Short-Term Load Forecasting Using Semigroup Based System-Type Neural Network
|
|
- Avis Parks
- 5 years ago
- Views:
Transcription
1 Short-erm Load Forecasting Using Semigroup Based System-ype Neural Network Kwang Y. Lee, Fellow, IEEE, and Shu Du Abstract his paper presents a methodology for short-term load forecasting using a semigroup-based system-type neural network. A technique referred to as algebraic decomposition is proposed for the neural network architecture, where the network is decomposed into a semigroup channel and a function channel. he semigroup channel, made of coefficient vector, is shown to exhibit the dependency of the load on temperature, and the function channel extracts the basis vector to represent the fundamental characteristics of daily load cycles. Regression and rearrangement methods are applied to handle the roughness of the load data surface, and interpolation and extrapolation of coefficient vector is achieved based upon the hourly temperature. he recombination of basis vector and coefficient vector at each hour gives the load forecast. his methodology is verified by testing on the load data from New England Independent System Operator (ISO) and achieves satisfactory results. Index erms Algebraic decomposition, load forecasting, neural network, system-type architecture. I. INRODUCION ORECASING load accurately is very important for Felectric utilities in a competitive environment created by the electric industry deregulation. In order to supply high quality electric energy to the customer in a secure and economic manner, an electric company faces many economical and technical problems in operation, planning and control of an electric energy system []. Load forecasting helps an electric utility to make important decisions including decisions on purchasing and generating electric power, load switching, and infrastructure development. Load forecasting is also significant for energy suppliers, financial institutions, and other participants in electric energy generation, transmission, distribution, and markets []. Based on time intervals, load forecasting can be divided into three main categories: short-term forecasting, mediumterm forecasting and long-term forecasting. Short-term forecasting usually forecasts one hour to one week, mediumterm forecasting concerns the future load from a week to a month and long-term forecasting is often for period longer than a year. he long-term and medium-term forecasts are applied to determine the capacity of generation, transmission, or distribution system, and the type of facilities required in his work was supported in part by the National Science Foundation under grant ECCS K. Y. Lee and S. Du are with the Department of Electrical and Computer Engineering, Baylor University, Waco, X ( Kwang_Y_Lee@baylor.edu; Shu_Du@baylor.edu). transmission expansion planning, annual hydrothermal maintenance scheduling, etc. he short-term forecast is used for controlling and scheduling power system, and also as inputs to load flow study or contingency analysis []. Additionally, short-term load forecasting can assist in estimating load flows and in making decisions to prevent overloading. he load can be regarded as a dynamic system which is affected by two main factors: time of the day and weather conditions. he time dependence of the load reflects the existence of a daily load pattern, which may vary for different weekdays and seasons. Among weather variables, temperature usually is the dominant factor influencing load. Humidity and wind speed may also impact on power consumption. In general, the load has two distinct patterns: weekday and weekend patterns. In addition, holiday patterns are different from non-holiday patterns. Section II describes the proposed approach. In Section III, the proposed approach will be applied to the forecasting problem. Finally, some conclusions are drawn and presented in Section IV. II. HE PROPOSED APPROACH A. Failures/Shortcomings of Conventional Neural Networks Recently, there is a shift occurred in the overall architecture of neural networks from simple or component-type networks to system-type architectures. he most popular architecture seems to be the Modular Connectionist Architecture, which is advocated by Jacobs and Jordan [3]. he most serious drawback in the design of system-type neural networks is the short of a cohesive discipline in the architectural design and in the design of the learning algorithm. Virtually, the entire design is based upon an intuitive basis. o illustrate the lack of a cohesive discipline, in [4], the partitioning of components corresponds to separation of variables, which works if the variables are separated and does not work if the variables are not separated []-[7]. B. System-ype Neural Network Method In previous papers []-[], a system-type neural network which implemented extrapolation was proposed. In this metho the distributed parameter system (DPS) surface determined by a given data set was expanded by extrapolating along one axis. In general the load is a function, Load = f ( Day, Hour, Weather, Customerclasses), and is often considered as Load = f ( Day, Hour), which is
2 parameterized by weather and customer classes. In this paper, however, the load is modeled as Load = f ( emperature, Hour) due to the importance of the temperature among many factors that affect the load. In the following, it will be shown that the load can be represented in the form: L( emperature, Hour) = C( ) E( H ), where E (H) is a set of linearly independent orthonormal basis functions, and C ( ) is a preliminary coefficient vector as a function of the hourly temperatures. In order to obtain E (H ) and C ( ), a technique referred to as algebraic decomposition, which aims to approximate and model load data set, is applied. his process at first requires load profile L (, H ) to be parameterized as { L ( H )}, = i,,, then converts n chosen members using l Gram-Schmidt process to obtain an orthonormal basis set E( H ) = [ e ( H ), e ( H ),, en ( H )]. Finally the least squares method is performed to determine the coefficient vector C ( ), i.e., L(, H ) = C( ) E( H ) () where E H ) = [ e ( H ), e ( H ),, e ( n ( H C ) = [ c ( ), c ( ),, c ( )]. ( n Neural networks are being used for systems described by partial differential equations (PDEs) []. he system-type architecture is shown in Fig., which implements a load function L (, H ). he proposed architecture realizes a system-type structure using two neural network channels, a Function Channel and a Semigroup Channel. )] in E (H). he outputs of the two channels are (internally) linearly summed so that the channels span an n-dimensional function space. he Semigroup Channel can be adapted from the Diagonal Recurrent Neural Network (DRNN) or the Elman architecture [], in which the input is separated into a dynamic scalar component and one static vector component, the initial vector C (). he output of the Semigroup Channel is a vector C ( ), which is associated to the dynamic input and to the static input C () by the semigroup property: C( ) = Φ( ) C() () where Φ + ) = Φ( ) Φ( ). ( C. Learning Algorithm of Proposed System-ype Neural Network Since it uses RBF Networks, the first component of the system, namely the Function Channel, can be designe rather than trained. he second component, the Semigroup Channel, can be trained in a successive manner as illustrated in Fig.. In each of the trainings, the Semigroup Channel receives a preliminary coefficient vector C ( ) as input composed by the dynamic scalar component and the static initial vector component, and gives a smoothened coefficient vector C ( ) as output. herefore the primary objective of training is to replicate (an if necessary, to smoothen) the vector C ( ) with a vector C ( ), which has the semigroup property. However, there is a secondary objective in training the Semigroup Channel, that is, the channel must also replicate the semigroup property of the trajectory by gradually acquiring a semigroup property in its own weight space. he acquisition of this semigroup property in the weight space becomes the basis for extrapolation. In order to obtain this property gradually, it is necessary that the training in this second step (semigroup tracking) occurs in a gradual manner, as shown in Fig.. he semigroup channel is trained along each sliced sub-trajectories until a convergence of the overall weight pattern is found. Fig.. System-type architecture. Look for weight convergence W4 W3... N he first component, Function Channel, outputs a vector of basis functions E (H ), while the Semigroup Channel supplies the Function Channel with a coefficient vector C ( ) as a function of the index. hese two channels realize a semigroup-based implementation of the mapping L (, H ). he Function Channel can have Radial Basis Function (RBF) architecture [3]. It consists of n RBF networks. Each of these networks operates as one of the n orthonormal basis functions W W Fig.. Overview of new training method. data point D. Regression Method Regession method is one of the most widely used approaches for load forecasting []. However, it is not the
3 3 main tool to perform load forecasting in this paper. Because the electric load is a complex system influenced by many factors, it can be formulated as in the following form: L = L + L + L (3) total base weather other factors where L is the base load which is caused by time factor; base L is the weather sensitive load which is due to weather weather variables, in which temperature usually is dominant among various weather variables; and L is a component of other factors the load resulting from other factors. he objective of applying regression here is to reduce effects caused by other unknown factors as much as possible or even eliminate them. herefore, the output of regression method is supposed to be highly correlated with time and weather variables. E. Rearrangement of Load Since load and temperatures are already tightly correlate the load consumptions of a certain time interval fluctuate because of different temperatures. Assuming the load changes smoothly with temperature, the regression load is rearranged according to the magnitudes of hourly temperature instead of the original time sequences. If the data surface of the rearranged load is smooth, the extrapolation along a single coordinate (temperature) can be performed. Because the temperature is changing hourly, it is necessary to rearrange the load with respect to the temperature at each hour so that a smooth load surface can be obtained. Fig. 3 illustrates the process. he depth of color represents magnitude of temperature. previous (historical) days, or extrapolation if temperature forecast exceeds the temperature bounds. herefore, the load forecasting at this hour can be achieved by recombining the basis vector and the interpolated coefficient vector or the extrapolated coefficient vector. Exactly same procedure is executed for 4 hours so as to obtain the forecasting loads of the target day. Fig. 4. Interpolation and extrapolation of the coefficient. III. SIMULAION RESULS he proposed forecasting approach is tested by using the historical load data obtained from New England Independent System Operator (ISO). he hourly temperatures of each day are weighted average values of 8 weather stations in the New England area in degrees Fahrenheit. In the simulation, load data for the year is chosen for testing. he load is classified into two groups: weekdays and weekends load. In each group, a moving window containing previous four weeks load is created for each target day. Actual raw load data in the moving window passes through the regression filter, and then algebraic decomposition decomposes the regression data into a basis vector and a coefficient vector of dimensionality n, where n is set to two. he results are analyzed by the following formulas: ) Standard Deviation N σ = [ L( Lˆ( ] (4) N d = Fig. 3. Rearrangement of the regression load. F. Interpolation and Extrapolation Interpolation and extrapolation involve only the coefficient vector generated by the Semigroup Channel. Fig. 4 illustrates the interpolation and extrapolation procedures for a given hour. Red circles represent the historical load and green circles represent the forecasting load. Because the hourly temperatures for a forecasting day are assumed to be already known, the coefficient vector can be found by interpolation if the forecasted temperature at a given hour does not exceed the temperature bounds experienced in where L ( is empirical load data for a given day (d) and hour (, L ˆ( is the corresponding load forecast. ) Percent Error Error = L( Lˆ( / L( () Here an arbitrary weekday is chosen as the forecasting day to show the simulation results. he weekday loads of previous four weeks are selected as historical data. Figs. and 6 show the regression load before the rearrangement and after the rearrangement. Notice that the axis in Fig. 6 is changed from Day to emperature by the rearrangement. It is obvious that the rearranged load surface is much smoother than the one before rearrangement.
4 4 x 4 Regression Load before Rearrangement Comparison of Original and Smoothened Coefficient Vector Smoothened Original.. MW. C Day Hour Fig. 7. Comparison of the original and smoothened coefficient C. Fig.. Regression load before rearrangement. Regression Load after Rearrangement Comparison of Original and Smoothened Coefficient Vector. x 4 C. -. MW Hour - - Smoothened Original Fig. 8. Comparison of the original and smoothened coefficient C. Extrapolation est C Fig. 6. Regression load after rearrangement. After the rearrangement, the load is decomposed into a basis vector and a preliminary coefficient vector. o fulfill those two objectives during the training of the Semigroup Channel, at first the preliminary coefficient vector needs to be smoothened and replicated. herefore the Semigroup Channel is trained with the initial coefficient vector C ( ) = [ c (), c()] and the dynamic scalar component using the proposed progressive training method. Figs. 7 and 8 show the smoothened coefficient vector plotted in two components. Secondly, it is necessary to determine whether the semigroup property occurs in the weight space. herefore the extrapolation test is performed to observe if the weight changes of the neural network can be replaced with a weight change model which owns the semigroup property. Figs. 9 and show that extrapolation can be carried out based on the similarity of two curves. When a forecasted temperature exceeds the temperature bounds of the moving window, extrapolation of the coefficient vector is require which is illustrated in Figs. and. C Smoothened C Fig. 9. Extrapolation test for C. C Smoothened C Extrapolation est Extrapolation est C Extrapolation est - Fig.. Extrapolation test for C.
5 C Smoothened C Fig.. Extrapolation of C. C Smoothened C Fig.. Extrapolation of C. Extrapolation of C Extrapolation Extrapolation of C Extrapolation Here three typical weeks are selected to show the performance of the proposed metho winter, spring, and summer weeks. he results include percent error and standard deviation at each hour of each day, both being calculated from the regression load and the actual load. In the first week which is a typical winter week, the highest error is 7.9% at 3: on Wednesday and the lowest error is.% at 3: on Sunday for the regression load. On the other han for the actual load in the same week, the highest error is 9.87% at 8: on uesday and the lowest error is.4% at 4: on Friday. It should be noticed that uesday is the New Year s Day, but we still have treated it as a normal weekday. herefore the errors for this day are relatively large. In the th week which is a typical spring week, the highest error is.% at : on Friday and the lowest error is nearly.% at 6: on uesday for the regression load. With respect to the actual loa the highest error is 8.47% at 9: on Saturday and the lowest error is nearly % at : on Wednesday. In the 3th week, which is a typical summer week, the highest error is 8.84% at : on Friday and the lowest error is.3% at 9: on Saturday for the regression load. For the actual loa the highest error is 8% at 8: on uesday and the lowest error is nearly % at 4: on uesday and 8: on Wednesday. As shown above, high hourly percent errors with respect to the regression load may appear for some hours, e.g., 8.84% in the 3th week. his is due to relatively abnormal hourly temperature changes in the forecasting day, like sudden increase or drop, compared to the historical temperatures of previous days. able I shows the results of the regression load averaged for the whole year. Friday shows the best forecasting result ABLE I SAISICS OF WHOLE YEAR AVERAGE FORECASING RESULS (REGRESSION LOAD). Mon. ue. Wed. hr. Fri. Sat. Sun. Hour Avg
6 6 and Saturday shows the worst. Based on the actual loa hursday shows the best result and Saturday shows the worst. Large errors are in late afternoons for weekdays and mornings for weekends. IV. CONCLUSIONS In this paper, a new neural network approach is developed for short-term load forecasting. his method is applied to the regression load instead of actual empirical load data. Rearrangement of the regression load along the temperature axis assures the smoothness of the coefficient vector so that the interpolation and extrapolation can be carried out. he results of the whole year show that the proposed method achieves satisfactory forecasting results with respect to regression load. However, the errors resulting from the use of actual load are generally beyond the short-term load forecasting requirement. his results from the existence of the load component which is dependent on unknown factors in the given load data. If the given load and temperature data are highly correlated to each other, it is expected that much better results can be delivered and the regression method can be removed from the proposed approach. systems using neural networks," Proc. of the American Control Conference, Vol. 6, pp , Nov.. [] I. Moghram and S. Rahman, "Analysis and evaluation of five short-term load forecasting techniques," IEEE rans. Power Systems, vol. 4, pp , Nov VII. BIOGRAPHIES Kwang Y. Lee received his B.S. degree in Electrical Engineering from Seoul National University, Korea, in 964, M.S. degree in Electrical Engineering from North Dakota State University, Fargo, in 968, and Ph.D. degree in System Science from Michigan State University, East Lansing, in 97. He has been with Michigan State, Oregon State, Univ. of Houston, Penn State, and Baylor University, where he is now a Professor and Chair of Electrical and Computer Engineering. His interests include power system control, operation, planning, and intelligent system applications to power systems. Dr. Lee is a Fellow of IEEE, Associate Editor of IEEE ransactions on Neural Networks, and Editor of IEEE ransactions on Energy Conversion. He is also a registered Professional Engineer. Shu Du received his B.S. degree in Automation from Beijing University of Chemical echnology in 3. He is currently enrolled in the Master program in Electrical and Computer Engineering at Baylor University, exas. His research interests are in neural networks and power systems. V. ACKNOWLEDGMEN he authors gratefully acknowledge the support by National Science Foundation under Grant #7838. VI. REFERENCES [] K. Y. Lee, Y.. Cha, and J. H. Park, "Short-term load forecasting using an artificial neural network," IEEE rans. Power Systems, vol. 7, pp. 4-3, Feb. 99. [] J. H. Chow, Applied Mathematics for Restructured Electric Power Systems: Optimization, Control, and Computational Intelligence. New York: Springer-Verlag,, ch.. [3] R. Jacobs and M. Jordan, "A competitive modular connectionist architecture," Advances in Neural Information Processing Systems, Vol. 3, p , 99. [4] A. Atiya, R. Aiya and S. Shaheen, "A Practical Gated Expert System Neural Network," IEEE International Joint Conference on Neural Networks, vol., pp , 998. [] K. Y. Lee, J. P. Velas, and B. H. Kim, "Development of an intelligent monitoring system with high temperature distributed fiberoptic sensor for fossil-fuel power plants," IEEE Power Engineering Society General Meeting, pp. 3-3, Jun. 4. [6] B. H. Kim, J. P. Velas, and K. Y. Lee, "Development of intelligent monitoring system for fossil-fuel power plants using system-type neural networks and semigroup theory," IEEE Power Engineering Society General Meeting, pp ,. [7] B. H. Kim, J. P. Velas, and K. Y. Lee, "Semigroup based neural network architecture for extrapolation of enthalpy in a power plant," International Conference on Intelligent System Applications to Power Systems, pp. 9-96,. [8] B. H. Kim, J. P. Velas, and K. Y. Lee, "Semigroup based neural network architecture for extrapolation of mass unbalance for rotating machines in power plants," International Federation of Automatic Control (IFAC) Symposium on Power Plants and Power Systems Control, in CD, 6. [9] B. H. Kim, J. P. Velas, and K. Y. Lee, "Short-term load forecasting using system-type neural network architecture," International Joint Conference on Neural Networks, pp , 6. [] N. J. Hobbs, B. H. Kim, and K. Y. Lee, "Long-term load forecasting using system type neural network architecture," International Conference on Intelligent Systems Applications to Power Systems 7, pp. -7, Nov. 7. [] R. Padhi and S. N. Balakrishnan, "Proper orthogonal decomposition based feedback optimal control synthesis of distributed parameter
System-Type Neural Network Architectures for Power Systems
6 International Joint Conference on Neural Networks Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada July 16-1, 6 System-Type Neural Network Architectures for Power Systems Kwang Y. Lee, Fellow,
More informationDay Ahead Hourly Load and Price Forecast in ISO New England Market using ANN
23 Annual IEEE India Conference (INDICON) Day Ahead Hourly Load and Price Forecast in ISO New England Market using ANN Kishan Bhushan Sahay Department of Electrical Engineering Delhi Technological University
More informationElectric Load Forecasting Using Wavelet Transform and Extreme Learning Machine
Electric Load Forecasting Using Wavelet Transform and Extreme Learning Machine Song Li 1, Peng Wang 1 and Lalit Goel 1 1 School of Electrical and Electronic Engineering Nanyang Technological University
More informationLoad Forecasting Using Artificial Neural Networks and Support Vector Regression
Proceedings of the 7th WSEAS International Conference on Power Systems, Beijing, China, September -7, 2007 3 Load Forecasting Using Artificial Neural Networks and Support Vector Regression SILVIO MICHEL
More informationAn Improved Method of Power System Short Term Load Forecasting Based on Neural Network
An Improved Method of Power System Short Term Load Forecasting Based on Neural Network Shunzhou Wang School of Electrical and Electronic Engineering Huailin Zhao School of Electrical and Electronic Engineering
More informationA Boiler-Turbine System Control Using A Fuzzy Auto-Regressive Moving Average (FARMA) Model
142 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 18, NO. 1, MARCH 2003 A Boiler-Turbine System Control Using A Fuzzy Auto-Regressive Moving Average (FARMA) Model Un-Chul Moon and Kwang Y. Lee, Fellow,
More informationShort-Term Electrical Load Forecasting for Iraqi Power System based on Multiple Linear Regression Method
Volume 00 No., August 204 Short-Term Electrical Load Forecasting for Iraqi Power System based on Multiple Linear Regression Method Firas M. Tuaimah University of Baghdad Baghdad, Iraq Huda M. Abdul Abass
More informationShort Term Load Forecasting Based Artificial Neural Network
Short Term Load Forecasting Based Artificial Neural Network Dr. Adel M. Dakhil Department of Electrical Engineering Misan University Iraq- Misan Dr.adelmanaa@gmail.com Abstract Present study develops short
More informationSHORT TERM LOAD FORECASTING
Indian Institute of Technology Kanpur (IITK) and Indian Energy Exchange (IEX) are delighted to announce Training Program on "Power Procurement Strategy and Power Exchanges" 28-30 July, 2014 SHORT TERM
More informationSMART GRID FORECASTING
SMART GRID FORECASTING AND FINANCIAL ANALYTICS Itron Forecasting Brown Bag December 11, 2012 PLEASE REMEMBER» Phones are Muted: In order to help this session run smoothly, your phones are muted.» Full
More informationANN and Statistical Theory Based Forecasting and Analysis of Power System Variables
ANN and Statistical Theory Based Forecasting and Analysis of Power System Variables Sruthi V. Nair 1, Poonam Kothari 2, Kushal Lodha 3 1,2,3 Lecturer, G. H. Raisoni Institute of Engineering & Technology,
More informationShort Term Load Forecasting Using Multi Layer Perceptron
International OPEN ACCESS Journal Of Modern Engineering Research (IJMER) Short Term Load Forecasting Using Multi Layer Perceptron S.Hema Chandra 1, B.Tejaswini 2, B.suneetha 3, N.chandi Priya 4, P.Prathima
More informationpeak half-hourly New South Wales
Forecasting long-term peak half-hourly electricity demand for New South Wales Dr Shu Fan B.S., M.S., Ph.D. Professor Rob J Hyndman B.Sc. (Hons), Ph.D., A.Stat. Business & Economic Forecasting Unit Report
More informationHow Accurate is My Forecast?
How Accurate is My Forecast? Tao Hong, PhD Utilities Business Unit, SAS 15 May 2012 PLEASE STAND BY Today s event will begin at 11:00am EDT The audio portion of the presentation will be heard through your
More informationIntegrated Electricity Demand and Price Forecasting
Integrated Electricity Demand and Price Forecasting Create and Evaluate Forecasting Models The many interrelated factors which influence demand for electricity cannot be directly modeled by closed-form
More informationpeak half-hourly South Australia
Forecasting long-term peak half-hourly electricity demand for South Australia Dr Shu Fan B.S., M.S., Ph.D. Professor Rob J Hyndman B.Sc. (Hons), Ph.D., A.Stat. Business & Economic Forecasting Unit Report
More informationFuzzy Logic Approach for Short Term Electrical Load Forecasting
Fuzzy Logic Approach for Short Term Electrical Load Forecasting M. Rizwan 1, Dinesh Kumar 2, Rajesh Kumar 3 1, 2, 3 Department of Electrical Engineering, Delhi Technological University, Delhi 110042, India
More informationHoliday Demand Forecasting
Holiday Demand Forecasting Yue Li Senior Research Statistician Developer SAS #AnalyticsX Outline Background Holiday demand modeling techniques Weekend day Holiday dummy variable Two-stage methods Results
More informationMODELLING ENERGY DEMAND FORECASTING USING NEURAL NETWORKS WITH UNIVARIATE TIME SERIES
MODELLING ENERGY DEMAND FORECASTING USING NEURAL NETWORKS WITH UNIVARIATE TIME SERIES S. Cankurt 1, M. Yasin 2 1&2 Ishik University Erbil, Iraq 1 s.cankurt@ishik.edu.iq, 2 m.yasin@ishik.edu.iq doi:10.23918/iec2018.26
More informationpeak half-hourly Tasmania
Forecasting long-term peak half-hourly electricity demand for Tasmania Dr Shu Fan B.S., M.S., Ph.D. Professor Rob J Hyndman B.Sc. (Hons), Ph.D., A.Stat. Business & Economic Forecasting Unit Report for
More informationChapter 3. Regression-Based Models for Developing Commercial Demand Characteristics Investigation
Chapter Regression-Based Models for Developing Commercial Demand Characteristics Investigation. Introduction Commercial area is another important area in terms of consume high electric energy in Japan.
More informationDetermine the trend for time series data
Extra Online Questions Determine the trend for time series data Covers AS 90641 (Statistics and Modelling 3.1) Scholarship Statistics and Modelling Chapter 1 Essent ial exam notes Time series 1. The value
More informationApplication of Artificial Neural Networks in Evaluation and Identification of Electrical Loss in Transformers According to the Energy Consumption
Application of Artificial Neural Networks in Evaluation and Identification of Electrical Loss in Transformers According to the Energy Consumption ANDRÉ NUNES DE SOUZA, JOSÉ ALFREDO C. ULSON, IVAN NUNES
More informationRTO Winter Resource Adequacy Assessment Status Report
RTO Winter Resource Adequacy Assessment Status Report RAAS 03/31/2017 Background Analysis performed in response to Winter Season Resource Adequacy and Capacity Requirements problem statement. Per CP rules,
More informationPeak Load Forecasting
Peak Load Forecasting Eugene Feinberg Stony Brook University Advanced Energy 2009 Hauppauge, New York, November 18 Importance of Peak Load Forecasting Annual peak load forecasts are important for planning
More informationFundamentals of Transmission Operations
Fundamentals of Transmission Operations Load Forecasting and Weather PJM State & Member Training Dept. PJM 2014 9/10/2013 Objectives The student will be able to: Identify the relationship between load
More informationPredicting freeway traffic in the Bay Area
Predicting freeway traffic in the Bay Area Jacob Baldwin Email: jtb5np@stanford.edu Chen-Hsuan Sun Email: chsun@stanford.edu Ya-Ting Wang Email: yatingw@stanford.edu Abstract The hourly occupancy rate
More informationFuzzy based Day Ahead Prediction of Electric Load using Mahalanobis Distance
200 International Conference on Power System Technology Fuzzy based Day Ahead Prediction of Electric Load using Mahalanobis Distance Amit Jain, Member, IEEE, E. Srinivas, and Santosh umar Kuadapu Abstract--Prediction
More informationA Hybrid Model of Wavelet and Neural Network for Short Term Load Forecasting
International Journal of Electronic and Electrical Engineering. ISSN 0974-2174, Volume 7, Number 4 (2014), pp. 387-394 International Research Publication House http://www.irphouse.com A Hybrid Model of
More informationThe Dayton Power and Light Company Load Profiling Methodology Revised 7/1/2017
The Dayton Power and Light Company Load Profiling Methodology Revised 7/1/2017 Overview of Methodology Dayton Power and Light (DP&L) load profiles will be used to estimate hourly loads for customers without
More informationA Hybrid Method of CART and Artificial Neural Network for Short-term term Load Forecasting in Power Systems
A Hybrid Method of CART and Artificial Neural Network for Short-term term Load Forecasting in Power Systems Hiroyuki Mori Dept. of Electrical & Electronics Engineering Meiji University Tama-ku, Kawasaki
More informationOne-Hour-Ahead Load Forecasting Using Neural Network
IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 17, NO. 1, FEBRUARY 2002 113 One-Hour-Ahead Load Forecasting Using Neural Network Tomonobu Senjyu, Member, IEEE, Hitoshi Takara, Katsumi Uezato, and Toshihisa Funabashi,
More informationTotal Market Demand Wed Jan 02 Thu Jan 03 Fri Jan 04 Sat Jan 05 Sun Jan 06 Mon Jan 07 Tue Jan 08
MW This report provides a summary of key market data from the IESO-administered markets. It is intended to provide a quick reference for all market stakeholders. It is composed of two sections: Section
More informationWEATHER NORMALIZATION METHODS AND ISSUES. Stuart McMenamin Mark Quan David Simons
WEATHER NORMALIZATION METHODS AND ISSUES Stuart McMenamin Mark Quan David Simons Itron Forecasting Brown Bag September 17, 2013 Please Remember» Phones are Muted: In order to help this session run smoothly,
More informationSYSTEM BRIEF DAILY SUMMARY
SYSTEM BRIEF DAILY SUMMARY * ANNUAL MaxTemp NEL (MWH) Hr Ending Hr Ending LOAD (PEAK HOURS 7:00 AM TO 10:00 PM MON-SAT) ENERGY (MWH) INCREMENTAL COST DAY DATE Civic TOTAL MAXIMUM @Max MINIMUM @Min FACTOR
More informationAdvance signals that can be used to foresee demand response days PJM SODRTF - March 9, 2018
Advance signals that can be used to foresee demand response days PJM SODRTF - March 9, 2018 1 OVERVIEW BGE s load response programs are split between two programs Peak Rewards and Smart Energy Rewards
More informationPredicting Future Energy Consumption CS229 Project Report
Predicting Future Energy Consumption CS229 Project Report Adrien Boiron, Stephane Lo, Antoine Marot Abstract Load forecasting for electric utilities is a crucial step in planning and operations, especially
More informationUSE OF FUZZY LOGIC TO INVESTIGATE WEATHER PARAMETER IMPACT ON ELECTRICAL LOAD BASED ON SHORT TERM FORECASTING
Nigerian Journal of Technology (NIJOTECH) Vol. 35, No. 3, July 2016, pp. 562 567 Copyright Faculty of Engineering, University of Nigeria, Nsukka, Print ISSN: 0331-8443, Electronic ISSN: 2467-8821 www.nijotech.com
More informationNeural-wavelet Methodology for Load Forecasting
Journal of Intelligent and Robotic Systems 31: 149 157, 2001. 2001 Kluwer Academic Publishers. Printed in the Netherlands. 149 Neural-wavelet Methodology for Load Forecasting RONG GAO and LEFTERI H. TSOUKALAS
More informationChapter 8 - Forecasting
Chapter 8 - Forecasting Operations Management by R. Dan Reid & Nada R. Sanders 4th Edition Wiley 2010 Wiley 2010 1 Learning Objectives Identify Principles of Forecasting Explain the steps in the forecasting
More informationINTRODUCTION TO FORECASTING (PART 2) AMAT 167
INTRODUCTION TO FORECASTING (PART 2) AMAT 167 Techniques for Trend EXAMPLE OF TRENDS In our discussion, we will focus on linear trend but here are examples of nonlinear trends: EXAMPLE OF TRENDS If you
More informationSYSTEM BRIEF DAILY SUMMARY
SYSTEM BRIEF DAILY SUMMARY * ANNUAL MaxTemp NEL (MWH) Hr Ending Hr Ending LOAD (PEAK HOURS 7:00 AM TO 10:00 PM MON-SAT) ENERGY (MWH) INCREMENTAL COST DAY DATE Civic TOTAL MAXIMUM @Max MINIMUM @Min FACTOR
More informationThe Research of Urban Rail Transit Sectional Passenger Flow Prediction Method
Journal of Intelligent Learning Systems and Applications, 2013, 5, 227-231 Published Online November 2013 (http://www.scirp.org/journal/jilsa) http://dx.doi.org/10.4236/jilsa.2013.54026 227 The Research
More informationShort-Term Demand Forecasting Methodology for Scheduling and Dispatch
Short-Term Demand Forecasting Methodology for Scheduling and Dispatch V1.0 March 2018 Table of Contents 1 Introduction... 3 2 Historical Jurisdictional Demand Data... 3 3 EMS Demand Forecast... 4 3.1 Manual
More informationOperations Report. Tag B. Short, Director South Region Operations. Entergy Regional State Committee (ERSC) February 14, 2018
Operations Report Tag B. Short, Director South Region Operations Entergy Regional State Committee (ERSC) February 14, 2018 1 Winter Operations Highlights South Region Max Gen Event Regional Dispatch Transfer
More informationDemand Forecasting Models
E 2017 PSE Integrated Resource Plan Demand Forecasting Models This appendix describes the econometric models used in creating the demand forecasts for PSE s 2017 IRP analysis. Contents 1. ELECTRIC BILLED
More informationDefining Normal Weather for Energy and Peak Normalization
Itron White Paper Energy Forecasting Defining Normal Weather for Energy and Peak Normalization J. Stuart McMenamin, Ph.D Managing Director, Itron Forecasting 2008, Itron Inc. All rights reserved. 1 Introduction
More informationShort Term Load Forecasting by Using ESN Neural Network Hamedan Province Case Study
119 International Journal of Smart Electrical Engineering, Vol.5, No.2,Spring 216 ISSN: 2251-9246 pp. 119:123 Short Term Load Forecasting by Using ESN Neural Network Hamedan Province Case Study Milad Sasani
More informationRecurrent Neural Network Based Gating for Natural Gas Load Prediction System
Recurrent Neural Network Based Gating for Natural Gas Load Prediction System Petr Musilek, Member, IEEE, Emil Pelikán, Tomáš Brabec and Milan Šimůnek Abstract Prediction of natural gas consumption is an
More informationWEATHER DEPENENT ELECTRICITY MARKET FORECASTING WITH NEURAL NETWORKS, WAVELET AND DATA MINING TECHNIQUES. Z.Y. Dong X. Li Z. Xu K. L.
WEATHER DEPENENT ELECTRICITY MARKET FORECASTING WITH NEURAL NETWORKS, WAVELET AND DATA MINING TECHNIQUES Abstract Z.Y. Dong X. Li Z. Xu K. L. Teo School of Information Technology and Electrical Engineering
More informationBetter Weather Data Equals Better Results: The Proof is in EE and DR!
Better Weather Data Equals Better Results: The Proof is in EE and DR! www.weatherbughome.com Today s Speakers: Amena Ali SVP and General Manager WeatherBug Home Michael Siemann, PhD Senior Research Scientist
More informationIJESRT. Scientific Journal Impact Factor: (ISRA), Impact Factor: [Gupta et al., 3(5): May, 2014] ISSN:
IJESRT INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY Short Term Weather -Dependent Load Forecasting using Fuzzy Logic Technique Monika Gupta Assistant Professor, Marwadi Education
More informationNEURO-FUZZY CONTROL IN LOAD FORECASTING OF POWER SECTOR
NEURO-FUZZY CONTROL IN LOAD FORECASTING OF POWER SECTOR S.HEMA CHANDRA 1,VENDOTI MOUNIKA 2 & GUNTURU VANDANA 3 1,2,3 Department of Electronics and Control Engineering, Sree Vidyanikethan Engineering College
More information2018 Annual Review of Availability Assessment Hours
2018 Annual Review of Availability Assessment Hours Amber Motley Manager, Short Term Forecasting Clyde Loutan Principal, Renewable Energy Integration Karl Meeusen Senior Advisor, Infrastructure & Regulatory
More informationA Fuzzy Logic Based Short Term Load Forecast for the Holidays
A Fuzzy Logic Based Short Term Load Forecast for the Holidays Hasan H. Çevik and Mehmet Çunkaş Abstract Electric load forecasting is important for economic operation and planning. Holiday load consumptions
More informationCOMPARISON OF PEAK FORECASTING METHODS. Stuart McMenamin David Simons
COMPARISON OF PEAK FORECASTING METHODS Stuart McMenamin David Simons Itron Forecasting Brown Bag March 24, 2015 PLEASE REMEMBER» Phones are Muted: In order to help this session run smoothly, your phones
More informationAbout Nnergix +2, More than 2,5 GW forecasted. Forecasting in 5 countries. 4 predictive technologies. More than power facilities
About Nnergix +2,5 5 4 +20.000 More than 2,5 GW forecasted Forecasting in 5 countries 4 predictive technologies More than 20.000 power facilities Nnergix s Timeline 2012 First Solar Photovoltaic energy
More informationApplication of Artificial Neural Network for Short Term Load Forecasting
aerd Scientific Journal of Impact Factor(SJIF): 3.134 e-issn(o): 2348-4470 p-issn(p): 2348-6406 International Journal of Advance Engineering and Research Development Volume 2,Issue 4, April -2015 Application
More informationImplementation of Artificial Neural Network for Short Term Load Forecasting
Implementation of Artificial Neural Network for Short Term Load Forecasting Anju Bala * * Research Scholar, E.E. Department, D.C.R. University of Sci. & Technology, Murthal, anju8305@gmail.com N. K. Yadav
More informationShort-Term Load Forecasting Using ARIMA Model For Karnataka State Electrical Load
International Journal of Engineering Research and Development e-issn: 2278-67X, p-issn: 2278-8X, www.ijerd.com Volume 13, Issue 7 (July 217), PP.75-79 Short-Term Load Forecasting Using ARIMA Model For
More informationQUANTIFICATION OF THE NATURAL VARIATION IN TRAFFIC FLOW ON SELECTED NATIONAL ROADS IN SOUTH AFRICA
QUANTIFICATION OF THE NATURAL VARIATION IN TRAFFIC FLOW ON SELECTED NATIONAL ROADS IN SOUTH AFRICA F DE JONGH and M BRUWER* AECOM, Waterside Place, Tygerwaterfront, Carl Cronje Drive, Cape Town, South
More informationWill it rain tomorrow?
Will it rain tomorrow? Bilal Ahmed - 561539 Department of Computing and Information Systems, The University of Melbourne, Victoria, Australia bahmad@student.unimelb.edu.au Abstract With the availability
More informationReport on System-Level Estimation of Demand Response Program Impact
Report on System-Level Estimation of Demand Response Program Impact System & Resource Planning Department New York Independent System Operator April 2012 1 2 Introduction This report provides the details
More informationSTATISTICAL LOAD MODELING
STATISTICAL LOAD MODELING Eugene A. Feinberg, Dora Genethliou Department of Applied Mathematics and Statistics State University of New York at Stony Brook Stony Brook, NY 11794-3600, USA Janos T. Hajagos
More information2013 Weather Normalization Survey. Itron, Inc El Camino Real San Diego, CA
Itron, Inc. 11236 El Camino Real San Diego, CA 92130 2650 858 724 2620 March 2014 Weather normalization is the process of reconstructing historical energy consumption assuming that normal weather occurred
More informationWhen One Size No Longer Fits All Electric Load Forecasting with a Geographic Hierarchy WHITE PAPER
When One Size No Longer Fits All Electric Load Forecasting with a Geographic Hierarchy WHITE PAPER SAS White Paper Table of Contents Executive Summary.... 1 Electric Load Forecasting Challenges... 1 Selected
More informationCropCast Corn and Soybean Report Kenny Miller Friday, March 17, 2017
Accumulated Rainfall (inches) Accumulated GDDs Temperature ( F)/Wind Speed (mph) Precipitation (inches) CropCast Corn and Soybean Report Kenny Miller Friday, March 17, 2017 Peoria, IL Regional Forecast
More informationForecasting of Electric Consumption in a Semiconductor Plant using Time Series Methods
Forecasting of Electric Consumption in a Semiconductor Plant using Time Series Methods Prayad B. 1* Somsak S. 2 Spansion Thailand Limited 229 Moo 4, Changwattana Road, Pakkred, Nonthaburi 11120 Nonthaburi,
More informationMISO September 15 Maximum Generation Event Overview. October 11, 2018
MISO September 15 Maximum Generation Event Overview October 11, 2018 Purpose & Key Takeaways Purpose: Summarize operations during the September 15 South Region Maximum Generation Event Key Takeaways: MISO
More informationYEAR 10 GENERAL MATHEMATICS 2017 STRAND: BIVARIATE DATA PART II CHAPTER 12 RESIDUAL ANALYSIS, LINEARITY AND TIME SERIES
YEAR 10 GENERAL MATHEMATICS 2017 STRAND: BIVARIATE DATA PART II CHAPTER 12 RESIDUAL ANALYSIS, LINEARITY AND TIME SERIES This topic includes: Transformation of data to linearity to establish relationships
More informationAn Approach of Correlation Inter-variable Modeling with Limited Data for Inter-Bus Transformer Weather Sensitive Loading Prediction
International Journal on Electrical Engineering and Informatics Volume 4, Number 4, December 0 An Approach of Correlation Inter-variable Modeling with Limited Data for Inter-Bus Transformer Weather Sensitive
More informationApplication of Dynamic Matrix Control To a Boiler-Turbine System
Application of Dynamic Matrix Control To a Boiler-Turbine System Woo-oon Kim, Un-Chul Moon, Seung-Chul Lee and Kwang.. Lee, Fellow, IEEE Abstract--This paper presents an application of Dynamic Matrix Control
More informationDirector Corporate Services & Board Secretary
March, The Board of Commissioners of Public Utilities Prince Charles Building Torbay Road, P.O. Box 0 St. John s, NL AA B Attention: Ms. Cheryl Blundon Director Corporate Services & Board Secretary Dear
More informationProject Appraisal Guidelines
Project Appraisal Guidelines Unit 16.2 Expansion Factors for Short Period Traffic Counts August 2012 Project Appraisal Guidelines Unit 16.2 Expansion Factors for Short Period Traffic Counts Version Date
More informationUse of Weather Inputs in Traffic Volume Forecasting
ISSC 7, Derry, Sept 13 14 Use of Weather Inputs in Traffic Volume Forecasting Shane Butler, John Ringwood and Damien Fay Department of Electronic Engineering NUI Maynooth, Maynooth Co. Kildare, Ireland
More informationShort-term Forecasting of Anomalous Load Using Rule-based Triple Seasonal Methods
1 Short-term Forecasting of Anomalous Load Using Rule-based Triple Seasonal Methods Siddharth Arora, James W. Taylor IEEE Transactions on Power Systems, forthcoming. Abstract Numerous methods have been
More informationA Unified Framework for Defining and Measuring Flexibility in Power System
J A N 1 1, 2 0 1 6, A Unified Framework for Defining and Measuring Flexibility in Power System Optimization and Equilibrium in Energy Economics Workshop Jinye Zhao, Tongxin Zheng, Eugene Litvinov Outline
More informationNormalization of Peak Demand for an Electric Utility using PROC MODEL
Normalization of Peak Demand for an Electric Utility using PROC MODEL Mark Harris, Jeff Brown, and Mark Gilbert* Central and South West Services, Inc. Tulsa, Oklahoma Abstract This paper discusses the
More informationMountain View Community Shuttle Monthly Operations Report
Mountain View Community Shuttle Monthly Operations Report December 6, 2018 Contents Passengers per Day, Table...- 3 - Passengers per Day, Chart...- 3 - Ridership Year-To-Date...- 4 - Average Daily Ridership
More informationSegmenting electrical load time series for forecasting? An empirical evaluation of daily UK load patterns
Segmenting electrical load time series for forecasting? An empirical evaluation of daily UK load patterns Sven F. Crone and Nikolaos Kourentzes Abstract Forecasting future electricity load represents one
More informationIntroduction to Forecasting
Introduction to Forecasting Introduction to Forecasting Predicting the future Not an exact science but instead consists of a set of statistical tools and techniques that are supported by human judgment
More informationDELFOS: GAS DEMAND FORECASTING. Author and speaker: JOSÉ MANUEL GÁLVEZ CAÑAMAQUE
DELFOS: GAS DEMAND FORECASTING Author and speaker: JOSÉ MANUEL GÁLVEZ CAÑAMAQUE PROJECT : DELFOS DURATION: OCTOBER 1998 - APRIL 2000 CUSTOMER COMPANY: ENAGAS (GAS NATURAL) OBJECTIVE : GAS DEMAND FORECASTING
More informationDr SN Singh, Professor Department of Electrical Engineering. Indian Institute of Technology Kanpur
Short Term Load dforecasting Dr SN Singh, Professor Department of Electrical Engineering Indian Institute of Technology Kanpur Email: snsingh@iitk.ac.in Basic Definition of Forecasting Forecasting is a
More informationParking Study MAIN ST
Parking Study This parking study was initiated to help understand parking supply and parking demand within Oneida City Center. The parking study was performed and analyzed by the Madison County Planning
More informationDemand Forecasting in Deregulated Electricity Markets
International Journal of Computer Applications (975 8887) Demand Forecasting in Deregulated Electricity Marets Anamia Electrical & Electronics Engineering Department National Institute of Technology Jamshedpur
More informationURD Cable Fault Prediction Model
1 URD Cable Fault Prediction Model Christopher Gubala ComEd General Engineer Reliability Analysis 2014 IEEE PES General Meeting Utility Current Practices & Challenges of Predictive Distribution Reliability
More informationCost of Inflow Forecast Uncertainty for Day Ahead Hydropower Production Scheduling
Cost of Inflow Forecast Uncertainty for Day Ahead Hydropower Production Scheduling HEPEX 10 th University Workshop June 25 th, 2014 NOAA Center for Weather and Climate Thomas D. Veselka and Les Poch Argonne
More informationAN INTERNATIONAL SOLAR IRRADIANCE DATA INGEST SYSTEM FOR FORECASTING SOLAR POWER AND AGRICULTURAL CROP YIELDS
AN INTERNATIONAL SOLAR IRRADIANCE DATA INGEST SYSTEM FOR FORECASTING SOLAR POWER AND AGRICULTURAL CROP YIELDS James Hall JHTech PO Box 877 Divide, CO 80814 Email: jameshall@jhtech.com Jeffrey Hall JHTech
More informationImplementation of GCPSO for Multi-objective VAr Planning with SVC and Its Comparison with GA and PSO
Implementation of GCPSO for Multi-obective VAr Planning with SVC and Its Comparison with GA and PSO Malihe M. Farsang Hossein Nezamabadi-pour and Kwang Y. Lee, Fellow, IEEE Abstract In this paper, Guaranteed
More informationThe Vaisala AUTOSONDE AS41 OPERATIONAL EFFICIENCY AND RELIABILITY TO A TOTALLY NEW LEVEL.
The Vaisala AUTOSONDE AS41 OPERATIONAL EFFICIENCY AND RELIABILITY TO A TOTALLY NEW LEVEL. Weather Data Benefit For Society The four most important things about weather prediction are quality, reliability,
More informationCropCast Corn and Soybean Report Kenny Miller Tuesday, March 14, 2017
Accumulated Rainfall (inches) Accumulated GDDs Temperature ( F)/Wind Speed (mph) Precipitation (inches) CropCast Corn and Soybean Report Kenny Miller Tuesday, March 14, 2017 Peoria, IL Regional Forecast
More informationImplementing an Intelligent Error Back Propagation (EBP) Relay in PSCAD TM /EMTDC 4.2.1
1 Implementing an Intelligent Error Back Propagation (EBP) Relay in PSCAD TM /EMTDC 4.2.1 E. William, IEEE Student Member, Brian K Johnson, IEEE Senior Member, M. Manic, IEEE Senior Member Abstract Power
More informationLOAD POCKET MODELING. KEY WORDS Load Pocket Modeling, Load Forecasting
LOAD POCKET MODELING Eugene A. Feinberg, Dora Genethliou Department of Applied Mathematics and Statistics State University of New York at Stony Brook Stony Brook, NY 794-36, USA Janos T. Hajagos KeySpan
More informationLOADS, CUSTOMERS AND REVENUE
EB-00-0 Exhibit K Tab Schedule Page of 0 0 LOADS, CUSTOMERS AND REVENUE The purpose of this evidence is to present the Company s load, customer and distribution revenue forecast for the test year. The
More informationDesign of Adaptive Filtering Algorithm for Relative Navigation
Design of Adaptive Filtering Algorithm for Relative Navigation Je Young Lee, Hee Sung Kim, Kwang Ho Choi, Joonhoo Lim, Sung Jin Kang, Sebum Chun, and Hyung Keun Lee Abstract Recently, relative navigation
More informationA FORECASTING METHOD BASED ON COMBINING AUTOMATIC CLUSTERING TECHNIQUE AND FUZZY RELATIONSHIP GROUPS
A FORECASTING METHOD BASED ON COMBINING AUTOMATIC CLUSTERING TECHNIQUE AND FUZZY RELATIONSHIP GROUPS Nghiem Van Tinh Thai Nguyen University of Technology, Thai Nguyen University Thai Nguyen, Vietnam ---------------------------------------------------------------------***---------------------------------------------------------------------
More informationChapter 7 Forecasting Demand
Chapter 7 Forecasting Demand Aims of the Chapter After reading this chapter you should be able to do the following: discuss the role of forecasting in inventory management; review different approaches
More informationOntario Demand Forecast
Ontario Demand Forecast DECEMBER 12, 2017 December 12, 2017 Public Page i Executive Summary The IESO is responsible for forecasting electricity demand in Ontario and for assessing whether transmission
More informationMean, Median, Mode, and Range
Mean, Median, Mode, and Range Mean, median, and mode are measures of central tendency; they measure the center of data. Range is a measure of dispersion; it measures the spread of data. The mean of a data
More informationIncreasing Transmission Capacities with Dynamic Monitoring Systems
INL/MIS-11-22167 Increasing Transmission Capacities with Dynamic Monitoring Systems Kurt S. Myers Jake P. Gentle www.inl.gov March 22, 2012 Concurrent Cooling Background Project supported with funding
More information