Vassilios Petridis

A genetic algorithm solution to the unit commitment problem

This paper presents a genetic algorithm (GA) solution to the unit commitment problem. GAs are general purpose optimization techniques based on principles inspired from the biological evolution using metaphors of mechanisms such as natural selection, genetic recombination and survival of the fittest. A simple GA algorithm implementation using the standard crossover and mutation operators could locate near optimal solutions but in most cases failed to converge to the optimal solution. However, using the varying quality function technique and adding problem specific operators, satisfactory solutions to the unit commitment problem were obtained. Test results for power systems of up to 100 units and comparisons with results obtained using Lagrangian relaxation and dynamic programming are also reported.

A neural network short term load forecasting model for the Greek power system

This paper presents the development of an artificial neural network (ANN) based short-term load forecasting model for the Energy Control Center of the Greek Public Power Corporation (PPC). The model can forecast daily load profiles with a lead time of one to seven days. Attention was paid for the accurate modeling of holidays. Experiences gained during the development of the model regarding the selection of the input variables, the ANN structure, and the training data set are described in the paper. The results indicate that the load forecasting model developed provides accurate forecasts.

Fuzzy lattice neural network (FLNN): a hybrid model for learning

This paper proposes two hierarchical schemes for learning, one for clustering and the other for classification problems. Both schemes can be implemented on a fuzzy lattice neural network (FLNN) architecture, to be introduced herein. The corresponding two learning models draw on adaptive resonance theory (ART) and min-max neurocomputing principles but their application domain is a mathematical lattice. Therefore they can handle more general types of data in addition to N-dimensional vectors. The FLNN neural model stems from a cross-fertilization of lattice theory and fuzzy set theory. Hence a novel theoretical foundation is introduced in this paper, that is the framework of fuzzy lattices or FL-framework, based on the concepts fuzzy lattice and inclusion measure. Sufficient conditions for the existence of an inclusion measure in a mathematical lattice are shown. The performance of the two FLNN schemes, that is for clustering and for classification, compares quite well with other methods and it is demonstrated by examples on various data sets including several benchmark data sets.

Short-term load forecasting using neural networks

Abstract An artificial neural network (ANN) model for short-term load forecasting (STLF) is presented. The proposed model is capable of forecasting the next 24-hour load profile at one time, as opposed to the usual ‘next one hour’ ANN models. The inputs to the ANN are load profiles of the two previous days and daily maximum and minimum temperature forecasts. The network is trained to learn the next days load profile. Testing of the model with one year of data from the Greek interconnected power system resulted in a 2.66% average absolute forecast error.

A Comparison of Word- and Sense-Based Text Categorization Using Several Classification Algorithms

Most of the text categorization algorithms in the literature represent documents as collections of words. An alternative which has not been sufficiently explored is the use of word meanings, also known as senses. In this paper, using several algorithms, we compare the categorization accuracy of classifiers based on words to that of classifiers based on senses. The document collection on which this comparison takes place is a subset of the annotated Brown Corpus semantic concordance. A series of experiments indicates that the use of senses does not result in any significant categorization improvement.

Varying fitness functions in genetic algorithm constrained optimization: the cutting stock and unit commitment problems

We present a specific varying fitness function technique in genetic algorithm (GA) constrained optimization. This technique incorporates the problems constraints into the fitness function in a dynamic way. It consists of forming a fitness function with varying penalty terms. The resulting varying fitness function facilitates the GA search. The performance of the technique is tested on two optimization problems: the cutting stock, and the unit commitment problems. Also, new domain-specific operators are introduced. Solutions obtained by means of the varying and the conventional (nonvarying) fitness function techniques are compared. The results show the superiority of the proposed technique.

Varying Fitness Functions in Genetic Algorithms: Studying the Rate of Increase of the Dynamic Penalty Terms

In this paper we present a promising technique that enhances the efficiency of GAs, when they are applied to constrained optimisation problems. According to this technique, the problem constraints are included in the fitness function as penalty terms, that vary during the GA evolution, facilitating thus the location of the global optimum and the avoidance of local optima. Moreover we proceed to test the effect that the rate of change in the fitness function has on GA performance. The tests are performed on two well-known real-world optimisation problems: the Cutting Stock problem and the Unit Commitment problem. Comparative results are reported.

Predictive modular neural networks for time series classification

A predictive modular neural network (PREMONN) architecture for time series classification is presented. The PREMONN has a hierarchical structure. The bottom level consists of a bank of linear or nonlinear predictor modules. The top level is a decision module which employs Bayesian or nonprobabilistic decision rules. For various choices of prediction and decision modules, convergence to correct classification is proven. Also it is shown that PREMONN is robust to noise and the speed/accuracy tradeoff is investigated. The analysis is mainly mathematical; however, we also present classification experiments to corroborate our conclusions. Copyright 1996 Elsevier Science Ltd.

SVM-Based Fuzzy Decision Trees for Classification of High Spatial Resolution Remote Sensing Images

A novel fuzzy decision tree is proposed in this paper (the FDT-support vector machine (SVM) classifier), where the node discriminations are implemented via binary SVMs. The tree structure is determined via a class grouping algorithm, which forms the groups of classes to be separated at each internal node, based on the degree of fuzzy confusion between the classes. In addition, effective feature selection is incorporated within the tree building process, selecting suitable feature subsets required for the node discriminations individually. FDT-SVM exhibits a number of attractive merits such as enhanced classification accuracy, interpretable hierarchy, and low model complexity. Furthermore, it provides hierarchical image segmentation and has reasonably low computational and data storage demands. Our approach is tested on two different tasks: natural forest classification using a QuickBird multispectral image and urban classification using hyperspectral data. Exhaustive experimental investigation demonstrates that FDT-SVM is favorably compared with six existing methods, including traditional multiclass SVMs and SVM-based binary hierarchical trees. Comparative analysis is carried out in terms of testing rates, architecture complexity, and computational times required for the operative phase.

Neural network observer for induction motor control

In many practical problems the task is the control of a nonlinear plant, under parameter uncertainty, by a controller of known structure that uses the values of the state variables. However, it frequently is the case that some state variables cannot be measured. In this article this type of problem arises from the control requirements of an induction motor and is tackled by neural network based observer techniques so that the state variables are estimated while the variations of the unknown parameters are compensated for. Extensive simulations have shown that this scheme works well in the case of motor control.<<ETX>>