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Ing. Marek Lóderer
Identification number: 64391
University e-mail: marek_loderer [at] stuba.sk
 
Výskumný pracovník s VŠ vzdelaním - Institute of Informatics, Information Systems and Software Engineering (FIIT)
 
2533V05  Intelligent Information Systems D-IIS
FIIT D-IIS den [interrupted]
Doctoral type of study, full-time, attendance method form
interrupted study

Contacts     Graduate     Lesson     Final thesis     
Projects     Publications     Supervised theses     

Basic information

Basic information about a final thesis

Type of thesis: Diploma thesis
Thesis title:Prediction of electricity consumption using biologically inspired algorithms
Written by (author): Ing. Peter Halaš
Department: Institute of Informatics, Information Systems and Software Engineering (FIIT)
Thesis supervisor: Ing. Marek Lóderer
Opponent:Ing. Petra Vrablecová
Final thesis progress:Final thesis was successfully defended.


Additional information

Additional information about the final thesis follows. Click on the language link to display the information in the desired language.

Language of final thesis:Slovak

Slovak        English

Title of the thesis:Prediction of electricity consumption using biologically inspired algorithms
Summary:Prediction of electricity consumption is very investigated field of research. The introduction of smart meters, which are part of a smart metering system, opened access to data on energy consumption. These data open up the possibility to apply various predictive models to reduce prediction error. In the energy market, there are several participants. By lowering prediction error, we can lower costs of suppliers. Suppliers must estimate the amount of electricity being consumed by their customers. The classical approach contains only one prediction model, which is then trained and used to predict the future consumption. This approach has been proved insufficient. The problem is that none of the models is sufficiently robust to accurately estimate the development of the time series. Ensemble model is trying to create the best possible prediction by applying multiple models. Ensemble model consists of three steps. In this work, we have focused particularly on the last step, which combines a number of prediction models. To solve the combination problem, we are applying swarm intelligent algorithms, which are designed for this type of problem. The aim of this study is to compare the accuracy of the prediction of the ensemble model, when applying conventional approaches in the process of combination prediction models, to the use of swarm intelligent algorithms. Comparison experiments will be carried out on three datasets from foreign countries.
Key words:ensemble model, swarm intelligent algorithms, energetics

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