Demand response of space heating using model predictive control in an educational office building

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

Standard

Demand response of space heating using model predictive control in an educational office building. / Mäkinen, Aleksi; Jokisalo, Juha; Kosonen, Risto.

CLIMA 2019 Conference, 26th-29th May, 2019, Bucharest, Romania. EDP SCIENCES, 2019. 03067 (E3S Web of Conferences; Vuosikerta 111).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

Harvard

Mäkinen, A, Jokisalo, J & Kosonen, R 2019, Demand response of space heating using model predictive control in an educational office building. julkaisussa CLIMA 2019 Conference, 26th-29th May, 2019, Bucharest, Romania., 03067, E3S Web of Conferences, Vuosikerta. 111, EDP SCIENCES, Bucharest, Romania, 26/05/2019. https://doi.org/10.1051/e3sconf/201911103067

APA

Mäkinen, A., Jokisalo, J., & Kosonen, R. (2019). Demand response of space heating using model predictive control in an educational office building. teoksessa CLIMA 2019 Conference, 26th-29th May, 2019, Bucharest, Romania [03067] (E3S Web of Conferences; Vuosikerta 111). EDP SCIENCES. https://doi.org/10.1051/e3sconf/201911103067

Vancouver

Mäkinen A, Jokisalo J, Kosonen R. Demand response of space heating using model predictive control in an educational office building. julkaisussa CLIMA 2019 Conference, 26th-29th May, 2019, Bucharest, Romania. EDP SCIENCES. 2019. 03067. (E3S Web of Conferences). https://doi.org/10.1051/e3sconf/201911103067

Author

Mäkinen, Aleksi ; Jokisalo, Juha ; Kosonen, Risto. / Demand response of space heating using model predictive control in an educational office building. CLIMA 2019 Conference, 26th-29th May, 2019, Bucharest, Romania. EDP SCIENCES, 2019. (E3S Web of Conferences).

Bibtex - Lataa

@inproceedings{19f713eb06514185a13d4298873956ff,
title = "Demand response of space heating using model predictive control in an educational office building",
abstract = "The building sector plays a remarkable role in decreasing of the overall global CO2 emissions since as much as 30{\%} from the total global CO2 emission are generated in buildings. Demand response provides one possibility to tackle the problem. It can be used to decrease CO2 emissions in entire energy system in addition to providing energy cost savings for building owners and energy companies. In this study, the demand response potential was estimated in an educational office building that was heated by district heating. The potential was defined in respect of energy cost savings, energy flexibility and thermal comfort. Model predictive control was developed, which utilized the dynamic hourly district heating prices. The MPC algorithm written in the Matlab software, predicted the future heating demand while the optimization algorithm NSGA-II minimized the heating energy cost, maximized the energy flexibility and maintained acceptable thermal comfort by changing the space heating temperature setpoints. The operation of the MPC algorithm was tested in the IDA ICE 4.8 simulation software. As a result, the annual district heating energy costs could be reduced by 4.2{\%} compared to the reference case with constant space heating temperature setpoint of 21 °C. The maximum flexibility factor attained was 14{\%}. Acceptable level of thermal comfort was maintained throughout the simulation time.",
author = "Aleksi M{\"a}kinen and Juha Jokisalo and Risto Kosonen",
year = "2019",
month = "8",
day = "13",
doi = "10.1051/e3sconf/201911103067",
language = "English",
series = "E3S Web of Conferences",
publisher = "EDP SCIENCES",
booktitle = "CLIMA 2019 Conference, 26th-29th May, 2019, Bucharest, Romania",
address = "France",

}

RIS - Lataa

TY - GEN

T1 - Demand response of space heating using model predictive control in an educational office building

AU - Mäkinen, Aleksi

AU - Jokisalo, Juha

AU - Kosonen, Risto

PY - 2019/8/13

Y1 - 2019/8/13

N2 - The building sector plays a remarkable role in decreasing of the overall global CO2 emissions since as much as 30% from the total global CO2 emission are generated in buildings. Demand response provides one possibility to tackle the problem. It can be used to decrease CO2 emissions in entire energy system in addition to providing energy cost savings for building owners and energy companies. In this study, the demand response potential was estimated in an educational office building that was heated by district heating. The potential was defined in respect of energy cost savings, energy flexibility and thermal comfort. Model predictive control was developed, which utilized the dynamic hourly district heating prices. The MPC algorithm written in the Matlab software, predicted the future heating demand while the optimization algorithm NSGA-II minimized the heating energy cost, maximized the energy flexibility and maintained acceptable thermal comfort by changing the space heating temperature setpoints. The operation of the MPC algorithm was tested in the IDA ICE 4.8 simulation software. As a result, the annual district heating energy costs could be reduced by 4.2% compared to the reference case with constant space heating temperature setpoint of 21 °C. The maximum flexibility factor attained was 14%. Acceptable level of thermal comfort was maintained throughout the simulation time.

AB - The building sector plays a remarkable role in decreasing of the overall global CO2 emissions since as much as 30% from the total global CO2 emission are generated in buildings. Demand response provides one possibility to tackle the problem. It can be used to decrease CO2 emissions in entire energy system in addition to providing energy cost savings for building owners and energy companies. In this study, the demand response potential was estimated in an educational office building that was heated by district heating. The potential was defined in respect of energy cost savings, energy flexibility and thermal comfort. Model predictive control was developed, which utilized the dynamic hourly district heating prices. The MPC algorithm written in the Matlab software, predicted the future heating demand while the optimization algorithm NSGA-II minimized the heating energy cost, maximized the energy flexibility and maintained acceptable thermal comfort by changing the space heating temperature setpoints. The operation of the MPC algorithm was tested in the IDA ICE 4.8 simulation software. As a result, the annual district heating energy costs could be reduced by 4.2% compared to the reference case with constant space heating temperature setpoint of 21 °C. The maximum flexibility factor attained was 14%. Acceptable level of thermal comfort was maintained throughout the simulation time.

U2 - 10.1051/e3sconf/201911103067

DO - 10.1051/e3sconf/201911103067

M3 - Conference contribution

T3 - E3S Web of Conferences

BT - CLIMA 2019 Conference, 26th-29th May, 2019, Bucharest, Romania

PB - EDP SCIENCES

ER -

ID: 35800811