Data-driven demand flexibility estimation in a commercial buildings from air conditioning and lighting system
Visualitza/Obre
MB_VR_RV_Data driven demand flexibility estimation in a commercial building from air conditioning and lighting system_Barbero etal 2020.pdf (280,1Kb) (Accés restringit)
Sol·licita una còpia a l'autor
Què és aquest botó?
Aquest botó permet demanar una còpia d'un document restringit a l'autor. Es mostra quan:
- Disposem del correu electrònic de l'autor
- El document té una mida inferior a 20 Mb
- Es tracta d'un document d'accés restringit per decisió de l'autor o d'un document d'accés restringit per política de l'editorial
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/352859
Tipus de documentText en actes de congrés
Data publicació2021
Condicions d'accésAccés restringit per política de l'editorial
Tots els drets reservats. Aquesta obra està protegida pels drets de propietat intel·lectual i
industrial corresponents. Sense perjudici de les exempcions legals existents, queda prohibida la seva
reproducció, distribució, comunicació pública o transformació sense l'autorització del titular dels drets
Abstract
Renewable and distributed energy sources, with photovoltaic in the first place, are leading the energy transition. However, they alone will hardly find the way to success in this challenge because of their intrinsic limitations, such as their dependency on weather condition and the unpredictability of their production. Demand Response services are considered crucial for the integration of renewable sources in the grid. This study presents a methodology to evaluate potential flexibility in commercial buildings by managing air conditioning and lighting systems of the site. The study proposes two different data-driven approaches to evaluate flexibility depending on the technology considered. For lighting systems, flexibility is evaluated using the XGBoost classification algorithm combined with a piecewise function. The thermal behavior of the building is modeled with a simplified RC equivalent state-space model. For both algorithms, preprocessing showed to be a critical part of the process, which is explained stage-by-stage in the paper. Results show that the lighting flexibility algorithm is very reliable, reaching an average error of less than 1 % during the months considered. The simplified thermal modeling proposed shows promising results, having less than 0.2 o C of RMSE error for the internal temperature forecast. In addition, results show the flexibility potentiality of the building, which is potentially able to shift up to 23 % of its power consumption during certain hours of the day. This opens huge opportunities for taking advantage from dynamic tariffs, increase self-consumption from renewable sources or participate in flexibility markets to reduce buildings' electricity bill and help renewable integration.
CitacióBarbero, M. [et al.]. Data-driven demand flexibility estimation in a commercial buildings from air conditioning and lighting system. A: World Sustainable Energy Days. "World Sustainable Energy Days 2021 (WSED): 23rd European Photovoltaic Solar Energy Conference: Wels, Austria: June 21-25, 2021: proceedings". 2021, p. 1-7.
Fitxers | Descripció | Mida | Format | Visualitza |
---|---|---|---|---|
MB_VR_RV_Data d ... stem_Barbero etal 2020.pdf | 280,1Kb | Accés restringit |