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Krzysztof Gajowniczek

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Associate Professor

Warsaw University of Life Sciences

Poland

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About

Krzysztof Gajowniczek is an Assistant Professor at Warsaw University of Life Sciences in Poland. His research focuses on time-series classification, power management optimization, and decision tree learning, as evidenced by his recent publications on simulation studies, electricity tariff analysis, and novel R packages for imbalanced datasets. He aims to apply deep learning approaches to enhance energy management solutions for commercial customers.

Recent Grants

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Forecasting of electricity usage using smart metering systems

Open Date: 2017-02-01

Close Date: 2018-08-01

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Collaborators (5)

Arkadiusz Orłowski

POLAND

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Weronika Radziszewska

Institute of Fluid Flow Machinery, Polish Academy of Sciences

POLAND

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Marcin Bator

Warsaw University of Life Sciences

POLAND

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Jörg Verstraete

Institute of Fluid Flow Machinery, Polish Academy of Sciences

POLAND

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Jacek Brożyna

assistant professor

Rzeszów University

POLAND

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Krzysztof Gajowniczek

Research Interests

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About

Recent Grants

Articles (19)

A Comparative Study of Time Series Clustering Performance with Classification as a Benchmark

Automatic Assessment of Tool Wear Condition During Milling of Laminated MDF Board Using the EVA-02 Classifier

Data-Driven Peak Demand Identification in Commercial Electricity Consumption for Load Curve Flattening

Multimodal Utility Data for Appliance Recognition: A Case Study with Rule-Based Algorithms

Simulation Study on How Input Data Affects Time-Series Classification Model Results

Matching Electricity Footprint of Commercial Customers to Industry-Specific Profiles for Enhanced Power Management

Influence of Explanatory Variable Distributions on the Behavior of the Impurity Measures Used in Classification Tree Learning

Cluster-Based Approach to Estimate Demand in the Polish Power System Using Commercial Customers’ Data

Changing Electricity Tariff—An Empirical Analysis Based on Commercial Customers’ Data from Poland

HOFS: Higher order mutual information approximation for feature selection in R

Deep Learning-Based Approaches to Optimize the Electricity Contract Capacity Problem for Commercial Customers

Deep Learning-Based Approaches to Optimize the Electricity Contract Capacity Problem for Commercial Customers

ImbTreeAUC: An R package for building classification trees using the area under the ROC curve (AUC) on imbalanced datasets

ImbTreeEntropy and ImbTreeAUC: Novel R Packages for Decision Tree Learning on the Imbalanced Datasets

ImbTreeEntropy: An R package for building entropy-based classification trees on imbalanced datasets

Interactive Decision Tree Learning and Decision Rule Extraction Based on the ImbTreeEntropy and ImbTreeAUC Packages

Whole Time Series Data Streams Clustering: Dynamic Profiling of the Electricity Consumption

Whole Time Series Data Streams Clustering: Dynamic Profiling of the Electricity Consumption

Simulation Study on the Electricity Data Streams Time Series Clustering

Collaborators (5)

Arkadiusz Orłowski

Weronika Radziszewska

Marcin Bator

Jörg Verstraete

Jacek Brożyna

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