Publikacje

Publikacje

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).