On the short term forecasting of heat power for heating of building
Artykuł w czasopiśmie
MNiSW
140
Lista 2021
| Status: | |
| Autorzy: | Cholewa Tomasz, Siuta-Olcha Alicja, Smolarz Andrzej, Muryjas Piotr, Wolszczak Piotr, Guz Łukasz, Balaras Constantinos A. |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2021 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Wolumen/Tom: | 307 |
| Numer artykułu: | 127232 |
| Strony: | 1 - 7 |
| Impact Factor: | 11,072 |
| Web of Science® Times Cited: | 22 |
| Scopus® Cytowania: | 22 |
| Bazy: | Web of Science | Scopus |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Witryna wydawcy |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 1 maja 2021 |
| Abstrakty: | angielski |
| The energy efficiency of existing buildings may be increased by using new control techniques of their heating systems, especially if such methods are validated and easy to install. Hence, short-term forecasting of heat power demand is needed, in order to optimize their operation. This work presents a simple, new method of short-term forecasting of heat power for space heating, which may be easily applied in existing buildings. The method is first presented and then validated with two case studies, a multifamily building and a school, using hourly data from three heating seasons. It was found that beyond the outdoor meteorological parameters the accuracy of the method is improved by including the equivalent indoor temperature as the parameter related to the effect of the building occupant behavior. Accordingly, the resulting mean absolute percentage error of the predicted heat demand using the proposed prediction method was 3.2% and 12.0% for the two buildings. Compared to a simple model of the heat power demand that is based only on the outdoor temperature error was lower by 61.4% and 43.2% for two buildings respectively. In addition, five profiles of equivalent indoor temperature were proposed in order to select the most accurate one for a specific building. This method may be also used in the process of predictive control of heating systems, because the external and internal parameters are measurable and predictable, which will contribute to more energy efficient systems in existing and new buildings. |
