Electrical Load Forecasting - Modeling and Model Construction

Succinct and understandable, this book provides practitioners and students with a step-by-step guide to the mathematics and construction of electrical load forecasting models. Written by one of the world's foremost experts on the subject, this book provides a brief discussion of algorithms, the...

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Main Author Soliman, S. A
Format eBook
LanguageEnglish
Published Chantilly Elsevier 2010
Elsevier Science & Technology
Butterworth-Heinemann
Edition1
Subjects
Electric power consumption
Electric power systems
Electric power-plants
Electrical & Power Engineering
Electrical Design & Energy Efficiency
Forecasting
Load
Mathematics
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Abstract Succinct and understandable, this book provides practitioners and students with a step-by-step guide to the mathematics and construction of electrical load forecasting models. Written by one of the world's foremost experts on the subject, this book provides a brief discussion of algorithms, their advantages and disadvantages and when they are best utilized. This is followed by a clear and rigorous exposition of the statistical techniques and algorithms such as regression, neural networks, fuzzy logic, and expert systems. In this book, readers find reliable and easy-to-use techniques designed to improve their forecasting techniques and construct more accurate models. The book provides a good description of the basic theory and models needed to truly understand how the models are prepared so that they are not just blindly plugging and chugging numbers.
AbstractList Succinct and understandable, this book is a step-by-step guide to the mathematics and construction of electrical load forecasting models. Written by one of the world's foremost experts on the subject, Electrical Load Forecasting provides a brief discussion of algorithms, their advantages and disadvantages and when they are best utilized. The book begins with a good description of the basic theory and models needed to truly understand how the models are prepared so that they are not just blindly plugging and chugging numbers. This is followed by a clear and rigorous exposition of the statistical techniques and algorithms such as regression, neural networks, fuzzy logic, and expert systems. The book is also supported by an online computer program that allows readers to construct, validate, and run short and long term models. Step-by-step guide to model constructionConstruct, verify, and run short and long term modelsAccurately evaluate load shape and pricingCreat regional specific electrical load models
Succinct and understandable, this book provides practitioners and students with a step-by-step guide to the mathematics and construction of electrical load forecasting models. Written by one of the world's foremost experts on the subject, this book provides a brief discussion of algorithms, their advantages and disadvantages and when they are best utilized. This is followed by a clear and rigorous exposition of the statistical techniques and algorithms such as regression, neural networks, fuzzy logic, and expert systems. In this book, readers find reliable and easy-to-use techniques designed to improve their forecasting techniques and construct more accurate models. The book provides a good description of the basic theory and models needed to truly understand how the models are prepared so that they are not just blindly plugging and chugging numbers.
Author Al-Kandari Ahmad M
Soliman Soliman Abdel-Hady
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Snippet Succinct and understandable, this book provides practitioners and students with a step-by-step guide to the mathematics and construction of electrical load...
Succinct and understandable, this book is a step-by-step guide to the mathematics and construction of electrical load forecasting models. Written by one of the...
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Publisher
SubjectTerms Electric power consumption
Electric power systems
Electric power-plants
Electrical & Power Engineering
Electrical Design & Energy Efficiency
Forecasting
Load
Mathematics
TableOfContents Title Page Introduction Table of Contents 1. Mathematical Background and State of the Art 2. Static State Estimation 3. Load Modeling for Short-Term Forecasting 4. Fuzzy Regression Systems and Fuzzy Linear Models 5. Dynamic State Estimation 6. Load-Forecasting Results Using Static State Estimation 7. Load-Forecasting Results Using Fuzzy Systems 8. Dynamic Electric Load Forecasting 9. Electric Load Modeling for Long-Term Forecasting Index
8.6.2 Estimating 1995 Year Load Contour -- 8.6.3 Annual Load Growth Results -- 8.6.4 Remarks -- 8.7 Kalman Filtering Algorithm with Moving Window Weather -- 8.7.1 Load-Forecasting Model -- 8.7.2 Winter Model -- 8.8 Kalman Filter Parameter Estimation -- 8.8.1 Basic Kalman Filter -- 8.8.2 Prediction of the Kalman Filter Model -- 8.8.3 Examples and Results -- 8.8.4 Order of the Load Model -- 8.8.5 One-Hour Prediction -- 8.8.6 Twenty-Four-Hour Prediction -- 8.8.7 Weekdays and Weekends Profiles -- 8.8.8 Conclusions -- 8.9 Fuzzy Load Forecasting Using the Kalman Filter -- 8.9.1 Fuzzy Linear Model -- 8.9.2 Fuzzy Parameter Estimation Using Kalman Filtering -- 8.9.3 Kalman Filter Prediction Model -- 8.9.4 Fuzzy Rule-Based Inference -- 8.10 Model Validation and Results -- 8.10.1 One-Day Parameter Estimation and Load Prediction -- 8.10.2 Up to 60 Days of Load Prediction -- 8.10.3 Conclusions -- 8.11 Recursive Least Error Squares -- 8.11.1 Testing the Algorithm -- 8.11.2 Conclusions -- References -- Chapter 9. Electric Load Modeling for Long-Term Forecasting -- 9.1 Introduction -- 9.2 Peak-Load-Demand Model -- 9.2.1 Example -- 9.2.2 A More Detailed Model -- 9.2.3 A Time-Dependent Model -- 9.3 Time-Series Analysis -- 9.3.1 Example for the Time-Series Model -- 9.3.2 Remarks -- 9.4 Kalman Filtering Algorithm -- 9.4.1 Estimating Multiple Regression Models -- 9.4.2 Estimating the Next Year's Load Contour -- 9.5 Annual Load Growth -- 9.5.1 Load Modeling for the Kalman Filtering Algorithm -- 9.5.2 Kalman Filter Parameter Estimation Algorithm -- 9.6 Computer Exercises -- 9.6.1 Multiple Regression Models Results -- 9.6.2 Estimating the 1995 Load Contour -- 9.6.3 Kalman Filter Prediction Results -- 9.6.4 Remarks -- 9.7 Long-Term/Mid term Forecasting (Short-Term Correlation and Annual Growth) -- 9.7.1 Load Regression Models -- 9.7.2 Estimating the Next Year's Load Contour
6.4.2 Estimation of Constant Model Parameters for Weekday (One Set) -- 6.4.3 Model Parameter Estimation for Every Hour in a Summer Weekend Day (24 Sets) -- 6.4.4 Estimation of Constant Model Parameters for a Summer Weekend Day (One Set) -- 6.4.5 General Remarks for Summer Model A -- 6.4.6 Winter Predictions -- 6.5 Model B -- 6.5.1 Summer Weekday -- 6.5.2 Summer Weekend Day -- 6.5.3 General Remarks for Summer Model B -- 6.5.4 Winter Predictions -- 6.6 Model C Results -- 6.6.1 General Remarks for Summer Model C -- 6.6.2 Winter Predictions -- 6.7 Conclusion -- Appendix 6.1 Winter Static Load Results for Model A -- Appendix 6.2 Winter Static Load Results for Model B -- Appendix 6.3 Winter Static Load Results for Model C -- Chapter 7. Load-Forecasting Results Using Fuzzy Systems -- 7.1 Objectives -- 7.2 Fuzzy Load Model A -- 7.2.1 Load Parameters for a Summer Weekday -- 7.2.2 Load Estimation for a Summer Weekday -- 7.2.3 Load Prediction for a Summer Weekday -- 7.2.4 Load Parameters for a Summer Weekend Day -- 7.2.5 Load Prediction for a Summer Weekend Day -- 7.2.6 Load Estimation and Prediction for a Winter Weekday and a Winter Weekend Day -- 7.3 Fuzzy Load Model B -- 7.3.1 Load Parameters for Model B -- 7.3.2 Load Estimation and Prediction -- 7.4 Fuzzy Load Model C -- 7.4.1 Load Parameters for Model C -- 7.4.2 Load Estimation and Prediction for a Summer Day -- 7.4.3 Load Estimation and Prediction for a Winter Day -- 7.5 Conclusion -- Appendix 7.1 Winter Load Forecasting: Fuzzy Case Model A -- Appendix 7.2 Winter Load Forecasting: Fuzzy Case Model B -- Appendix 7.3 Winter Load Forecasting: Fuzzy Case Model C -- Chapter 8. Dynamic Electric Load Forecasting -- 8.1 Objectives -- 8.2 Introduction -- 8.3 Load Regression Models -- 8.4 Estimating the Next Year's Load Contour -- 8.5 Annual Load Growth -- 8.6 Examples -- 8.6.1 Multiple Regression Models Results
9.7.3 Annual Load Growth -- 9.8 Examples of Long-Term/Mid Term Forecasting -- 9.8.1 Multiple Regression Model Results -- 9.8.2 Estimating the 1995 Load Contour -- 9.8.3 Annual Load Growth Results -- 9.8.4 Remarks -- 9.9 Fuzzy Regression for Peak-Load Forecasting -- 9.9.1 Modeling of Electric Annual Peak Load -- 9.9.2 A Nonfuzzy Peak Load with Fuzzy Parameters -- 9.9.3 A Fuzzy Peak-Load Demand -- 9.10 Testing the Algorithm -- 9.10.1 Nonfuzzy Annual Peak Load -- 9.10.2 Fuzzy Annual Peak Load -- 9.10.3 Remarks -- 9.11 Time-Series Models -- 9.11.1 Time Series -- 9.11.2 Forecasting Methods -- 9.11.3 Forecasting Errors -- 9.12 Power System Load Forecasting -- 9.12.1 A Simple Example of Power System Load Forecasting -- 9.13 Linear Regression Method -- 9.14 Autoregressive (AR) Model -- 9.15 Moving Average (MA) Model -- 9.16 Autoregressive Moving Average (ARMA, or Box-Jenkins) Model -- 9.17 Autoregressive Integrated Moving Average (ARIMA) Model -- 9.18 ARMAX and ARIMAX Models -- 9.18.1 Remarks -- References -- Index
Front Cover -- Half Title Page -- Title Page -- Copyright -- Dedication -- Table of Contents -- Acknowledgments -- Introduction -- Outline of the Book -- Chapter 1. Mathematical Background and State of the Art -- 1.1 Objectives -- 1.2 Matrices and Vectors -- 1.3 Matrix Algebra -- 1.3.1 Addition of Matrices -- 1.3.2 Matrix Subtraction (Difference) -- 1.3.3 Matrix Multiplication -- 1.3.4 Inverse of a Matrix (Matrix Division) -- 1.4 Rank of a Matrix -- 1.5 Singular Matrix -- 1.6 Characteristic Vectors of a Matrix -- 1.7 Diagonalization -- 1.8 Partitioned Matrices -- 1.9 Partitioned Matrix Inversion -- 1.10 Quadratic Forms -- 1.11 State Space Representation -- 1.12 Difference Equations -- 1.13 Some Optimization Techniques -- 1.13.1 Unconstrained Optimization -- 1.13.2 Constrained Optimization -- 1.14 State of the Art -- References -- Chapter 2. Static State Estimation -- 2.1 Objectives -- 2.2 The Static Estimation Problem Formulation -- 2.2.1 Linear Least Error Squares Estimation -- 2.2.2 Weighted Linear Least Error Squares (WLES) Estimation -- 2.2.3 Constrained Least Error Squares (CLES) Estimation -- 2.2.4 Recursive Least Error Squares (RLES) Estimation -- 2.2.5 Nonlinear Least Error Squares (NLLES) Estimation -- 2.3 Properties of Least Error Squares Estimation -- 2.4 Least Absolute Value Static State Estimation -- 2.4.1 Historical Perspective -- 2.4.2 Least Absolute Value of Error Estimation -- 2.4.3 Least Absolute Value Based on Linear Programming -- 2.4.4 Schlossmacher Iterative Algorithm -- 2.4.5 Sposito and Hand Algorithm -- 2.4.6 Soliman and Christensen Algorithm -- 2.5 Constrained LAV Estimation -- 2.6 Nonlinear Estimation Using the Soliman and Christensen Algorithm -- 2.7 Leverage Points -- 2.8 Comparison between LES Estimation and LAV Estimation Algorithms -- References -- Chapter 3. Load Modeling for Short-Term Forecasting
3.1 Objectives -- 3.2 Introduction -- 3.3 Base Load -- 3.4 Weather-Dependent Load -- 3.4.1 Temperature -- 3.4.2 Wind Speed -- 3.4.3 Humidity -- 3.4.4 Illumination -- 3.5 Residual Load -- 3.6 Short-Term Load Models -- 3.6.1 Multiple Linear Regression -- 3.6.2 General Exponential Smoothing -- 3.6.3 Stochastic Time Series -- 3.6.4 Qualities of Forecasting Models -- 3.7 Special Load-Forecasting Models -- 3.7.1 Model A: Multiple Linear Regression Model -- 3.7.2 Model B: Harmonics Model -- 3.7.3 Model C: Hybrid Model -- References -- Chapter 4. Fuzzy Regression Systems and Fuzzy Linear Models -- 4.1 Objectives -- 4.2 Fuzzy Fundamentals -- 4.3 Fuzzy Sets and Membership -- 4.3.1 Membership Functions -- 4.3.2 Basic Terminology and Definitions -- 4.3.3 Support of a Fuzzy Set -- 4.3.4 Normality -- 4.3.5 Convexity and Concavity -- 4.3.6 Basic Operation -- 4.4 Fuzzy Linear Estimation -- 4.4.1 Nonfuzzy Output (Yj = mj) -- 4.4.2 Fuzzy Output Systems -- 4.5 Fuzzy Short-Term Load Modeling -- 4.5.1 Multiple Fuzzy Linear Regression Model: Crisp Data -- 4.5.2 Multiple Fuzzy Linear Regression Model: Fuzzy Data -- 4.5.3 Fuzzy Load Model B -- 4.5.4 Fuzzy Load Model C -- 4.6 Conclusion -- References -- Chapter 5. Dynamic State Estimation -- 5.1 Objectives -- 5.2 Discrete Time Systems -- 5.3 Discrete Time-Optimal Filtering -- 5.3.1 Kalman Filter -- 5.3.2 Initialization of the Kalman Filter -- 5.3.3 Divergence Problems in Kalman Filter -- 5.3.4 Soliman and Christensen Filter: Weighted Least Absolute Value Filter (WLAVF) -- 5.4 Recursive Least Error Squares -- References -- Chapter 6. Load-Forecasting Results Using Static State Estimation -- 6.1 Objectives -- 6.2 Description of the Data -- 6.3 Offline Simulation (Static Load Forecasting Estimation) -- 6.4 Model A Results -- 6.4.1 Model Parameters Estimation for Every Hour in a Summer Weekday (24 Sets)
Title Electrical Load Forecasting - Modeling and Model Construction
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