregressive moving average model and the copula function, served as a forecasting model. COVID-19 cases were correlated with wastewater viral load, hospitalization cases, and death cases. The forecasted trend of COVID-19 cases closely paralleled one of the reported cases, with over 90% of the forecasted COVID-19 cases falling within the 99% confidence interval of the reported cases. Wastewater SARS-CoV-2 viral load could serve as a predictor for COVID-19 cases and hospitalization cases.

Keywords: COVID-19, modeling, time series, copula function

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Authors: Arpan Dwivedi, Yogesh Pahariya

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In this paper, optimal design of hybrid standalone power supply system (SAPS) is done for off grid applications in remote areas where transmission of power is difficult. The hybrid SAPS system uses two primary energy sources, wind and solar, and in addition to these diesel generator is also connected to meet the load demand in case of failure of wind and solar system. This paper presents mathematical modeling of 1.4 MW hybrid SAPS system for rural electrification. This paper firstly focuses on mathematical modeling of PV module connected in a string, secondly focuses on modeling of permanent magnet wind turbine generator (PMWTG). The hybrid controller is also designed for selection of power from the source available as per the load demand. The power output of hybrid SAPS system is analyzed for meeting load demands at urban as well as for rural areas.

Keywords: SAPS, DG, PMWTG, rural area, off-grid, PV module

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Authors: Susmita Panda, Arnab Banerjee, Ajinkya Baxy, Bappaditya Manna

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The design of short to medium-span high-speed bridges in critical locations is an essential aspect of vehicle-bridge interaction. Due to dynamic interaction between moving load and bridge, mathematical models or finite element modeling computations become time-consuming. Thus, to reduce the computational effort, a universal approximator using an artificial neural network (ANN) has been used to evaluate the dynamic response of the bridge. The data set generation and training of surrogate models have been conducted over the results obtained from mathematical modeling. Further, the robustness of the surrogate model has been investigated, which showed an error percentage of less than 10% with conventional methods. Additionally, the dependency of the dynamic response of the bridge on various load and bridge parameters has been highlighted through a parametric study.

Keywords: artificial neural network, mode superposition method, moving load analysis, surrogate models

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Authors: Morteza Aien, Masoud Rashidinejad, Mahmud Fotuhi-Firuzabad

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As energetic and environmental issues are getting more and more attention all around the world, the penetration of distributed energy resources (DERs) mainly those harvesting renewable energies (REs) ascends with an unprecedented rate. This matter causes more uncertainties to appear in the power system context; ergo, the uncertainty analysis of the system performance is an obligation. The uncertainties of any system can be represented probabilistically or possibilistically. Since sufficient historical data about all the system variables is not available, therefore, they do not have a probability density function (PDF) and must be represented possibilistiacally. When some of system uncertain variables are probabilistic and some are possibilistic, neither the conventional pure probabilistic nor pure possibilistic methods can be implemented. Hence, a combined solution is appealed. The first of this two-paper series formulates a new possibilistic-probabilistic tool for the load flow uncertainty assessment. The proposed methodology is based on the evidence theory and joint propagation of possibilistic and probabilistic uncertainties. This possibilistic- probabilistic formulation is solved in the second companion paper in an uncertain load flow (ULF) study problem.

Keywords: probabilistic uncertainty modeling, possibilistic uncertainty modeling, uncertain load flow, wind turbine generator

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Authors: H. Ghodbane, A. A. Taleb, O. Kraa

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This paper presents geometric design of induction heating system. The objective of this design is to improve the temperature distribution in the load. The study of such a device requires the use of models or modeling representation, physical, mathematical, and numerical. This modeling is the basis of the understanding, the design, and optimization of these systems. The optimization technique is to find values of variables that maximize or minimize the objective function.

Keywords: optimization, modeling, geometric design system, temperature increase

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Authors: Muhammad Fanseem E. M., Rama Satya Satish Kumar, Indrajeet Bhausaheb Bhavar, Deepak M.

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Keeping the right balance of electricity between the supply and demand sides of the grid is one of the most important objectives of electrical grid operation. Power generation and demand forecasting are the core of power management and generation scheduling. Large, centralized producing units were used in the construction of conventional power systems in the past. A certain level of balance was possible since the generation kept up with the power demand. However, integrating renewable energy sources into power networks has proven to be a difficult challenge due to its intermittent nature. The power imbalance caused by rising demands and peak loads is negatively affecting power quality and dependability. Demand side management and demand response were one of the solutions, keeping generation the same but altering or rescheduling or shedding completely the load or demand. However, shedding the load or rescheduling is not an efficient way. There comes the significance of virtual power plants. The virtual power plant integrates distributed generation, dispatchable load, and distributed energy storage organically by using complementing control approaches and communication technologies. This would eventually increase the utilization rate and financial advantages of distributed energy resources. Most of the writing on virtual power plant models ignored technical limitations, and modeling was done in favor of a financial or commercial viewpoint. Therefore, this paper aims to address the modeling intricacies of VPPs and their technical limitations, shedding light on a holistic understanding of this innovative power management approach.

Keywords: cost optimization, distributed energy resources, dynamic modeling, model quality tests, power system modeling

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Authors: J. Bartus, J. Odrobinak

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The paper presents a nonlinear analysis 3D model of composite steel and concrete beams with web openings using the Finite Element Method (FEM). The core of the study is the introduction of basic modeling techniques comprehending the description of material behavior, appropriate elements selection, and recommendations for overcoming problems with convergence. Results from various finite element models are compared in the study. The main objective is to observe the concrete failure mechanism and its influence on the structural performance of numerical models of the beams at particular load stages. The bearing capacity of beams, corresponding deformations, stresses, strains, and fracture patterns were determined. The results show how load-bearing elements consisting of concrete parts can be analyzed using FEM software with various options to create the most suitable numerical model. The paper demonstrates the versatility of Ansys software usage for structural simulations.

Keywords: Ansys, concrete, modeling, steel

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Authors: Giovanni Masala, Stefania Marica

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Forecasting electricity load plays a crucial role regards decision making and planning for economical purposes. Besides, in the light of the recent privatization and deregulation of the power industry, the forecasting of future electricity load turned out to be a very challenging problem. Empirical data about electricity load highlights a clear seasonal behavior (higher load during the winter season), which is partly due to climatic effects. We also emphasize the presence of load periodicity at a weekly basis (electricity load is usually lower on weekends or holidays) and at daily basis (electricity load is clearly influenced by the hour). Finally, a long-term trend may depend on the general economic situation (for example, industrial production affects electricity load). All these features must be captured by the model. The purpose of this paper is then to build an hourly electricity load model. The deterministic component of the model requires non-linear regression and Fourier series while we will investigate the stochastic component through econometrical tools. The calibration of the parameters’ model will be performed by using data coming from the Italian market in a 6 year period (2007- 2012). Then, we will perform a Monte Carlo simulation in order to compare the simulated data respect to the real data (both in-sample and out-of-sample inspection). The reliability of the model will be deduced thanks to standard tests which highlight a good fitting of the simulated values.

Keywords: ARMA-GARCH process, electricity load, fitting tests, Fourier series, Monte Carlo simulation, non-linear regression

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Authors: Seyed Amin Vakili, Sahar Sadat Vakili, Seyed Ehsan Vakili, Nader Abdoli Yazdi

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The main purpose of this paper is to present the Modified Newmark Method as a method of non-linear frame analysis by considering the effect of the axial load (second order analysis). The discussion will be restricted to plane frameworks containing a constant cross-section for each element. In addition, it is assumed that the frames are prevented from out-of-plane deflection. This part of the investigation is performed to generalize the established method for the assemblage structures such as frameworks. As explained, the governing differential equations are non-linear and cannot be formulated easily due to unknown axial load of the struts in the frame. By the assumption of constant axial load, the governing equations are changed to linear ones in most methods. Since the modeling and the solutions of the non-linear form of the governing equations are cumbersome, the linear form of the equations would be used in the established method. However, according to the ability of the method to reconsider the minor omitted parameters in modeling during the solution procedure, the axial load in the elements at each stage of the iteration can be computed and applied in the next stage. Therefore, the ability of the method to present an accurate approach to the solutions of non-linear equations will be demonstrated again in this paper.

Keywords: nonlinear, stability, buckling, modified newmark method

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Authors: Amira M. Attia, Karim H. Youssef, Nabil H. Abbasi

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Demand Side Management (DSM) is an essential characteristic of current and future smart grid systems. As one of DSM functions, load management aims to control customers’ total electric consumption and utility’s load factor by using various load shaping techniques. However, applying load shaping techniques such as load shifting, peak clipping, or strategic conservation individually does not provide the desired level of improvement for load factor increment and/or customer’s bill reduction. In this paper, two load shaping techniques will be simulated as constrained optimization problems. The purpose is to reflect the application of combined load shifting and strategic conservation model together at the same time, and the application of combined load shifting and peak clipping model as well. The problem will be formulated and solved by using disciplined convex programming (CVX) based MATLAB® R2013b. Simulation results will be evaluated and compared for studying the most impactful multi-techniques model in improving load curve.

Keywords: convex programing, demand side management, load shaping, multiple, building energy optimization

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Authors: Javad Shamsi Soosahab, Reza Ziaie Moayed

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The compressive and tensile bearing capacity of helical piles in sand is investigated by means of numerical modeling. The analyses are carried out using two-dimensional finite-element software, Optum G2. The load–displacement behavior under compression and tension is compared in different relative densities for constant and various elastic modulus. The criterion used to find the ultimate axial load is the load corresponding to 5% of the helical diameter. The results show that relative density of sand plays an essential role in the response of ultimate capacities towards various condition. Increase in elastic modulus with depth is found to play a relatively more significant role to the increase in ultimate compressive load capacities, however tension bearing capacity decreases.

Keywords: helical piles, Optum G2, relative density, constant and various elastic modulus

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Authors: Firas M. Tuaimah

Abstract:

This paper presents an Artificial Neural Network based approach for short-term load forecasting and exactly for two days ahead. Two seasons have been discussed for Iraqi power system, namely summer and winter; the hourly load demand is the most important input variables for ANN based load forecasting. The recorded daily load profile with a lead time of 1-48 hours for July and December of the year 2012 was obtained from the operation and control center that belongs to the Ministry of Iraqi electricity. The results of the comparison show that the neural network gives a good prediction for the load forecasting and for two days ahead.

Keywords: short-term load forecasting, artificial neural networks, back propagation learning, hourly load demand

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Authors: Afaneen Anwer, Samara M. Kamil

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Load Forecast required prediction accuracy based on optimal operation and maintenance. A good accuracy is the basis of economic dispatch, unit commitment, and system reliability. A good load forecasting system fulfilled fast speed, automatic bad data detection, and ability to access the system automatically to get the needed data. In this paper, the formulation of the load forecasting is discussed and the solution is obtained by using artificial neural network method. A MATLAB environment has been used to solve the load forecasting schedule of Iraqi super grid network considering the daily load for three years. The obtained results showed a good accuracy in predicting the forecasted load.

Keywords: load forecasting, neural network, back-propagation algorithm, Iraqi power system

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Authors: Spyridon Nikolaidis

Abstract:

A modeling approach for CMOS gates is presented based on the use of the equivalent inverter. A new model for the inverter has been developed using a simplified transistor current model which incorporates the nanoscale effects for the planar technology. Parametric expressions for the output voltage are provided as well as the values of the output and supply current to be compatible with the CCS technology. The model is parametric according the input signal slew, output load, transistor widths, supply voltage, temperature and process. The transistor widths of the equivalent inverter are determined by HSPICE simulations and parametric expressions are developed for that using a fitting procedure. Results for the NAND gate shows that the proposed approach offers sufficient accuracy with an average error in propagation delay about 5%.

Keywords: CMOS gate modeling, inverter modeling, transistor current mode, timing model

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Authors: Byungmin Cho, Dongchul Lee, Taejin Kim, Minhee Lee

Abstract:

The procedure for the seismic retrofit of existing buildings includes the seismic evaluation. In the evaluation step, it is assessed whether the buildings have satisfactory performance against seismic load. Based on the results of that, the buildings are upgraded. To evaluate seismic performance of the buildings, it usually goes through the model transformation from elastic analysis to inelastic analysis. However, when the data is not delivered through the interwork, engineers should manually input the data. In this process, since it leads to inaccuracy and loss of information, the results of the analysis become less accurate. Therefore, in this study, the process for the seismic evaluation of existing buildings using structural information modeling is suggested. This structural information modeling makes the work economic and accurate. To this end, it is determined which part of the process could be computerized through the investigation of the process for the seismic evaluation based on ASCE 41. The structural information modeling process is developed to apply to the seismic evaluation using Perform 3D program usually used for the nonlinear response history analysis. To validate this process, the seismic performance of an existing building is investigated.

Keywords: existing building, nonlinear analysis, seismic performance, structural information modeling

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Authors: Md. Shafiullah, Ali T. Al-Awami

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This project proposes optimal scheduling of imported power of a load aggregator with the utilization of EVs to maximize its profit. As with the increase of renewable energy resources, electricity price in competitive market becomes more uncertain and, on the other hand, with the penetration of renewable distributed generators in the distribution network the predicted load of a load aggregator also becomes uncertain in real time. Though there is uncertainties in both load and price, the use of EVs storage capacity can make the operation of load aggregator flexible. LA submits its offer to day-ahead market based on predicted loads and optimized use of its EVs to maximize its profit, as well as in real time operation it uses its energy storage capacity in such a way that it can maximize its profit. In this project, load aggregators profit maximization algorithm is formulated and the optimization problem is solved with the help of CVX. As in real time operation the forecasted loads differ from actual load, the mismatches are settled in real time balancing market. Simulation results compare the profit of a load aggregator with a hypothetical group of 1000 EVs and without EVs.

Keywords: CVX, electricity market, load aggregator, load and price uncertainties, profit maximization, real time balancing operation

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Authors: Rouaz Idriss, Bourahla Nour-Eddine, Kahlouche Farah, Rafa Sid Ali

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The stability of structures made of light-gauge steel depends highly on the contribution of Shear Wall Panel (SWP) systems under horizontal forces due to wind or earthquake loads. Steel plate sheathing is often used with these panels made of cold formed steel (CFS) to improve its shear strength. In order to predict the shear strength resistance, two methods are presented in this paper. In the first method, the steel plate sheathing is modeled with plats strip taking into account only the tension and compression force due to the horizontal load, where both track and stud are modeled according to the geometrical and mechanical characteristics of the specimen used in the experiments. The theoretical background and empirical formulations of this method are presented in this paper. However, the second method is based on a micro modeling of the cold formed steel Shear Wall Panel “CFS-SWP” using Abaqus software. A nonlinear analysis was carried out with an in-plan monotonic load. Finally, the comparison between these two methods shows that the micro modeling with Abaqus gives better prediction of shear resistance of SWP than strips method. However, the latter is easier and less time consuming than the micro modeling method.

Keywords: cold formed steel 'CFS', shear wall panel, strip method, finite elements

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Authors: P. Charvin, R. Bourrou, F. Lemont, C. Lafon, A. Russello

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For nuclear waste treatment and confinement, a specific IN-CAN melting module based on low-frequency induction heating have been designed. The frequency of 50Hz has been chosen to improve penetration length through metal. In this design, the liquid metal, strongly stirred by electromagnetic effects, presents shape of a dome caused by strong Laplace forces developing in the bulk of bath. Because of a lower density, the glass phase is located above the metal phase and is heated and stirred by metal through interface. Electric parameters (Intensity, frequency) give precious information about metal load and composition (resistivity of alloy) through impedance modification. Then, power supply can be adapted to energy transfer efficiency for suitable process supervision. Modeling of this system allows prediction of metal dome shape (in agreement with experimental measurement with a specific device), glass and metal velocity, heat and motion transfer through interface. MHD modeling is achieved with COMSOL and Fluent. First, a simplified model is used to obtain the shape of the metal dome. Then the shape is fixed to calculate the fluid flow and the thermal part.

Keywords: electromagnetic stirring, induction heating, interface modeling, metal load

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Authors: Ramin Vafadary, Maryam Khanbaghi

Abstract:

Forecasting electricity load is important for various purposes like planning, operation, and control. Forecasts can save operating and maintenance costs, increase the reliability of power supply and delivery systems, and correct decisions for future development. This paper compares various time series methods to forecast 24 hours ahead of electricity load. The methods considered are the Holt-Winters smoothing, SARIMA Modeling, LSTM Network, Fbprophet, and Tensorflow probability. The performance of each method is evaluated by using the forecasting accuracy criteria, namely, the mean absolute error and root mean square error. The National Renewable Energy Laboratory (NREL) residential energy consumption data is used to train the models. The results of this study show that the SARIMA model is superior to the others for 24 hours ahead forecasts. Furthermore, a Bagging technique is used to make the predictions more robust. The obtained results show that by Bagging multiple time-series forecasts, we can improve the robustness of the models for 24 hours ahead of electricity load forecasting.

Keywords: bagging, Fbprophet, Holt-Winters, LSTM, load forecast, SARIMA, TensorFlow probability, time series

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Authors: Nuo Xu, Kok Hun Goh, Jeyatharan Kumarasamy

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Pile load testing is significant during foundation construction due to its traditional role of design validation and routine quality control of the piling works. In order to verify whether piles can take loadings at specified settlements, piles will have to undergo working load test where the test load should normally up to 150% of the working load of a pile. Selection or sampling of piles for the working load test is done subject to the number specified in Singapore National Annex to Eurocode 7 SS EN 1997-1:2010. This paper presents an innovative way to rationalize the number of pile load test by adopting statistical analysis approach and looking at the coefficient of variance of pile elastic modulus using a case study at Singapore Tuas depot. Results are very promising and have shown that it is possible to reduce the number of working load test without influencing the reliability and confidence on the pile quality. Moving forward, it is suggested that more load test data from other geological formations to be examined to compare with the findings from this paper.

Keywords: elastic modulus of pile under soil interaction, jurong formation, kentledge test, pile load test

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Authors: Ying Zhang

Abstract:

In ASME Boiler and Pressure Vessel Code, the plastic load is defined by applying the twice elastic slope (TES) criterion of plastic collapse to a characteristic load-deformation curve for the vessel. Several other plastic criterion such as tangent intersection (TI) criterion, plastic work (PW) criterion have been proposed in the literature, but all exhibit a practical limitation: difficult to define the load parameter for vessels subject to several combined loads. An alternative criterion: plastic work-tangent (PWT) criterion for evaluating plastic load in pressure vessel design by analysis is presented in this paper. According to the plastic work-load curve, when the tangent variation is less than a given value in the plastic phase, the corresponding load is the plastic load. Application of the proposed criterion is illustrated by considering the elastic-plastic response of the lower head of reactor pressure vessel (RPV) and nozzle intersection of (RPV). It is proposed that this is because the PWT criterion more fully represents the constraining effect of material strain hardening on the spread of plastic deformation and more efficiently ton evaluating the plastic load.

Keywords: plastic load, plastic work, strain hardening, plastic work-tangent criterion

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Authors: C. Senabre, S. Valero, M. Lopez, E. Velasco, M. Sanchez

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This paper focuses on an application of linear mixed models to short-term load forecasting. The challenge of this research is to improve a currently working model at the Spanish Transport System Operator, programmed by us, and based on linear autoregressive techniques and neural networks. The forecasting system currently forecasts each of the regions within the Spanish grid separately, even though the behavior of the load in each region is affected by the same factors in a similar way. A load forecasting system has been verified in this work by using the real data from a utility. In this research it has been used an integration of several regions into a linear mixed model as starting point to obtain the information from other regions. Firstly, the systems to learn general behaviors present in all regions, and secondly, it is identified individual deviation in each regions. The technique can be especially useful when modeling the effect of special days with scarce information from the past. The three most relevant regions of the system have been used to test the model, focusing on special day and improving the performance of both currently working models used as benchmark. A range of comparisons with different forecasting models has been conducted. The forecasting results demonstrate the superiority of the proposed methodology.

Keywords: short-term load forecasting, mixed effects models, neural networks, mixed effects models

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Authors: Ali Mahmoud, Shadi Najjar, Mounir Mabsout, Kassim Tarhini

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Empirical expressions for estimating the wheel load distribution and live-load bending moment are typically specified in highway bridge codes such as the AASHTO procedures. The purpose of this paper is to analyze the reliability levels that are inherent in reinforced concrete slab bridges that are designed based on the simplified empirical live load equations in the AASHTO LRFD procedures. To achieve this objective, bridges with multi-lanes (three and four lanes) and different spans are modeled using finite-element analysis (FEA) subjected to HS20 truck loading, tandem loading, and standard lane loading per AASHTO LRFD procedures. The FEA results are compared with the AASHTO LRFD moments in order to quantify the biases that might result from the simplifying assumptions adopted in AASHTO. A reliability analysis is conducted to quantify the reliability index for bridges designed using AASHTO procedures. To reach a consistent level of safety for three- and four-lane bridges, following a previous study restricted to one- and two-lane bridges, the live load factor in the design equation proposed by AASHTO LRFD will be assessed and revised if needed by alternating the live load factor for these lanes. The results will provide structural engineers with more consistent provisions to design concrete slab bridges or evaluate the load-carrying capacity of existing bridges.

Keywords: reliability analysis of concrete bridges, finite element modeling, reliability analysis, reinforced concrete bridge design, load carrying capacity

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Authors: Chil-Chyuan Kuo, Chen-Hsuan Tsai

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This study presents a cost-effective approach for rapid fabricating modeling platforms utilized in fused deposition modeling system. A small-batch production of modeling platforms about 20 pieces can be obtained economically through silicone rubber mold using vacuum casting without applying the plastic injection molding. The air venting systems is crucial for fabricating modeling platform using vacuum casting. Modeling platforms fabricated can be used for building rapid prototyping model after sandblasting. This study offers industrial value because it has both time-effectiveness and cost-effectiveness.

Keywords: vacuum casting, fused deposition modeling, modeling platform, sandblasting, surface roughness

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Authors: S. Neelima, P. S. Subramanyam

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The major tool for distribution planning is load forecasting, which is the anticipation of the load in advance. Artificial neural networks have found wide applications in load forecasting to obtain an efficient strategy for planning and management. In this paper, the application of neural networks to study the design of short term load forecasting (STLF) Systems was explored. Our work presents a pragmatic methodology for short term load forecasting (STLF) using proposed two-stage model of wavelet transform (WT) and artificial neural network (ANN). It is a two-stage prediction system which involves wavelet decomposition of input data at the first stage and the decomposed data with another input is trained using a separate neural network to forecast the load. The forecasted load is obtained by reconstruction of the decomposed data. The hybrid model has been trained and validated using load data from Telangana State Electricity Board.

Keywords: electrical distribution systems, wavelet transform (WT), short term load forecasting (STLF), artificial neural network (ANN)

Procedia PDF Downloads 396