Analysis of Data Cleaning Techniques for Electrical Energy Consumption of a Public Building

Abstract

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.

Custom Outlier Detection for Electrical Energy Consumption Data Applied in Case of Demand Response in Block of Buildings

“…The measured data had a sampling frequency of 1 h. One of the main objectives of the demand response pilot project was to implement a system for monitoring and remote management of electricity consumption in each involved group of buildings. The monitoring [ 50 ] and remote management system has a relatively simple architecture, monitoring only a small number of consumers in each location, therefore the equipment is considered to have a proportional impact on the project. This objective could be achieved only after identifying all the electricity consumers, the operating regimes and establishing their importance according to their energy consumption.…”