Analysis of Data Cleaning Techniques for Electrical Energy Consumption of a Public Building
| Autor: | Dan D. Micu, Mircea Lancrajan, Alexandru G. Berciu, Levente Czumbil, Dacian I. Jurj, Denisa M. Barar |
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| Rok vydání: | 2020 |
| Předmět: |
Process (engineering)
Computer science 020206 networking & telecommunications 02 engineering and technology Energy consumption Industrial engineering Field (computer science) Work (electrical) Order (exchange) 020204 information systems Outlier 0202 electrical engineering electronic engineering information engineering Data analysis Anomaly detection Data Cleaning Electricity Consumption Outliers LOF IQR Clusters Median Public Buildings Cleaning Methods Artificial Intelligence Machine Learning Database |
| Zdroj: | 2020 55th International Universities Power Engineering Conference (UPEC) |
| DOI: | 10.1109/upec49904.2020.9209781 |
| Popis: | Statistical Techniques and Artificial Intelligence are becoming much more a necessity in a fastened world rather than just a theoretical use case. In order to satisfy this need, the optimization process starts with data collecting and cleaning. The aim of this paper is to provide a short overview of the outlier detection methods and to explain the need for data cleaning in the field of energy consumption by analyzing the energetic profile data from the Technical University of Cluj-Napoca’s swimming complex. In the first and second parts of the article, a short overview of cleaning methods are presented. The third part compares the efficiency of the proposed methods. Finally, but not least the fourth part of the article is dedicated to conclusions and future work. |
| Databáze: | OpenAIRE |
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