Demand response on blocks of buildings — Romanian pilot site innovation project

Bargauan, Bogdan; Fati, Ovidiu; Ceclan, Andrei; Micu, Dan D.; Şteţ, Denisa; Czumbil, Levente; Muresan, Paul

doi:10.1109/mps.2017.7974433

Cited by 9 publications

(7 citation statements)

References 4 publications

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“…Partly the problem was due to the lack of consideration of the context of the building during the event time such as different baseline values (i.e., the amount of the electricity that a building usually consumes under the same condition without DR operation), weather condition, and event time. These and similar examples from the literature [30] show that calculating saving potentials in DR programs simply using average benchmarking values from generic design specifications and not taking into account the context around event times result in a loss for building owners in either way. Hence, facility operators and third aggregators are in need of tools to accurately estimate the true potential of buildings by relying on integrated information including protocols, historical energy performance data, and contextual data around the event times (e.g., baseline values corresponding to the event times, weather data, etc.).…”

Section: Introductionmentioning

confidence: 61%

“…DR protocols are instructional statements for building operators to follow to operate major equipment in that building. For buildings which participated in DR program earlier, a yearly assessment is conducted by DR engineers and they will simply increase or decrease the DR enrollment by comparing the average performance on savings for events that happened in that year with the previous DR enrollments [30].…”

Section: Introductionmentioning

confidence: 99%

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A Data-driven Framework to Estimate Saving Potential of Buildings in Demand Response Events

Ergan²

2018

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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In the U.S., the increasing electricity demand gives pressure on the power grids because of its limited capacity to serve demand. Instead of building new power plants to meet the increasing demand, Demand Response (DR) programs incentivize end-consumers to reduce certain electricity demand during certain periods (e.g., peak demand and emergency times). In the current practice, saving potential of buildings, i.e., the amount of electricity that end-consumers can save during an event, is usually determined using the technical specifications of equipment installed, which is unrealistic and leads to over or underestimation of the expected saving potential. In this study, the authors developed a data-driven framework to quantify the electricity saving potential in buildings. The framework was applied to nineteen campus buildings. Several prediction algorithms were used to fit models to the integrated datasets of these buildings, and models were evaluated using four criteria to avoid over-fitting and under-fitting. The best performance of the models resulting in 0.86 of R 2 , which represents high capability to quantify the electricity saving potential. The contribution of this study is the proposed data-driven framework, which provides facility operators with reliable tools to accurately quantify saving potential of buildings. The conducted case study using the framework on 19 test buildings showed that facility operators could avoid unnecessary penalties by eliminating them to sign up for unrealistic targets, and help them to gain the most value out of the DR programs by knowing the true potential of their buildings.

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Section: Introductionmentioning

confidence: 61%

Section: Introductionmentioning

confidence: 99%

A Data-driven Framework to Estimate Saving Potential of Buildings in Demand Response Events

Ergan²

2018

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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“…The related daily consumptions in (kWh) and the absorbed power in (kW) are presented. Also, the total or aggregate electricity consumption is presented [ 51 ] for the four pilot groups of buildings: The block of buildings from the Faculty of Electrical Engineering; The block of buildings from the MarastiStudents Campus; The Faculty of Building Services; The swimming pool complex. …”

Section: Overview Of the Implemented Demand Response Systemmentioning

confidence: 99%

mentioning

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

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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.

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“…In the process of finalizing DR-BoB "Demand Response in Blocks of Buildings" project funded by the EU Horizon 2020 innovation program under grant agreement No. 696114/2016 [46,47] data was collected from Technical University of Cluj-Napoca's (TUCN) buildings in order to develop an energy monitoring tool and targeting system with a Demand Response curve control strategy. In the process of gathering the electrical consumption data from the swimming complex of the Technical University of Cluj-Napoca, there were detected inconsistent data (see Fig.…”

Section: A Proposed Outlier Detection Techniquesmentioning

confidence: 99%

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

Jurj

Micu

Czumbil

et al. 2020

2020 55th International Universities Power Engineering Conference (UPEC)

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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.

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Demand response on blocks of buildings — Romanian pilot site innovation project

Cited by 9 publications

References 4 publications

A Data-driven Framework to Estimate Saving Potential of Buildings in Demand Response Events

A Data-driven Framework to Estimate Saving Potential of Buildings in Demand Response Events

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

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

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