1 GAs: What Are They?.- 2 GAs: How Do They Work?.- 3 GAs: Why Do They Work?.- 4 GAs: Selected Topics.- 5 Binary or Float?.- 6 Fine Local Tuning.- 7 Handling Constraints.- 8 Evolution Strategies and Other Methods.- 9 The Transportation Problem.- 10 The Traveling Salesman Problem.- 11 Evolution Programs for Various Discrete Problems.- 12 Machine Learning.- 13 Evolutionary Programming and Genetic Programming.- 14 A Hierarchy of Evolution Programs.- 15 Evolution Programs and Heuristics.- 16 Conclusions.- Appendix A.- Appendix B.- Appendix C.- Appendix D.- References.

Genetic Algorithms + Data Structures = Evolution Programs

Introduction to linear and nonlinear programming

Load forecasting has become one of the major areas of research in electrical engineering, and most traditional forecasting models and artificial intelligence techniques have been tried out in this task. Artificial neural networks (NNs) have lately received much attention, and a great number of papers have reported successful experiments and practical tests with them. Nevertheless, some authors remain skeptical, and believe that the advantages of using NNs in forecasting have not been systematically proved yet. In order to investigate the reasons for such skepticism, this review examines a collection of papers (published between 1991 and 1999) that report the application of NNs to short-term load forecasting. Our aim is to help to clarify the issue, by critically evaluating the ways in which the NNs proposed in these papers were designed and tested.

Neural networks for short-term load forecasting: a review and evaluation

Stochastic Optimal Control

Accurate forecasting of electricity load has been one of the most important issues in the electricity industry. Recently, along with power system privatization and deregulation, accurate forecast of electricity load has received increasing attention. Because of the general nonlinear mapping capabilities of forecasting, artificial neural networks have played a crucial role in forecasting electricity load. Support vector machines (SVMs) have been successfully employed to solve nonlinear regression and time series problems. However, SVMs have rarely been applied to forecast electricity load. This investigation elucidates the feasibility of using SVMs to forecast electricity load. Moreover, simulated annealing (SA) algorithms were employed to choose the parameters of a SVM model. Subsequently, examples of electricity load data from Taiwan were used to illustrate the proposed SVMSA (support vector machines with simulated annealing) model. The empirical results reveal that the proposed model outperforms the other two models, namely the autoregressive integrated moving average (ARIMA) model and the general regression neural networks (GRNN) model. Consequently, the SVMSA model provides a promising alternative for forecasting electricity load.

Support Vector Machines with Simulated Annealing Algorithms in Electricity Load Forecasting

Application of least absolute value parameter estimation based on linear programming to short-term load forecasting

1. Introduction.- 2. Some Optimal Control Techniques.- 3. Dynamic Optimal Load Flow.- 4. Economic Coordination of Hydrothermal-Nuclear Systems.- 5. Optimal Tie-Line Control.- 6. Optimal Control of Turboalternators.- 7. Conclusion.

Optimal Control Applications in Electric Power Systems

A new technique for curve fitting based on minimum absolute deviations

Functional analysis and minimum norm formulation are employed for solving the optimal long-term problem of a hydro power system for maximum total expected benefits (benefits from energy generated by a hydro power system over the planning period plus the expected future returns from water left in storage at the end of that period). The proposed method takes into account the stochasticity of the river inflows; we assume that their probability properties are preestimated from past history. The adequate fulfillment of other system functions such as flood control and water supply is guaranteed via constraints on storage and release variables. Numerical results are presented for a real system in operation consisting of three rivers; each river has two series reservoirs.

S. A. Soliman

Papers

Application of least absolute value parameter estimation based on linear programming to short-term load forecasting

Optimal Control Applications in Electric Power Systems

A new technique for curve fitting based on minimum absolute deviations

Application of Functional Analysis to Optimization of a Variable Head Multireservoir Power System for Long‐Term Regulation

Optimal filtering of linear discrete dynamic systems based on least absolute value approximations