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STLF using Neural Networks and Fuzzy for anomalous load scenarios - A case study for Hajj

STLF using Neural Networks and Fuzzy for anomalous load scen...

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International Conference on Electrical and computer engineering (ICECE)

作者： Suman Ahmmed Mohammad Asif Ashraf Khan Md. Khairul Hasan Ahmed Yousuf Saber Mohammad Nurul Huda Mohammad Zahidur Rahman Department of Computer Science and Engineering United International University Dhaka Bangladesh Department of Computer Science and Engineering Ahsanullah University of Science and Technology Bangladesh Real-Time Power and Intelligent Systems Laboratory Missouri University of Science and Technology USA Department of Computer Science and Engineering Jahangir Nagar University Dhaka Bangladesh

Load Forecasting has an important role in load generation, scheduling, planning etc. in power system. Different computational intelligent techniques are used in Short Term Load Forecasting (STLF) to make it more effective. Neural Networks (NN) is an effective mapping algorithm that can map complex input-output relationships, which is an important technique to do STLF having existing dataset. Usually a proper NN is sufficient to achieve accepted level of performance. But different load dataset may bear some irregular nature of load demand scenario due to having special events, where accuracy of NN suffers significantly. To enhance the performance for those situations, the authors propose a hybrid STLF approach-Neural Networks and Fuzzy (NNF) method. The authors first try to select the best possible trained NN and do STLF. Considering historical data trend and of existing errors of NN solution, for those special days, NNF determine the Load Change trend. Fuzzy Inference Rules (FIR) have been developed to further improvement by fuzzy method. In fuzzy part the NNF apply FIR on two inputs: STLF of NN and Load Change trend, to enhance the performance of STLF for special events. To evaluate the proposed method it is applied on the dataset of Regional Power Control Center of Saudi Electricity Company, Western Operation Area (SEC-WOA). Since the authors deal with the daily load dataset of Saudia Arabia of Hijri years (Arabian years), Hajj has been chosen as one of the anomalous load scenario. Empirical results show that for Hajj event of Hijri 1428 year, the accuracy of STLF by NN is approximately 6.37%, whereas proposed NNF can decline the error at only 1.92%.

关键词： Artificial neural networks Data structures Boolean functions Load forecasting Finite impulse response filter Forecasting Load modeling

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Dynamic system eigenvalue extraction using a linear echo state network for small-signal stability analysis - a novel application

Dynamic system eigenvalue extraction using a linear echo sta...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jiaqi Liang Jing Dai Ganesh K. Venayagamoorthy Ronald G. Harley Honorary Research Associate University of KwaZulu-Natal Durban South Africa School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Real Time Power and Intelligent Systems Laboratory Missouri University of Science and Technology Rolla MO USA

A large nonlinear dynamic system usually has complex dynamic modes corresponding to the system's eigenvalues. These eigenvalues govern the system's local behavior and thus are critical information for designing system operation and control strategies. Without the availability of the system's analytical model, which is often the case for large nonlinear systems, the system's eigenvalues need to be estimated. A linear echo state network (ESN) based method for extracting observable eigenvalues of a dynamic system together with the participation factors of these eigenvalues in the accessible system states is presented in this paper. A linear ESN is first trained to track the dynamic system's local responses under injected small perturbation signals. The dynamic system's eigenvalues are then extracted from the ESN's weight matrices. Given the merit of fast training of ESNs, the ESN can be quickly retrained once the system operating point changes, and the system eigenvalues can be reestimated. Application of the proposed eigenvalue extraction method in the power system small-signal analysis is presented to demonstrate the effectiveness of the proposed method.

关键词： Eigenvalues and eigenfunctions Power system dynamics Nonlinear dynamical systems Training Generators

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Using hardware methods to improve time-predictable performance in real-time Java systems 09

Using hardware methods to improve time-predictable performan...

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7th International Workshop on Java Technologies for real-time and Embedded systems, JTRES 2009

作者： Whitham, Jack Audsley, Neil Schoeberl, Martin Real-Time Systems Group Dept. of Computer Science University of York York United Kingdom Institute of Computer Engineering Vienna University of Technology Austria

ISBN: (纸本)9781605587325

This paper describes hardware methods, a lightweight and platform-independent scheme for linking real-time Java code to co-processors implemented using a hardware description language (HDL). Intended for use in embedded systems, hardware methods have similar semantics to the native methods used to interface Java code to legacy C/C++ software, but are also time-predictable, facilitating accurate worstcase execution time (WCET) analysis. By reference to several examples, the paper demonstrates the applicability of hardware methods and shows that they can (1) reduce the WCET of embedded real-time Java, and (2) improve the quality of WCET estimates in the presence of infeasible paths. Copyright 2009 ACM.

关键词： computer hardware description languages

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Integrated modeling of software product lines with feature models and classification trees

Integrated modeling of software product lines with feature m...

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1st International Workshop on Model-Driven Approaches in Software Product Line engineering, MAPLE 2009 - Collocated with the 13th International Software Product Line Conference, SPLC 2009

作者： Oster, Sebastian Markert, Florian Scḧurr, Andy Müller, Werner Real-Time Systems Group Technische Universität Darmstadt Germany Computer Systems Group Technische Universität Darmstadt Germany Global Systems Engineering Methods Adam Opel GmbH Germany

Software Product Lines (SPLs) are an approach to improve reusability of software in a large number of products that share a common set of features. In SPLs, Feature Models (FMs) are frequently used to model commonalities and variabilities. However, according to the best of our knowledge, there are no approaches to automatically generate test cases on the basis of a stand-alone FM. We introduce a method, which fills this gap. In single system software testing, Classification Trees (CTs) are a proven approach for generating test cases derived from the original system specification. In this paper, we explore the relations and similarities between FMs and CTs and integrate both methods to a unified approach called Feature Model for Testing (FMT).

关键词： Software testing

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Absolute high-precision localisation of an unmanned ground vehicle by using real-time aerial video imagery for geo-referenced orthophoto registration

Absolute high-precision localisation of an unmanned ground v...

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21. Fachgesprach Autonome Mobile Systeme, AMS 2009 - 21st Expert Discussion on Autonomous Mobile systems, AMS 2009

作者： Kuhnert, Lars Ax, Markus Langer, Matthias Nguyen Van, Duong Kuhnert, Klaus-Dieter FB 12 - Electrical Engineering and Computer Science Institute for Real-Time Learning Systems University of Siegen Hölderlinstr. 3 57068 Siegen Germany

ISBN: (纸本)9783642102837

This paper describes an absolute localisation method for an unmanned ground vehicle (UGV) if GPS is unavailable for the vehicle. The basic idea is to combine an unmanned aerial vehicle (UAV) to the ground vehicle and use it as an external sensor platform to achieve an absolute localisation of the robotic team. Beside the discussion of the rather naive method directly using the GPS position of the aerial robot to deduce the ground robot's position the main focus of this paper lies on the indirect usage of the telemetry data of the aerial robot combined with live video images of an onboard camera to realise a registration of local video images with apriori registered orthophotos. This yields to a precise driftless absolute localisation of the unmanned ground vehicle. Experiments with our robotic team (AMOR and PSYCHE) successfully verify this approach.

关键词： Ground vehicles

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U-FIPI: Ubiquitous sensor network service infra supporting bidirectional location-awareness between mobile nodes and fixture nodes

U-FIPI: Ubiquitous sensor network service infra supporting b...

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2009 6th IEEE Consumer Communications and Networking Conference, CCNC 2009

作者： Kim, Baek-Gyu Kim, Tae-Hyon Kang, Soon-Ju Lee, Jae Shin Shon, Jin Ho Go, Sang Chul School of Electrical Engineering and Computer Science Kyungpook National University Daegu Korea Republic of Advanced Real-Time Systems Co. Ltd. Daegu Korea Republic of

ISBN: (纸本)9781424423095

We introduce a ubiquitous sensor network system, called U-FIPI, to monitor the early symptom of fire and intruder in indoor environment. Especially, this system adapts a novel approach to provide bidirectional locationawareness service between various mobile nodes and U-FIPI fixture nodes making it suitable for total service as a new concept of building management system. ©2009 IEEE.

关键词： Sensor nodes

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An introduction to the echo state network and its applications in power system

An introduction to the echo state network and its applicatio...

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2009 15th International Conference on Intelligent System Applications to Power systems, ISAP '09

作者： Dai, Jing Venayagamoorthy, Ganesh K. Harley, Ronald G. Georgia Institute of Technology School of Electrical and Computer Engineering Atlanta GA 30332 United States Department of Electrical and Computer Engineering Real-Time Power and Intelligent Systems Laboratory Missouri University of Science and Technology Rolla MO 65409 United States

ISBN: (纸本)9781424450985

Echo State Network (ESN) is a new type of Recurrent Neural Network (RNN) proposed in recent years. The training process of ESN is easier and requires less computational effort than regular RNN which has the same size. Due to its high modeling capability of complex dynamic system, ESN has been used in various power system applications such as power system nonlinear load modeling and true harmonic current detection, wide area monitoring, intelligent control of an Active Power Filter (APF), overhead conductor thermal dynamics identification, wind speed or water inflow forecasting, etc. This paper introduces the basic concept and the offline and online training algorithms of the ESN in detail and reviews the state of the art of ESN applications in power systems. © 2009 IEEE.

关键词： Recurrent neural networks

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Computational intelligence approach to load forecasting - A practical application for the desert of Saudi Arabia

Computational intelligence approach to load forecasting - A ...

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2009 12th International Conference on computer and Information Technology, ICCIT 2009

作者： Ahmmed, Suman Rahman, Dewan Md Fayzur Hasan, Md Khairul Saber, Ahmed Yousuf Rahman, Mohammad Zahidur Department of Computer Science and Engineering United International University Dhaka Bangladesh Department of Computer Science and Engineering Ahsanullah University of Science and Technology Bangladesh Real-Time Power and Intelligent Systems Laboratory Missouri University of Science and Technology United States Department of Computer Science and Engineering Jahangimagar University Dhaka Bangladesh

ISBN: (纸本)9781424462841

This paper presents the development of an Artificial Neural Networks and Particle Swarm Optimization (ANN-PSO) based short-term load forecasting model with improved generalization technique for the Regional Power Control Center of Saudi Electricity Company, Western Operation Area (SEC-WOA). Weather, load demand, wind speed, wind direction, heat, sunlight, etc. are quite different in a desert land than other places. Thus this model is different from a typical forecasting model considering inputs and outputs. In this research paper two steps have been introduced, first load forecasting made by mapping mechanism and then optimization technique applied to improve its accuracy. This paper includes ANN and PSO models for 24-hours ahead load forecasting. ANN is an effective mathematical tool for mapping complex relationships. It is also successful for doing forecasting, categorization, classification, and so forth. On the other hand, PSO is the most promising optimization tool. It has better information sharing and conveying mechanism;it has better balance of local and global searching abilities;and can handle huge multi-dimensional optimization problems efficiently with hundreds of thousands of constraints. Thus PSO is chosen as the optimization model ofthe weight matrix of ANN. Results show that the proposed ANN-PSO performs much better than ANN for the load forecasting in a desert like Saudi Arabia. ©2009 IEEE.

关键词： Neural networks

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Load forecasting of a desert: A computational intelligence approach

Load forecasting of a desert: A computational intelligence a...

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2009 15th International Conference on Intelligent System Applications to Power systems, ISAP '09

作者： Saber, Ahmed Yousuf Al-Shareef, Abdulaziz Real-Time Power and Intelligent Systems Laboratory Missouri University of Science and Technology Rolla MO 65409-0040 United States Electrical and Computer Engineering Department King Abdulaziz University Jeddah KSA Saudi Arabia

ISBN: (纸本)9781424450985

This paper presents artificial neural networks and particle swarm optimization (ANN-PSO) based short-term load forecasting model with improved generalization technique for the Regional Power Control Center of Saudi Electricity Company, Western Operation Area (SEC-WOA) of Saudi Arabia. Weather, load demand, wind speed, wind direction, heat, sunlight, etc. are quite different in a desert land than other places. Thus this model is different from a typical forecasting model considering inputs and outputs. In this research, two models are implemented - firstly load forecasting model for prediction;however, it is not sufficient for desired level of accurate forecasting, and secondly, optimization to improve the results up to at least better than existing results. This paper includes ANN and PSO models for 24-hours ahead load forecasting. ANN is a mathematical tool for mapping complex relations;it is well proved for the successful use of prediction, function approximation with dynamics, categorization, classification, and so forth. In this research, 24- step ahead calculations are performed in the ANN model and results are moderate. On the other hand, PSO is the most promising optimization tool. It is a swarmed based optimization method;it has better information sharing and conveying mechanism;it has better balance of local and global searching abilities;it can handle huge multi-dimensional optimization problems efficiently with hundreds of thousands of constraints. Thus PSO is chosen as the optimization tool that is applied on the weight matrix of ANN to improve results. In this research, PSO reliably and accurately tracks the continuously changing weights of ANN for uncertain load demand. By analyzing the model of ANN for the load-forecasting problem of SEC-WOA with hundreds of thousands of data and changing-uncertain load demand, the PSO is applied for the ANN weight adjustment and to optimize the uncertain load demand, as the ANN is not an optimization method. Results

关键词： Neural networks

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Periodic Power Management Schemes for real-time Event Streams

Periodic Power Management Schemes for Real-Time Event Stream...

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Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference

作者： Kai Huang Luca Santinelli Jian-Jia Chen Lothar Thiele Giorgio C.Buttazzo Computer Engineering and Networks Laboratory Real-Time Systems Laboratory Scuola Superiore Sant’Anna of Pisa

Power dissipation has constrained the performance boosting of modern computer systems in the past *** power management(DPM) has been implemented in many systems to change the system(or device) state dynamically to reduce the power *** paper explores how to efficiently and effectively reduce the energy consumption to handle event streams with hard real-time or quality of service(Qo S) *** adopt real-time Calculus to describe the event arrival by arrival curves in the interval *** reduce the implementation overhead,we propose a periodic scheme to determine when to turn on/off the system(or device).This paper first presents two approaches to derive periodic scheme to cope with systems with only one event stream,in which one approach derives an optimal solution for periodic power management with higher complexity and the other derives approximated solutions with lower ***,extensions are proposed to deal with multiple event *** results reveal the effectiveness of our approaches.

关键词： Power Management Energy Minimization real-time Event Streams real-time Calculus QoS

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