Integrating Technologies for Demand Response in Blocks of Buildings - A UK Case Study

Crosbie, Tracey; Broderick, John; Dawood, Muneeb; Charlesworth, Richard; Vuković, Vladimir; Short, Michael; Dawood, Nashwan

doi:10.24928/jc3-2017/0225

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The biggest challenge in evaluating the effectiveness of a demand response (DR) event is to properly determine what would be the real energy consumption in the absence of the event. Hence, to calculate the key performance indicators (KPI’s) [ 54 ] for DR events taking place within the four Technical University of Cluj-Napoca (TUCN) blocks of buildings, it was impetuously necessary to identify a baseline consumption level for each day when demand response events took place. The starting point in determining the baseline reference consumption was a database composed of hourly electricity consumption values, starting 2014 until fall of 2019 for each block of buildings within TUCN, included in the pilot project.…”

Section: Applied Methodsmentioning

confidence: 99%

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

Jurj

Czumbil

Bargauan

et al. 2021

Sensors

View full text Add to dashboard Cite

The aim of this paper is to provide an extended analysis of the outlier detection, using probabilistic and AI techniques, applied in a demo pilot demand response in blocks of buildings project, based on real experiments and energy data collection with detected anomalies. A numerical algorithm was created to differentiate between natural energy peaks and outliers, so as to first apply a data cleaning. Then, a calculation of the impact in the energy baseline for the demand response computation was implemented, with improved precision, as related to other referenced methods and to the original data processing. For the demo pilot project implemented in the Technical University of Cluj-Napoca block of buildings, without the energy baseline data cleaning, in some cases it was impossible to compute the established key performance indicators (peak power reduction, energy savings, cost savings, CO2 emissions reduction) or the resulted values were far much higher (>50%) and not realistic. Therefore, in real case business models, it is crucial to use outlier’s removal. In the past years, both companies and academic communities pulled their efforts in generating input that consist in new abstractions, interfaces, approaches for scalability, and crowdsourcing techniques. Quantitative and qualitative methods were created with the scope of error reduction and were covered in multiple surveys and overviews to cope with outlier detection.

show abstract

Section: Applied Methodsmentioning

confidence: 99%