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PhD thesis - abstract

New stochastic optimization methods to the unit commitment problem are proposed. The mathematical model of the problem includes the cost function defined as the sum of variable production costs and start-up unit costs in 24-hour optimization period, the load  balance constraint, power generation limits of a single unit and set of units, and minimum up and down time constraints.

Methods of construction of variable cost and start-up cost characteristics are proposed. These characteristics include the costs of fuel, additional material costs, operating costs of the installations of combustion gases cleaning (desulphurization, denitrogenation), the cost of air pollution, waste storage costs and sewage costs.

Two approaches are proposed to the solution of the problem: (i) combined, using evolutionary algorithms, simulated annealing and a hybrid of both these algorithms and (ii) integrated, with comprehensive and sequential evolutionary algorithms. 

In the first approach, the optimization problem is decomposed into the problem of generating unit selection (combinatorial), solved using stochastic methods, and the problem of determining the generation levels of selected units (continuous), solved using Lagrange multipliers. Several ways of variable definitions and  representation methods adapted to them are proposed. Some heuristic operators to modify solutions in the process of searching the solution space are proposed – mutation, recombination and transposition operators. In the simulated annealing algorithm the adaptive annealing schedule, that allows taking into account the problem constraints, is introduced, where the temperature is adapted to the stages of searching the solution space. The hybrid algorithm has the structure of simulated annealing, but there are many parallel processes of annealing. The solution space is searched by a population of points which are modified independently in the inner loop of the algorithm and exchange information among themselves in the outer loop.

In the integrated approach, the evolutionary algorithms solve the problems of generating unit selection and determining the unit generation levels simultaneously. The comprehensive evolutionary algorithm optimizes the cost function in the whole optimization period, while  the  sequential evolutionary algorithm optimizes the cost function in the subsequent whiles of the  optimization period. The floating-point representation of variables and specialized operators of mutation, transposition and crossover are applied.

The cost function constrains are eliminated using the repair strategy of infeasible solutions (random or deterministic) and penalty functions.

All variants of the developed optimization algorithms were tested in a practical task. Comparative calculations were made using other optimization algorithms, deterministic and stochastic. In all cases the proposed algorithms gave better solutions.

A statistical analysis of the accuracy and sensitivity of the presented optimization methods was performed.

The proposed methods of unit commitment can be applied in a single power station and in power systems as well. They can also be used in some models of the energy market.

Habilitation thesis - abstract

In the habilitation thesis the models for short-term electric load forecasting based on the methods of machine learning, pattern recognition and computational intelligence are proposed. The common features of these models are learning on examples and using the similarities between patterns of seasonal cycles of the load time series. These series are non-stationary, heteroscedastic, show trend, many cycles of seasonal fluctuations and random noise. The new approach based on the pattern similarity simplify the forecasting problem and enables the development of effective forecasting models. These models are based on the following assumption: if the patterns of the seasonal cycles of the time series are similar to each other (input patterns), then the patterns of cycles following them (forecast patterns) are also similar to each other. This assumption allows us to build forecasting models using analogies between repetitive fragments of the time series with seasonal fluctuations. In this work a method of statistical analysis of the similarities between patterns, which allows us to confirm the validity of the assumption is proposed. Many definitions of input and output patterns and measures of similarity between them are given.
The forecasting machine learning models based on similarities between pattern of the daily cycles of time series are proposed:

  • model based on the kernel estimators,
  • models based on the nearest neighbor estimators: k nearest neighbor estimator and fuzzy estimators of the regression function,
  • model based on artificial immune system with local feature selection,
  • models based on pattern clustering: hard and fuzzy k-means, self-organizing feature map, neural gas, hierarchical clustering and two new artificial immune systems.

For learning and optimization of the models deterministic and stochastic methods, such as evolutionary algorithms and original tournament searching method, are used. The following analyses of the models are performed: computational complexity, sensitivity to changes in parameter values and resistance to noisy and incomplete data. The methods of statistical analysis of forecasting errors are described. The accuracies of the proposed models and classical models (ARIMA, exponential smoothing), as well as the neural network (multilayer perceptron) are compared.
Models based on the pattern similarity are distinguished by simplicity, clear structure and simple principle of operation. The number of parameters here is small, which implies a less complex process of learning and optimization, and better generalization of the model. The proposed models use nonparametric local regression, which ensures high accuracy, competitive with the accuracy of other forecasting models dedicated to this problem.

My research interests are focused on methods of computational intelligence, pattern recognition and machine learning in application to:

  • analysis and forecasting of time series with multiple seasonal cycles, 
  • data classification,
  • feature selection,
  • combinatorial and continuous optimization.

 

Analysis and forecasting of time series with multiple seasonal cycles

I proposed many forecasting models based on neural networks, artificial immune systems, fuzzy logic, clustering methods, regression trees, kernel and nearest neighbor estimators. A key element of these models is appropriate data preprocessing, which helps to simplify the forecasting problem. I tested these models in the short-term electrical load forecasting. I summarized some of my achievements in this field in the book: Similarity-based Machine Learning Methods for Short-Term Load Forecasting. Academic Publishing House EXIT, Warsaw 2012 (habilitation monograph; in Polish). 

I collaborate with the Polish power system operator (PSE-Operator) in developing forecasting models.   

 

Data classification  

I proposed a new multi-class classifier based on immune system principles. The unique feature of this classifier is the embedded property of local feature selection. This method of feature selection was inspired by the binding of an antibody to an antigen, which occurs between amino acid residues forming an epitope and a paratope. 

 

Feature selection

I proposed a new search method to the feature selection problem – the tournament searching. The tournament feature selection method is a simple stochastic searching method with only one parameter controlling the global-local searching properties of the algorithm. It is less complicated and easier to use than other stochastic methods, e.g. the simulated annealing or genetic algorithm. 

 

Combinatorial and continuous optimization

In my PhD thesis I solved the problem of unit commitment and economic dispatch using evolutionary algorithms and simulated annealing. I proposed several methods of data representations, heuristic genetic operators, definitions of the objective function for the feasible and infeasible solutions, adaptive annealing schedule and hybrid methods combining simulated annealing and evolutionary algorithms. I summarized my research in this field in the book: New Optimization Methods for the Electric Power Unit Commitment Problem. Czestochowa University of Technology Publishing House, Czestochowa 2011 (based on the PhD thesis; in Polish). 

I collaborated with the Polish power system operator (PSE-Operator) in developing methods of long-term optimal dispatch of generating units.

I proposed directed mutation operators (tournament mutation, roulette wheel mutation) to improve the speeding up the convergence of genetic algorithms and  tournament searching.

I used evolutionary computation and tournament searching in other optimization problems such as: tuning of the parameters of the forecasting models and hysteresis model, localization of the power substations.

Welcome to my website!

I am a Professor in the Faculty of Electrical Engineering at Czestochowa University of Technology (CUT), Poland. My research focuses on machine learning, artificial intelligence and data mining, with a particular emphasis on their applications in classification, regression, forecasting, and optimization problems.

 

Education and Qualifications

    • 1994 M.Sc., Faculty of Electrical Engineering, CUT
    • 2003 Ph.D., Faculty of Electrical Engineering, CUT
    • 2013 Habilitation, Faculty of Electrical, Electronic, Computer and Control Engineering, Łódź University of Technology
    • 2023 Full Professor

 

PhD thesis

Dudek G.: Unit Commitment using Evolutionary Algorithms. CUT, 2003 (in Polish) abstract

 

Habilitation thesis

Dudek G.: Similarity-based Machine Learning Methods for Short-Term Load Forecasting. Academic Publishing House EXIT, Warszawa 2012 (habilitation monograph; in Polish) abstract

 

Positions

    • 2014–23 Professor, Faculty of Electrical Engineering, CUT
    • 2014–18 Professor, Katowice School of Information Technologies
    • 2016–19 Director of the Computer Science Institute at CUT
    • 2016–19 Head of Data Engineering and Computational Intelligence Division at CUT
    • 2017–19 Deputy Dean for Scientific Research in the Faculty of Electrical Engineering at CUT
    • 2019 Head of Computer Science Division at CUT
    • 2023- Full professor, Faculty of Electrical Engineering, CUT
    • 2023- Professor, Centre for Data Analysis, Modelling and Computational Sciences (CAMiNO), University of Łódź  
    • 2023- Full professor, Faculty of Mathematics and Computer Science, University of Łódź

 

Grants

    • 1998-1999 Forecasting Electricity Demand using Statistical Pattern Recognition Methods. Nature of contribution: main contractor. Funding: Research Committee
    • 1999-2000 Short-Term Load Forecasting using RBF Networks. Nature of contribution: project manager and head contractor. Funding: Research Committee
    • 2001-2002 Unit Commitment for Thermal Power Stations using Evolutionary Algorithms and Simulated Annealing. Nature of contribution: project manager and head contractor. Funding: Research Committee
    • 2005-2006 Short-Term Load Forecasting using Fuzzy Clustering and Genetic Algorithms. Nature of contribution: project manager and head contractor. Funding: Research Committee
    • 2007-2008 Short-Term Forecasting of Electricity Demand and Energy Prices on the Stock and Balancing Markets using Cluster Analysis Methods. Nature of contribution: project manager and head contractor. Funding: CUT Rector
    • 2008-2009 Short-Term Forecasting of Electricity Demand and Energy Prices on the Stock and Balancing Markets using Nonparametric Regression Methods. Nature of contribution: project manager and head contractor. Funding: CUT Rector
    • 2010-2013 Non-Classical Methods of Short-Term Load Forecasting. Nature of contribution: project manager and head contractor. Funding: Ministry of Science and Higher Education
    • 2018-2020 Randomized Learning Methods for Artificial Neural Networks. Nature of contribution: project manager and head contractor. Funding: National Science Centre

 

Publications

Four books, over 130 papers and chapters

 

Honors and awards

    • Ranked in the ranking of the world's most influential people in science (top 2% list, Stanford University and Elsevier)

      Career-long impact

      • 2020 - #163669
      • 2021 - #160743
      • 2022 - #152973
      • 2023 - #140584

      Single year impact

      • 2019 - #65010
      • 2020 - #68514
      • 2021 - #70158
      • 2022 - #60118
      • 2023 - #33607

       

    • Third place in Global Energy Forecasting Competition 2014 (GEFCOM 2014) - Price forecasting 
    • 18 awards of CUT Rector for research activity (2011-2023)
    • 8 awards of CUT Rector for organizational activity (2010-2018)
    • Our paper is one of the most downloaded papers of Applied Soft Computing journal

 

 

Projects for industry

Ten projects for energy companies, mainly for the Polish power system operator (PSE-Operator) and Tauron, in developing the electrical load forecasting models, methods of long-term optimal dispatch of generating units and smart grid data analysis

 

Software

    • FatRec – a computer program for determining the expected and theoretical frequencies of genotypes in a population, heterozygosity, power of discrimination, probability of paternity exclusion and polymorphic information content. For Department of Forensic Medicine, Medical University of Silesia, Katowice, 2001
    • FatRec2 – a computer program for determining the paternity probability on the basis of genotype and allele frequencies. For Department of Forensic Medicine, Medical University of Silesia, Katowice, 2003
    • HLA Patch Generator – an Excel application for generation of HLA eplets. For prof. Rene Duquesnoy, University of Pittsburgh Medical Center, USA, 2004
    • HLAMatching  -  a computer program for generating triplets compatible in terms of HLA antigens – for the selection of donors in leukemia. For Medigen, Warsaw, 2004
    • HLAMatchmaker - ABC Eplet Matching - a computer program for typing compatibility between donor and recipient at the HLA eplet level. For prof. Rene Duquesnoy, University of Pittsburgh Medical Center, USA, 2010 more information about triplets and eplets
    • HLAmatching_2 - a computer program for statistical analysis of the results of HLA genotype matching and determination of the survival curve. For Medigen, Warsaw, 2017

 

Teaching

Artificial intelligence, evolutionary algorithms, expert systems, machine learning, fuzzy modeling, operations research and optimization, programming in C, Python, Pascal and Java Script, nuclear power plants

 

Review activities

    • IEEE Transactions on Evolutionary Computation
    • IEEE Transactions on Neural Networks and Learning Systems
    • IEEE Transactions on Cybernetics
    • IEEE Transactions on Industrial Electronics
    • Neural Networks
    • Information Sciences
    • Knowledge-Based Systems
    • Applied Soft Computing
    • Expert Systems with Applications
    • Neurocomputing
    • Neural Computation and Applications
    • Computational Optimization and Applications
    • Engineering Applications of Artificial Intelligence
    • Complexity
    • International Journal of Forecasting
    • European Journal of Operational Research
    • Computers in Human Behavior
    • Journal of Artificial Intelligence and Soft Computing Research
    • Applied Energy
    • Energies
    • Transactions on Computational Collective Intelligence
    • International Journal of Applied Mathematics and Computer Science, Special Issue on Evolutionary Computation
    • International Journal of Electrical Power & Energy Systems
    • Intelligent Decision Technologies
    • Przegląd Elektrotechniczny (Electrical Review)
    • Rynek Energii (Energy Market) 
    • ...

2024

        1. Smyl S., Oreshkin B.N., Pełka P., Dudek G.: Any-Quantile Probabilistic Forecasting of Short-Term Electricity Demand. Applied Energy (submitted), 2024.
          https://doi.org/... | ArXiv2404.17451
        2. Smyl S., Dudek G., Pełka P.: Contextually Enhanced ES-dRNN with Dynamic Attention for Short-Term Load Forecasting. Neural Networks, vol. 169, pp. 660-672, 2024.
          https://doi.org/10.1016/j.neunet.2023.11.017 | ArXiv:2212.09030
        3. Dudek G., Fiszeder P., Kobus P., Orzeszko W.: Forecasting Cryptocurrencies Volatility using Statistical and Machine Learning Methods: A Comparative Study. Applied Soft Computing, vol. 151, pp. 111132, 2024.
          https://doi.org/10.1016/j.asoc.2023.111132 
        4. Galita G., Sarnik J., Brzezinska O., Budlewski T., Poplawska M., Sakowski S., Dudek G., Majsterek I., Makowska J., Poplawski T.: The Association between Inefficient Repair of DNA Double-Strand Breaks and Common Polymorphisms of the HRR and NHEJ Repair Genes in Patients with Rheumatoid Arthritis. International Journal of Molecular Sciences, vol. 25(5), pp. 2619, 2024.
          https://doi.org/10.3390/ijms25052619
        5. Dudek G., Sakowski S., Brzezińska O., Sarnik J., Budlewski T., Dragan G., Poplawska M., Poplawski T., Bijak M., Makowska J.: Machine Learning-based Prediction of Rheumatoid Arthritis with Development of ACPA Autoantibodies in the Presence of Non-HLA Genes Polymorphisms. Plos One 19(3): e0300717, 2024.
          https://doi.org/10.1371/journal.pone.0300717
        6. Dudek G. (ed.): Applied Machine Learning II. Applied Sciences special issue reprint, MDPI, 2024.
          https://www.mdpi.com/books/reprint/8975-applied-machine-learning 
        7. Dudek G.: Meta-Learning based on Recurrent Neural Networks for Ensembling Forecasts of Time Series with Multiple Seasonal Patterns. Contribution to Statistics, Springer, 2024 (submitted).
          https://doi.org/... | ArXiv:...
        8. Dudek G.: Meta-Learning for Combining Forecasts: Deterministic and Probabilistic Approaches. Proc. 5th Polish Conference on Artificial Intelligence (PP-RAI'24), Progress in Polish Artificial Intelligence Research 5, pp. 43-48, 2024.
          https://doi.org/10.17388/WUT.2024.0002.MiNI
        9. Dudek G.: Stacking for Probabilistic Short-term Load Forecasting. In: L. Franco et al. (eds.), Computational Science – ICCS 2024. LNCS, vol. 14833, pp. 3–18, Springer, Cham, 2024.
          https://doi.org/10.1007/978-3-031-63751-3_1 | ArXiv:2406.10718 | pdf
        10. Jankowski N., Dudek G.: Automatic Kernel Construction During the Neural Network Learning by Modified Fast Singular Value Decomposition. In: L. Franco et al. (eds.), Computational Science – ICCS 2024. LNCS, vol. 14834, pp. 205–212, Springer, Cham, 2024.
          https://doi.org/10.1007/978-3-031-63759-9_25 | pdf

2023

        1. Dudek G.: STD: A Seasonal-Trend-Dispersion Decomposition of Time Series. IEEE Transaction on Knowledge and Data Engineering, 2023.
          https://doi.org/10.1109/TKDE.2023.3268125 | ArXiv:2204.10398
        2. Smyl S., Dudek G., Pełka P.: ES-dRNN: A Hybrid Exponential Smoothing and Dilated Recurrent Neural Network Model for Short-Term Load Forecasting. IEEE Transactions on Neural Networks and Learning Systems, 2023 (in print).
          https://doi.org/10.1109/TNNLS.2023.3259149 | ArXiv:2112.02663
        3. Dudek G. (ed.): Applied Machine Learning. Applied Sciences special issue reprint, MDPI, 2023.
          https://doi.org/10.3390/books978-3-0365-7907-8 
        4. Dudek G.: Applied Machine Learning: New Methods, Applications, and Achievements. Applied Sciences, vol. 13(19), pp. 10845, 2023.
          https://doi.org/10.3390/app131910845
        5. Dudek G., Piotrowski P., Baczyński D.: Intelligent Forecasting and Optimization in Electrical Power Systems: Advances in Models and Applications. Energies, vol. 16(7), pp. 3024, 2023.
          https://doi.org/10.3390/en16073024
        6. Dudek G.: Combining Forecasts using Meta-Learning: A Comparative Study for Complex Seasonality. IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA'23, pp. 1-10, 2023.
          https://doi.org/10.1109/DSAA60987.2023.10302585 | ArXiv:...
        7. Smyl S., Dudek G., Pełka P.: Forecasting Cryptocurrency Prices using Contextual ES-adRNN with Exogenous Variables. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds.), Computational Science – ICCS 2023. LNCS, vol. 14073, pp. 450-464, Springer, Cham, 2023.
          https://doi.org/10.1007/978-3-031-35995-8_32 | ArXiv:...
        8. Dudek G.: Ensemble of Randomized Neural Networks with STD decomposition for Forecasting Time Series with Complex Seasonality. In: Rojas I., Joya G., Catala A. (eds) Advances in Computational Intelligence. 17th International Work-Conference on Artificial Neural Networks IWANN 2023. LNCS 14134, pp. 690-702. Springer, Cham 2023.
          https://doi.org/10.1007/978-3-031-43085-5_55 | ArXiv:...
        9. Dudek G.: Combining Forecasts of Time Series with Complex Seasonality using LSTM-based Meta-Learning. Engineering Proceedings 39, 53, (ITISE'23), 2023.
          https://doi.org/10.3390/engproc2023039053
        10. Smyl S., Dudek G., Pełka P.: Contextual ES-adRNN with Attention Mechanisms for Forecasting. In: Wojciechowski A., Lipiński P. (eds) Progress in Polish Artificial Intelligence Research 4 (Proc. 4th Polish Conference on Artificial Intelligence, PP-RAI'23), Series: Monographs of the Lodz University of Technology No. 2437, Lodz University of Technology Publishing House, pp. 101-106, 2023.
          https://doi.org/10.34658/9788366741928.14
        11. Dudek G., Smyl. S., Pełka P.: Recurrent Neural Networks for Forecasting Time Series with Multiple Seasonality: A Comparative Study. In: Valenzuela, O., Rojas, F., Herrera, L.J., Pomares, H., Rojas, I. (eds) Theory and Applications of Time Series Analysis. ITISE 2022. Contributions to Statistics. Springer, Cham, pp. 171-185, 2023.
          https://doi.org/10.1007/978-3-031-40209-8_12 | ArXiv:2203.09170

2022

        1. Orzeszko W., Dudek G., Stasiak M.D., Stawiarz M.: Time Series Forecasting in Economy and Finance using Machine Learning Metchods. Wydawnictwo Naukowe UMK, Toruń, 2022. (in Polish: Prognozowanie szeregów czasowych w ekonomii i finansach z wykorzystaniem metod uczenia maszynowego).
          Publisher's website
        2. Dudek G., Pełka P., Smyl S.: A Hybrid Residual Dilated LSTM end Exponential Smoothing Model for Mid-Term Electric Load Forecasting. IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 7, pp. 2879-2891, 2022.
          https://doi.org/10.1109/TNNLS.2020.3046629 | ArXiv:2004.00508 | pdf
        3. Dudek G: A Comprehensive Study of Random Forest for Short-Term Load Forecasting. Energies, vol. 15(20), pp. 7547, 2022.
          https://doi.org/10.3390/en15207547 | pdf
        4. Dudek G.: Special Issue on Applied Machine Learning. Applied Sciences, vol. 12(4), pp. 2039, 2022.
          https://doi.org/10.3390/app12042039
        5. Dudek G.: Boosted Ensemble Learning based on Randomized NNs for Time Series Forecasting. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds.) Computational Science – ICCS 2022. LNCS, vol. 13350, pp. 360-374, Springer, Cham, 2022. 
          https://doi.org/10.1007/978-3-031-08751-6_26 | ArXiv:2203.00980
        6. Smyl S., Dudek G., Pełka P.: ES-dRNN with Dynamic Attention for Short-Term Load Forecasting. Proc. Int. Joint Conf. on Neural Networks (IJCNN 2022), pp. 1-8, 2022.
          https://doi.org/10.1109/IJCNN55064.2022.9889791 | ArXiv:2203.00937
        7. Pełka P., Dudek G., Smyl S.: An Ensemble of Attentive Recurrent Networks with Randomized Dilations for Forecasting. Proc. 3rd Polish Conference on Artificial Intelligence (PP-RAI'22), pp. 57-60, 2022. 
          source | pdf

2021

        1. Dudek G., Pełka P.: Pattern Similarity-based Machine Learning Methods for Mid-term Load Forecasting: A Comparative Study. Applied Soft Computing, vol. 104, pp. 107223, 2021.
          https://doi.org/10.1016/j.asoc.2021.107223 | ArXiv:2003.01475 | pdf
        2. Oreshkin B., Dudek G., Pełka P., Turkina E.: N-BEATS Neural Network for Mid-Term Electricity Load Forecasting. Applied Energy, vol. 293, pp. 116918, 2021.
          https://doi.org/10.1016/j.apenergy.2021.116918 | ArXiv:2009.11961 | pdf
        3. Dudek G.: A Constructive Approach to Data-Driven Randomized Learning for Feedforward Neural Networks. Applied Soft Computing, vol. 112, pp. 107797, 2021.
          https://doi.org/10.1016/j.asoc.2021.107797 | ArXiv:1909.01961 | pdf
        4. Dudek G.: Short-Term Load Forecasting using Neural Networks with Pattern Similarity-based Error Weights. Energies, vol. 14(11), pp. 3224, 2021.
          https://doi.org/10.3390/en14113224 | pdf
        5. Dudek G., Pełka P.: Ensembles of Randomized Neural Networks for Pattern-based Time Series Forecasting. In: Mantoro T., Lee M., Ayu M.A., Wong K.W., Hidayanto A.N. (eds) Neural Information Processing, ICONIP 2021, LNCS 13110, pp. 418-430, Springer, ChamICONIP 2021.
          https://doi.org/10.1007/978-3-030-92238-2_35 | ArXiv:2107.04091 | pdf
        6. Dudek G.: Autoencoder based Randomized Learning of Feedforward Neural Networks for Regression. Proc. Int. Joint Conf. on Neural Networks (IJCNN 2021), pp. 1-8, 2021.
          https://doi.org/10.1109/IJCNN52387.2021.9534263 | ArXiv:2107.01711
        7. Dudek G.: Data-Driven Learning of Feedforward Neural Networks with Different Activation Functions.  In: Rutkowski L., Scherer R., Korytkowski M., Pedrycz W., Tadeusiewicz R., Zurada J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2021. LNCS 12854, pp. 66-77. Springer, Cham 2021.
          https://doi.org/10.1007/978-3-030-87986-0_6 | ArXiv:2107.01702 | pdf
        8. Dudek G.: Randomized Neural Networks for Forecasting Time Series with Multiple Seasonality. In: Rojas I., Joya G., Catala A. (eds) Advances in Computational Intelligence. 16th International Work-Conference on Artificial Neural Networks IWANN 2021. LNCS 12862, pp. 196-207. Springer, Cham 2021.
          https://doi.org/10.1007/978-3-030-85099-9_16 | ArXiv:2107.01705 | pdf
        9. Dudzik S., Dudek G.: Detection of Thinning of Homogeneous Material Using Active Thermography and Classification Trees. Metrology and Measurement Systems, vol. 28, no. 1, 2021.
          https://doi.org/10.24425/mms.2021.135994 | pdf

2020

        1. Bejger S., Dudek G., Orzeszko W., Stasiak M., Targiel K.: Machine Learning in Making Forecasting Decisions. Wydawnictwo Naukowe UMK, Toruń, 2020. (in Polish: Uczenie maszynowe w podejmowaniu decyzji prognostycznych).
          Publisher's website
        2. Dudek G., Pełka P., Smyl S.: 3ETS+RD-LSTM: A New Hybrid Model for Electrical Energy Consumption Forecasting. In: Yang H., Pasupa K., Leung A.CS., Kwok J.T., Chan J.H., King I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science, vol 12534, pp. 519-531. Springer, Cham.
          https://doi.org/10.1007/978-3-030-63836-8_43 | pdf
        3. Dudek G.: Generating Random Parameters in Feedforward Neural Networks with Random Hidden Nodes: Drawbacks of the Standard Method and How to Improve It. In: Yang H., Pasupa K., Leung A.CS., Kwok J.T., Chan J.H., King I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol. 1333, pp. 598-606. Springer, Cham.
          https://doi.org/10.1007/978-3-030-63823-8_68 | ArXiv:1908.05864 | pdf
        4. Dudek G.: Are Direct Links Necessary in Random Vector Functional Link Networks for Regression? In: Rutkowski L., Scherer R., Korytkowski M., Pedrycz W., Tadeusiewicz R., Zurada J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2020. LNSC 12415, pp. 60-70. Springer, Cham 2020.
          https://doi.org/10.1007/978-3-030-61401-0_6 | ArXiv:2003.13090 | pdf
        5. Pełka P., Dudek G.: Ensemble Forecasting of Monthly Electricity Demand using Pattern Similarity-based Methods. In: Rutkowski L., Scherer R., Korytkowski M., Pedrycz W., Tadeusiewicz R., Zurada J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2020. LNSC 12415, pp. 712-723. Springer, Cham 2020.
          https://doi.org/10.1007/978-3-030-61401-0_66 | ArXiv:2004.00426 | pdf
        6. Pełka P., Dudek G.: Pattern-based Long Short-term Memory for Mid-term Electrical Load Forecasting. Proc. Int. Joint Conf. on Neural Networks (IJCNN 2020), pp. 1-8, 2020.
          https://doi.org/10.1109/IJCNN48605.2020.9206895 | pdf
        7. Dudek G.: Data-Driven Randomized Learning of Feedforward Neural Networks. Proc. Int. Joint Conf. on Neural Networks (IJCNN 2020), pp. 1-8, 2020.
          https://doi.org/10.1109/IJCNN48605.2020.9207353 | ArXiv:1908.03891 | pdf

2019

        1. Dudek G.: Generating random weights and biases in feedforward neural networks with random hidden nodes. Information Sciences, vol. 481, pp. 33-56, 2019.
          https://doi.org/10.1016/j.ins.2018.12.063 | arXiv:1710.04874 (old version) | pdf
        2. Dudek G.: Multilayer Perceptron for Short-Term Load Forecasting: From Global to Local Approach. Neural Computing and Applications 32, pp. 3695–3707, 2019.
          https://doi.org/10.1007/s00521-019-04130-y | pdf
        3. Dudek G.: New Methods of Generating Random Parameters in Feedforward Neural Networks with Random Hidden Nodes. PP-RAI'19, 2019. 
          pdf
        4. Dudek G.: Sensitivity Analysis of the Neural Networks Randomized Learning. In: Rutkowski L., Scherer R., Korytkowski M., Pedrycz W., Tadeusiewicz R., Zurada J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2019. LNAI 11508, pp. 51-61. Springer, Cham 2019. 
          https://doi.org/10.1007/978-3-030-20912-4_5 | pdf
        5. Pełka P., Dudek G.: Pattern-Based Forecasting Monthly Electricity Demand Using Multilayer Perceptron. In: Rutkowski L., Scherer R., Korytkowski M., Pedrycz W., Tadeusiewicz R., Zurada J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2019. LNAI 11508, pp. 663-672. Springer, Cham 2019. 
          https://doi.org/10.1007/978-3-030-20912-4_60 | pdf
        6. Dudek G.: Improving Randomized Learning of Feedforward Neural Networks by Appropriate Generation of Random Parameter. In: Rojas I., Joya G., Catala A. (eds) Advances in Computational Intelligence. 15th International Work-Conference on Artificial Neural Networks IWANN 2019. LNCS 11506, pp. 517-530. Springer, Cham 2019. 
          https://doi.org/10.1007/978-3-030-20521-8_43 | arXiv:1908.05542 | pdf
        7. Dudek G.: Short-term load forecasting using Theta method. Proc. 14th Int. Conf. on Forecasting in Power Engineering 2018, E3S Web Conf. vol. 84, 2019.
          https://doi.org/10.1051/e3sconf/20198401004 | pdf
        8. Kornatka M., Gawlak A., Dudek G.: Determination of reliability indices of the distribution network based on data from AMI. Proc. 14th Int. Conf. on Forecasting in Power Engineering 2018, E3S Web Conf. vol. 84, 2019.
          https://doi.org/10.1051/e3sconf/20198402004 | pdf
        9. Pełka P., Dudek G.: Medium-Term Electric Energy Demand Forecasting Using Generalized Regression Neural Network. In: Świątek J., Borzemski L., Wilimowska Z. (eds) Information Systems Architecture and Technology: Proceedings of 39th International Conference on Information Systems Architecture and Technology – ISAT 2018. Advances in Intelligent Systems and Computing, vol 853, pp. 218-227. Springer, Cham 2019.
          https://doi.org/10.1007/978-3-319-99996-8_20 | pdf

2018

        1. Dudek G.: Probabilistic Forecasting of Electricity Prices using Kernel Regression. Proc. 15th Int. Conf. on European Energy Market (EEM'2018), pp. 1-5, 2018.
          https://doi.org/10.1109/EEM.2018.8469930 | pdf
        2. Dudek G., Szkutnik J., Gawlak A., Kornatka M.: Analysis of Smart Meter Data for Electricity Consumers. Proc. 15th Int. Conf. on European Energy Market (EEM'2018), pp. 1-5, 2018.
          https://doi.org/10.1109/EEM.2018.8469896 | pdf
        3. Dudek G., Gawlak A., Kornatka M., Szkutnik J.: The Method of Detecting Illegal Electricity Consumption Using the AMI System. Proc. 15th Int. Conf. on European Energy Market (EEM'2018), pp. 1-5, 2018.
          https://doi.org/10.1109/EEM.2018.8470006 | pdf
        1. Dudek G.: Multivariate Regression Tree for Pattern-based Forecasting Time Series with Multiple Seasonal Cycles. In: Borzemski L., Świątek J., Wilimowska Z. (eds) Information Systems Architecture and Technology: Proceedings of 38th International Conference on Information Systems Architecture and Technology – ISAT 2017. Advances in Intelligent Systems and Computing, vol. 655, pp. 85-94. Springer, Cham, 2018.
          https://doi.org/10.1007/978-3-319-67220-5_8  | pdf
        2. Dudek G., Dudzik S.: Classification Tree for Material Defect Detection using Active Thermography. In: Borzemski L., Świątek J., Wilimowska Z. (eds) Information Systems Architecture and Technology: Proceedings of 38th International Conference on Information Systems Architecture and Technology – ISAT 2017. Advances in Intelligent Systems and Computing, vol 655, pp. 118-127. Springer, Cham, 2018.
          https://doi.org/10.1007/978-3-319-67220-5_11  | pdf
        3. Pełka P., Dudek G.: Neuro-Fuzzy System for Medium-term Electric Energy Demand Forecasting. In: Borzemski L., Świątek J., Wilimowska Z. (eds) Information Systems Architecture and Technology: Proceedings of 38th International Conference on Information Systems Architecture and Technology – ISAT 2017. Advances in Intelligent Systems and Computing, vol 655, pp. 38-47. Springer, Cham, 2018.
          https://doi.org/10.1007/978-3-319-67220-5_4  | pdf

2017

        1. Dudek G.: Artificial Immune System with Local Feature Selection for Short-Term Load Forecasting. IEEE Transactions on Evolutionary Computation, vol. 21, pp. 116-130, 2017.
          http://dx.doi.org/10.1109/TEVC.2016.2586049 | pdf
        2. Dudek G.: Stochastic Optimization Algorithms for Learning GRNN Forecasting Model – Comparative Study. Przegląd Elektrotechniczny (Electrical Review), r. 93(4), pp. 66-69, 2017.
          http://dx.doi.org/10.15199/48.2017.04.17 | pdf
        3. Dudek G., Janicki M.: Nearest Neighbour Model with Weather Inputs for Pattern-based Electricity Demand Forecasting. Przegląd Elektrotechniczny (Electrical Review), r. 93(3), pp.7-10, 2017. 
          http://dx.doi.org/10.15199/48.2017.03.02 | pdf
        4. Dudek G., Pełka P.: Forecasting monthly electricity demand using k nearest neighbor method. Przegląd Elektrotechniczny (Electrical Review), r. 93(4), pp.62-65, 2017 (in Polish). 
          http://dx.doi.org/10.15199/48.2017.04.16  | pdf
        5. Pełka P., Dudek G.: Prediction of Monthly Electric Energy Consumption using Pattern-Based Fuzzy Nearest Neighbour Regression. Proc. 2nd Int. Conf. Computational Methods in Engineering Science (CMES'17), ITM Web Conf. Vol. 15, pp. 1-5, 2017.
          https://doi.org/10.1051/itmconf/20171502005  | pdf
        6. Dudek G., Pełka P.: Medium-term electric energy demand forecasting using Nadaraya-Watson estimator. Proc. 18th Int. Scientific Conf. on Electric Power Engineering (EPE'17), pp. 1-6, 2017.
          https://doi.org/10.1109/EPE.2017.7967255  | pdf
        7. Dudek G., Szkutnik J.: Daily load curves in distribution networks — Analysis of diversity and outlier detection. Proc. 18th Int. Scientific Conf. on Electric Power Engineering (EPE'17), pp. 1-5, 2017.
          https://doi.org/10.1109/EPE.2017.7967357 | pdf
        8. Dudek G.: Ensembles of general regression neural networks for short-term electricity demand forecasting;. Proc. 18th Int. Scientific Conf. on Electric Power Engineering (EPE'17), pp. 1-5, 2017.
          https://doi.org/10.1109/EPE.2017.7967256 | pdf

2016

        1. Dudek G.: Neural Networks for Pattern-based Short-Term Load Forecasting: A Comparative Study. Neurocomputing, vol. 2015, pp. 64-74, 2016.
          http://dx.doi.org/10.1016/j.neucom.2016.04.021 | pdf
        2. Dudek G.: Heterogeneous Ensembles for Short-Term Electricity Demand Forecasting. Proc. 17th Conf. Electric Power Engineering (EPE'2016), pp. 1-6, 2016.
          http://dx.doi.org/10.1109/EPE.2016.7521771 | pdf
        3. Opaliński A., Dudek G.: Electricity Demand Prediction by Multi-Agent System with History-based Weighting. Proc. 17th Conf. Electric Power Engineering (EPE'2016), pp. 1-5, 2016.
          http://dx.doi.org/10.1109/EPE.2016.7521810 | pdf
        4. Dudek G.: Multilayer Perceptron for GEFCom2014 Probabilistic Electricity Price Forecasting. International Journal of Forecasting, vol. 32, pp. 1057-1060, 2016.
          http://dx.doi.org/10.1016/j.ijforecast.2015.11.009 | pdf
        5. Dudek G.: Extreme Learning Machine as A Function Approximator: Initialization of Input Weights and Biases. Proc. 9th Int. Conf. Computer Recognition Systems (CORES 2015), pp. 59-69, 2016.
          http://dx.doi.org/10.1007/978-3-319-26227-7_6 | pdf
        6. Dudek G.: Pattern-based Local Linear Regression Models for Short-Term Load Forecasting. Electric Power System Research, vol. 130, pp. 139-147, 2016.
          http://dx.doi.org/10.1016/j.epsr.2015.09.001 | pdf

2015

        1. Dudek G.: Pattern Similarity-based Methods for Short-term Load Forecasting – Part 2: Models. Applied Soft Computing, vol. 36, pp. 422-441, 2015.
          http://dx.doi.org/10.1016/j.asoc.2015.07.035 | pdf
        2. Dudek G.: Pattern Similarity-based Methods for Short-term Load Forecasting – Part 1: Principles. Applied Soft Computing, vol. 37, pp. 277-287, 2015.
          http://dx.doi.org/10.1016/j.asoc.2015.08.040 | pdf
        3. Dudek G.: Short-Term Load Cross-Forecasting using Pattern-Based Neural Models. Proc. 16th Int. Conf. on Electric Power Engineering (EPE'2015), pp. 179-183, 2015.
          http://dx.doi.org/10.1109/EPE.2015.7161178 | pdf
        4. Dudek G.: Extreme Learning Machine for Function Approximation – Interval Problem of Input Weights and Biases. Proc. IEEE 2nd Int. Conf. on Cybernetics (CybConf’2015), pp. 62-67, 2015.
          http://dx.doi.org/10.1109/CYBConf.2015.7175907 | pdf
        5. Popławski T., Dudek G., Łyp J.: Forecasting methods for balancing energy market in Poland. International Journal of Electrical Power and Energy Systems, vol. 65, pp. 94-101, 2015.
          http://dx.doi.org/10.1016/j.ijepes.2014.09.029 | pdf
        6. Dudek G.: Generalized Regression Neural Network for Forecasting Time Series with Multiple Seasonal Cycles. In: Filev D. et al. (eds.): Intelligent Systems’2014, Advances in Intelligent Systems and Computing 323, pp. 839-846, 2015.
          http://dx.doi.org/10.1007/978-3-319-11310-4_73 | pdf
        7. Dudek G.: Short-Term Load Forecasting using Random Forests. In: Filev D. et al. (eds.): Intelligent Systems’2014, Advances in Intelligent Systems and Computing 323, pp. 821-828, 2015.
          http://dx.doi.org/10.1007/978-3-319-11310-4_71 | pdf

2014

        1. Dudek G.: Short-term load forecasting using fuzzy regression trees. Przegląd Elektrotechniczny (Electrical Review), r. 90, nr 4, s. 108-111, 2014 (in Polish). pdf
        2. Dudek G.: Tournament Searching Method for Optimization of the Forecasting Model Based on the Nadaraya-Watson Estimator. In: Rutkowski L. et al. (eds.): Artificial Intelligence and Soft Computing, ICAISC 2014, LNAI 8468, pp. 351-360, 2014. pdf

2013

        1. Dudek G.: Genetic algorithm with binary representation of generating unit start-up and shut-down times for the unit commitment problem. Expert Systems with Applications, vol. 40, issue 15, pp. 6080-6086, 2013. pdf
        2. Dudek G.: Exponential Smoothing Models for Short-Term Load Forecasting. Rynek Energii (Energy Market), special issue no. 1 (VIII), pp. 64-69, 2013 (in Polish). pdf
        3. Dudek G.: Forecasting Time Series with Multiple Seasonal Cycles using Neural Networks with Local Learning. In: Rutkowski L. et al. (eds.): Artificial Intelligence and Soft Computing, ICAISC 2013, LNCS 7894, pp. 52-63, 2013. pdf
        4. Dudek G.: Simulated Annealing Combined with Evolutionary Algorithm to Unit Commitment Problem. In: Rocha C. et al. (eds.): Artificial Intelligence andHybrid Systems, pp. 175-196, iConcept Press 2013. pdf
        5. Dudek G.: Artificial Immune System for Forecasting Time Series with Multiple Seasonal Cycles. Transactions on Computational Collective Intelligence XI, LNCS 8065, pp. 176-197, 2013. pdf

2012

        1. Dudek G.: Similarity-based Machine Learning Methods for Short-Term Load Forecasting. Academic Publishing House EXIT, Warszawa 2012 (habilitation monograph; in Polish: Systemy uczące się oparte na podobieństwie obrazów do prognozowania szeregów czasowych obciążeń elektroenergetycznych).
          Publisher's website
        2. Dudek G.: Artificial immune system for classification with local feature selection. IEEE Transactions on Evolutionary Computation, vol. 16, issue 6, pp. 847-860, 2012. pdf>
        3. Dudek G.: Approximation of the Hysteresis Loop using Computational Intelligence Methods. Przegląd Elektrotechniczny (Electrical Review), r. 88, no. 12b, pp. 8-11, 2012 (in Polish). pdf
        4. Chwastek K., Dudek G.: Estimation of Parameters for a Hysteresis Model using Evolution Strategy. Przegląd Elektrotechniczny (Electrical Review), r. 88, no. 12b, pp. 5-7, 2012 (in Polish). pdf
        5. Dudek G.: ARIMA Models for Short-Term Load Forecasting. Rynek Energii (Energy Market) 2 (99), pp. 94-98, 2012 (in Polish). pdf
        6. Dudek G.: Optimization of the Forecasting Models Based on the Nearest Neighbor Estimators. Rynek Energii (Energy Market), special issue no. 1 (VII), pp. 118-123, 2012 (in Polish). pdf
        7. Dudek G.: Tournament Feature Selection with Directed Mutations. In: Rutkowski L. et al. (eds.): Swarm and Evolutionary Computation, LNCS 7269, pp. 190-198. pdf
        8. Dudek G.: Variable Selection in the Kernel Regression based Short-Term Load Forecasting Model. In: Rutkowski L. et al. (eds.): Artificial Intelligence and Soft Computing, LNCS 7268, pp. 557-563. pdf

2011

        1. Dudek G.: New Optimization Methods for the Electric Power Unit Commitment Problem. Czestochowa University of Technology Publishing House, Częstochowa 2011 (based on the PhD thesis; in Polish: Nowe metody rozdziału obciążeń w elektroenergetyce).

        2. Dudek G.: Artificial Immune Clustering Algorithm to Forecasting Seasonal Time Series. In Jędrzejowicz P. et al. (eds): Computational Collective Intelligence. Technologies and Applications, ICCCI 2011, LNCS 6922, pp. 468-477, 2011. pdf
        3. Dudek G.: Artificial Immune System to Short-Term Load Forecasting. Śląskie Wiadomości Elektryczne (Silesian Electric News), no. 6, pp. 12-15, 2011 (in Polish).
        4. Dudek G.: Optimization of the Kernel Regression Model to Short-Term Load Forecasting, Przegląd Elektrotechniczny (Electrical Review), r. 87, no. 9a, pp. 222-225, 2011 (in Polish).
        5. Dudek G.: Forecasting of the Daily Load Curves using Cluster Analysis Methods. Rynek Energii (Energy Market) 2 (93), pp. 73-78, 2011 (in Polish).
        6. Dudek G.: Next day electric load curve forecasting using k-means clustering. Rynek Energii (Energy Market) 1 (92), pp. 143-149, 2011.
        7. Dudek G.: Neuro-fuzzy approach to the next day load curve forecasting. Przegląd Elektrotechniczny (Electrical Review), r. 87, no. 2, pp. 61-64, 2011.

2010

        1. Dudek G.: Short-term load forecasting based on kernel conditional density estimation. Przegląd Elektrotechniczny (Electrical Review), r. 86, no 8, pp. 164-167, 2010. pdf
        2. Dudek G.: Tournament searching method to feature selection problem. In: Rutkowski L. et al. (eds.): Artificial Intelligence and Soft Computing, ICAISC 2010, LNCS 6114, pp. 437-444. pdf
        3. Dudek G.: Forecasting Model based on the Fuzzy Estimators of the Regression Function for Preparing the Daily Coordination Plans. Rynek Energii (Energy Market), special issue no. 1 (V), pp. 150-155, 2010 (in Polish).
        4. Dudek G. Adaptive simulated annealing schedule to the unit commitment problem. Electric Power Systems Research, vol. 80, issue 4, pp. 465-472, 2010. pdf
        5. Dudek G. Similarity-based approaches to short-term load forecasting. In: Zhu J.J., Fung G.P.C. (eds.): Forecasting Models: Methods and Applications. pp. 161-178, iConcept Press 2010. pdf

2009

        1. Dudek G.: Short-Term Load Forecasting using Nearest Neighbour Estimators. Proc. XIV Conf. Present-Day Problems of Power Engineering, vol. 3, pp. 31-39, 2009 (in Polish).
        2. Dudek G.: PWR and BWR Nuclear Power Plant Simulators for Education of the International Atomic Energy Agency. Proc. XIV Conf. Present-Day Problems of Power Engineering, vol.. 1, pp. 269-277, 2009 (in Polish).
        3. Dudek G. A comparison of the neural gas and self organizing map methods for next day load curve forecasting. Przegląd Elektrotechniczny (Electrical Review), r. 85, no. 3, pp. 153-156, 2009.
        4. Dudek G. Similarity Analysis of the Patterns of the Electrical Load Time Series Sequences. Przegląd Elektrotechniczny (Electrical Review), r. 85, no. 3, pp. 149-152, 2009 (in Polish).

2008

        1. Dudek G.: Artificial Immune System for Short-Term Electric Load Forecasting. In: Rutkowski L. et al. (eds.): Artificial Intelligence and Soft Computing, ICAISC 2008, LNCS 5097, pp. 1007-1017, 2008. pdf
        2. Dudek G.: Feature subset selection using genetic algorithm with roulette wheel and tournament mutations. Proc. 4-th PD Forum-Conference on Computer Science, 2008.

2007

        1. Dudek G.: A Genetic Algorithm with a Roulette Wheel Mutation. Informatyka Teoretyczna i Stosowana (Theoretical and Applied Computer Science), r. 7, no. 12, pp. 91-107, 2007
        2. Dudek G.: The Analysis of the Short-Term Power System Load Forecasting Model based on the Fuzzy Clustering. Badania Operacyjne i Decyzje (Operation Research and Decisions), no. 2, pp. 15-34, 2007 (in Polish).
        3. Dudek G.: Genetic Algorithm with Integer Representation of Unit Start-Up And Shut-Down Times for the Unit Commitment Problem. European Transactions on Electrical Power 17, pp. 500-511, 2007. pdf
        4. Dudek G.: Naive Methods for Forecasting the Power System Load and Energy Prices. Proc. XIII Conf. Present-Day Problems of Power Engineering, vol. 3, pp. 43-50, 2007 (in Polish).

2006

        1. Dudek G.: Short-Term Power System Load Forecasting using Fuzzy Clustering Method. Przegląd Elektrotechniczny (Electrical Review), r. 82, no. 9, pp. 26-28, 2006 (in Polish).
        2. Dudek G.: Application of the Hierarchical Clustering Methods to the Daily Electrical Load Profile Forecasting. Przegląd Elektrotechniczny (Electrical Review), r. 82, no. 9, pp. 9-11, 2006 (in Polish).
        3. Dudek G.: Data Preprocessing in Similarity-Based Methods of Short-Term Load Forecasting. Przegląd Elektrotechniczny (Electrical Review), r. 82, no. 9, pp. 15-19, 2006 (in Polish).
        4. Dudek G.: Unit Commitment Using a Hybrid Method Combining Simulated Annealing and Genetic Algorithm. Archiwum Energetyki (Archives of Energetics), vol. XXXV, no. 1, pp. 3- 28, 2006 (in Polish).

2005

        1. Dudek G.: Localizations of the Power Substations using an Evolution Strategy. Proc. Conf. Computer Methods and Systems in Automatics and Electrical Engineering, pp. 107-110, 2005 (in Polish).

2004

        1. Dudek G.: Unit Commitment by Genetic Algorithm with Specialized Search Operators. Electric Power Systems Research 72, pp. 299-308, 2004. pdf
        2. Dudek G.: Selected Methods of Analysis of the Electrical Load Time Series. Proc. Conf. Forecasting in Power Engineering, pp. 116-125, 2004 (in Polish).
        3. Dudek G.: Regression Tree as a Forecasting Tool. Proc. Conf. Forecasting in Power Engineering, pp. 99-105, 2004 (in Polish).
        4. Dudek G.: Complex and Sequential Evolutionary Algorithms for Unit Commitment and Economic Power Dispatch. Proc. 7-th Conf. Evolutionary Computation and Global Optimization, pp. 33-46, 2004.

2003

        1. Dudek G.: Usefulness of GMDH Network for Short-Term Load Forecasting. Proc. Conf. Computer Methods and Systems in Automatics and Electrical Engineering, vol. 1, pp. 136-138, 2003 (in Polish).

2002

        1. Dudek G.: Complex Evolutionary Algorithm to Economic Dispatch. Proc. Conf. Forecasting in Power Engineering, pp. 225-234, 2002 (in Polish).

2001

        1. Dudek G.: Construction of Characteristics of the Start-up Costs of Power Units. Proc. Conf. Computer Methods and Systems in Automatics and Electrical Engineering, vol. 1, pp. 87-89, 2001 (in Polish).
        2. Dudek G.: Weighted Feature Selection in Minimal Distance Classification. Proc. Conf. Computer Methods and Systems in Automatics and Electrical Engineering, vol. 1, pp. 49-51, 2001 (in Polish).
        3. Dudek G.: Variable Representation Methods and Genetic Operators in Evolutionary Algorithm for Unit Commitment. Proc. Conf. Computer Methods and Systems in Automatics and Electrical Engineering, vol. 1, pp. 93-95, 2001 (in Polish).
        4. Dudek G.: Analysis of the Chaos Theory-based Methods for Long-Term Load Forecasting - Schuster method. Proc. Conf. Computer Methods and Systems in Automatics and Electrical Engineering, vol. 1, pp. 90-92, 2001 (in Polish).
        5. Dudek G.: Forecasting of the Daily Load Curves using RBF network. Proc. X Conf. Present-Day Problems of Power Engineering, vol. 3, pp. 93-100, 2001 (in Polish).

2000

        1. Dudek G.: Unit Commitment using Simulated Annealing. Proc. Conf. Forecasting in Power Engineering, pp. 299-309, 2000 (in Polish).
        2. Dudek G.: Short-Term Load Forecasting using RBF Networks. Proc. Conf. Forecasting in Power Engineering, pp. 59-68, 2000 (in Polish).
        3. Dudek G.: Short-Term Load Forecasting using Neural Networks – Architecture Selection and Learning Problems. Proc. 5-th Conf. Evolutionary Computation and Global Optimization, pp. 59-66, 2000 (in Polish).
        4. Dudek G.: Genetic Algorithm as a Unit Commitment Method. Proc. Conf. Evolutionary Computation and Global Optimization, pp. 51- 58, 2000 (in Polish).

1999

        1. Dudek G.: Economic Dispatch in a Power Station using Genetic Algorithm. Proc. Conf. Optimization in Power Engineering, pp. 9-18, 1999 (in Polish).
        2. Dudek G., Engiel J.: A Simple Neural Network Simulator for Education. Proc. Conf. Computer Methods and Systems in Automatics and Electrical Engineering, vol. 2, pp. 379-381, 1999 (in Polish).
        3. Dudek G.: A Genetic Algorithm as a Unit Commitment Tool for Thermal Units. Proc. Conf. Computer Methods and Systems in Automatics and Electrical Engineering, vol. 2, pp. 218-220, 1999 (in Polish).
        4. Dudek G.: Short-Term Load Forecasting Methodology using Neural networks. Proc. Conf. Computer Methods and Systems in Automatics and Electrical Engineering, vol. 2, pp. 214-217, 1999 (in Polish).
        5. Dudek G.: Selection of Diagnostic Parameters using Simulated Annealing. Proc. Conf. Computer Methods and Systems in Automatics and Electrical Engineering, vol. 1, pp. 67-69, 1999 (in Polish).
        6. Dudek G.: Genetic Algorithm for Symptom Selection in Diagnostic Tests. Proc. Conf. Evolutionary Computation and Global Optimization, pp. 99-106, 1999 (in Polish).

1998

        1. Dudek G.: Neural Network Classifiers for Fault Detection of the Power Station Devices. Proc. Conf. Modeling of Power Station Exploitation, pp. 107-118, 1998 (in Polish).

1997

        1. Topór-Kamiński L., Dudek G.: Learning of Layered Neural Networks by Voting Rule. Proc. III Conf. Neural Networks and Their Applications, pp. 131-135, 1997 (in Polish).
        2. Topór-Kamiński L., Dudek G.: A Current Conveyor with Opened Current Mirrors. Proc. III Conf. Neural Networks and Their Applications, pp. 85-87, 1997 (in Polish).
        3. Dudek G.: Hecht-Nielsen Neural Network for Short-Term Load Forecasting. Proc. VIII Conf. Present-Day Problems of Power Engineering, vol. 4, pp. 65-72, 1997 (in Polish).
        4. Dobrzańska I., Dudek G., Seweryn P.: A Method of Analysis of the Economic Situation of Regional Energy Providers. Proc. VIII Conf. Present-Day Problems of Power Engineering, vol. 5, pp. 93-100, 1997 (in Polish).

1995

        1. Dudek G., Seweryn P.: Determination of the Optimal Value of the Contracted Capacity for Minimum Annual Tariff Costs. Proc. Conf. Future of Power Engineering -- Trends, Directions, Methods, pp.205-212, 1995 (in Polish).
  1. STLF data