Improving Building Temperature Forecasting: A Data-driven Approach with System Scenario Clustering

Dafang Zhao*, Zheng Chen, Zhengmao Li, Xiaolei Yuan, Ittetsu Taniguchi

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

1 Sitaatiot (Scopus)
6 Lataukset (Pure)

Abstrakti

Heat, Ventilation and Air Conditioning (HVAC) systems play a critical role in maintaining a comfortable thermal environment and cost approximately 40% of primary energy usage in the building sector. For smart energy management in buildings, usage patterns and their resulting profiles allow the improvement of control systems with prediction capabilities. However, for large-scale HVAC system management, it is difficult to construct a detailed model for each subsystem. In this paper, a new data-driven room temperature prediction model is proposed based on the k-means clustering method. The proposed data-driven temperature prediction approach extracts the system operation feature through historical data analysis and further simplifies the system-level model to improve generalization and computational efficiency. We evaluate the proposed approach in the real world. The results demonstrated that our approach can significantly reduce modeling time without reducing prediction accuracy.

AlkuperäiskieliEnglanti
Otsikko2024 IEEE Power and Energy Society General Meeting, PESGM 2024
KustantajaIEEE
Sivumäärä5
ISBN (elektroninen)979-8-3503-8183-2
DOI - pysyväislinkit
TilaJulkaistu - 2024
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE Power and Energy Society General Meeting - Seattle, Yhdysvallat
Kesto: 21 heinäk. 202425 heinäk. 2024

Julkaisusarja

NimiIEEE Power and Energy Society General Meeting
ISSN (painettu)1944-9925
ISSN (elektroninen)1944-9933

Conference

ConferenceIEEE Power and Energy Society General Meeting
LyhennettäPESGM
Maa/AlueYhdysvallat
KaupunkiSeattle
Ajanjakso21/07/202425/07/2024

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