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Krzysztof Gajowniczek
Krzysztof Gajowniczek
Institute of Information Technology, Warsaw University of Life Sciences
Zweryfikowany adres z sggw.pl - Strona główna
Tytuł
Cytowane przez
Cytowane przez
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Short term electricity forecasting using individual smart meter data
K Gajowniczek, T Ząbkowski
Procedia Computer Science 35, 589-597, 2014
1192014
Electricity forecasting on the individual household level enhanced based on activity patterns
K Gajowniczek, T Ząbkowski
PLoS ONE 12 (4), e0174098, 2017
922017
Data Mining Techniques for Detecting Household Characteristics Based on Smart Meter Data
K Gajowniczek, T Ząbkowski
Energies 8 (7), 7407-7427, 2015
782015
Artifical Intelligence and Soft Computing: 10th International Conference, ICAISC 2010, Zakopane, Poland, June 13-17, 2010, Part II
L Rutkowski, R Scherer, R Tadeusiewicz, LA Zadeh, JM Zurada
Springer Berlin Heidelberg, 2010
442010
Two-stage electricity demand modelling using machine learning algorithms
K Gajowniczek, T Ząbkowski
Energies 10 (10), 1547-1571, 2017
412017
Simulation study on clustering approaches for short-term electricity forecasting
K Gajowniczek, T Ząbkowski
Complexity 2018, 2018
302018
Electricity peak demand classification with artificial neural networks
K Gajowniczek, R Nafkha, T Ząbkowski
Proceedings of the 2017 Federated Conference on Computer Science and …, 2017
222017
Comparison of Decision Trees with Rényi and Tsallis Entropy Applied for Imbalanced Churn Dataset
K Gajowniczek, A Orłowski, Z Tomasz
Annals of Computer Science and Information Systems 5, 39 – 44, 2015
212015
Revealing Household Characteristics from Electricity Meter Data with Grade Analysis and Machine Learning Algorithms
K Gajowniczek, T Ząbkowski, M Sodenkamp
Applied Sciences 8 (9), art. 1654, 2018
202018
Entropy Based Trees to Support Decision Making for Customer Churn Management
K Gajowniczek, A Orłowski, T Ząbkowski
Acta Physica Polonica A 129 (5), 971-979, 2016
192016
Short term electricity forecasting based on user behavior using individual smart meter data
K Gajowniczek, T Ząbkowski
Intelligent & Fuzzy Systems, 2015
19*2015
Do customers choose proper tariff? empirical analysis based on polish data using unsupervised techniques
R Nafkha, K Gajowniczek, T Ząbkowski
Energies 11 (3), 514, 2018
182018
ESTIMATING THE ROC CURVE AND ITS SIGNIFICANCE FOR CLASSIFICATION MODELS’ASSESSMENT
K Gajowniczek, T Ząbkowski, R Szupiluk
Metody Ilościowe w Badaniach Ekonomicznych 15 (2), 382-391, 2014
182014
Weighted Random Forests to Improve Arrhythmia Classification
K Gajowniczek, I Grzegorczyk, T Ząbkowski, C Bajaj
Electronics 9 (1), art. 99, 2020
172020
Grade analysis for energy usage patterns segmentation based on smart meter data
T Ząbkowski, K Gajowniczek, R Szupiluk
2015 IEEE 2nd International Conference on Cybernetics (CYBCONF), 234-239, 2015
152015
Simulation study on the application of the generalized entropy concept in artificial neural networks
K Gajowniczek, A Orłowski, T Ząbkowski
Entropy 20 (4), 249, 2018
132018
Artificial Neural Networks and Machine Learning–ICANN 2017: 26th International Conference on Artificial Neural Networks, Alghero, Italy, September 11-14, 2017, Proceedings, Part II
A Lintas, S Rovetta, PFMJ Verschure, AEP Villa
Springer, 2017
132017
Q-Entropy Approach to Selecting High Income Households.
K Gajowniczek, K Karpio, P Łukasiewicz, A Orłowski, T Ząbkowski
Acta Physica Polonica, A. 127, 2015
132015
Smart metering and data privacy issues
T Ząbkowski, K Gajowniczek
Information systems in Management 2 (3), 239--249, 2013
132013
Reducing False Arrhythmia Alarms Using Different Methods of Probability and Class Assignment in Random Forest Learning Methods
K Gajowniczek, I Grzegorczyk, T Ząbkowski
Sensors 19 (7), art. 1588, 2019
102019
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