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Pregled bibliografske jedinice broj: 947281

Association rule mining based quantitative analysis approach of household characteristics impacts on residential electricity consumption patterns


Wang, Fei; Li, Kangping; Duić, Neven; Mi, Zegqiang; Hodge, Bri-Mathias, Shafie-khak, Miadreza; Catalão, Joãoa
Association rule mining based quantitative analysis approach of household characteristics impacts on residential electricity consumption patterns // Energy conversion and management, 171 (2018), 839-854 doi:10.1016/j.enconman.2018.06.017 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 947281 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Association rule mining based quantitative analysis approach of household characteristics impacts on residential electricity consumption patterns

Autori
Wang, Fei ; Li, Kangping ; Duić, Neven ; Mi, Zegqiang ; Hodge, Bri-Mathias, Shafie-khak, Miadreza ; Catalão, Joãoa

Izvornik
Energy conversion and management (0196-8904) 171 (2018); 839-854

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Electricity consumption pattern ; Household characteristics ; Association rule mining ; Clustering ; Apriori algorithm

Sažetak
The comprehensive understanding of the residential electricity consumption patterns (ECPs) and how they relate to household characteristics can contribute to energy efficiency improvement and electricity consumption reduction in the residential sector. After recognizing the limitations of current studies (i.e. unreasonable typical ECP (TECP) extraction method and the problem of multicollinearity and interpretability for regression and machine learning models), this paper proposes an association rule mining based quantitative analysis approach of household characteristics impact on residential ECPs trying to address them together. First, an adaptive density-based spatial clustering of applications with noise (DBSCAN) algorithm is utilized to create seasonal TECP of each individual customer only for weekdays. K-means is then adopted to group all the TECPs into several clusters. An enhanced Apriori algorithm is proposed to reveal the relationships between TECPs and thirty five factors covering four categories of household characteristics including dwelling characteristics, socio- demographic, appliances and heating and attitudes towards energy. Results of the case study using 3326 records containing smart metering data and survey information in Ireland suggest that socio-demographic and cooking related factors such as employment status, occupants and whether cook by electricity have strong significant associations with TECPs, while attitudes related factors almost have no effect on TECPs. The results also indicate that those households with more than one person are more likely to change ECP across seasons. The proposed approach and the findings of this study can help to support decisions about how to reduce electricity consumption and CO2 emissions

Izvorni jezik
Engleski

Znanstvena područja
Strojarstvo



POVEZANOST RADA


Projekti:
120-1201918-1920 - Racionalno skladištenje energije za održivi razvoj energetike (Duić, Neven, MZOS ) ( POIROT)

Ustanove:
Fakultet strojarstva i brodogradnje, Zagreb

Profili:

Avatar Url Neven Duić (autor)

Poveznice na cjeloviti tekst rada:

doi

Citiraj ovu publikaciju:

Wang, Fei; Li, Kangping; Duić, Neven; Mi, Zegqiang; Hodge, Bri-Mathias, Shafie-khak, Miadreza; Catalão, Joãoa
Association rule mining based quantitative analysis approach of household characteristics impacts on residential electricity consumption patterns // Energy conversion and management, 171 (2018), 839-854 doi:10.1016/j.enconman.2018.06.017 (međunarodna recenzija, članak, znanstveni)
Wang, F., Li, K., Duić, N., Mi, Z., Hodge, Bri-Mathias, Shafie-khak, Miadreza & Catalão, J. (2018) Association rule mining based quantitative analysis approach of household characteristics impacts on residential electricity consumption patterns. Energy conversion and management, 171, 839-854 doi:10.1016/j.enconman.2018.06.017.
@article{article, year = {2018}, pages = {839-854}, DOI = {10.1016/j.enconman.2018.06.017}, keywords = {Electricity consumption pattern, Household characteristics, Association rule mining, Clustering, Apriori algorithm}, journal = {Energy conversion and management}, doi = {10.1016/j.enconman.2018.06.017}, volume = {171}, issn = {0196-8904}, title = {Association rule mining based quantitative analysis approach of household characteristics impacts on residential electricity consumption patterns}, keyword = {Electricity consumption pattern, Household characteristics, Association rule mining, Clustering, Apriori algorithm} }
@article{article, year = {2018}, pages = {839-854}, DOI = {10.1016/j.enconman.2018.06.017}, keywords = {Electricity consumption pattern, Household characteristics, Association rule mining, Clustering, Apriori algorithm}, journal = {Energy conversion and management}, doi = {10.1016/j.enconman.2018.06.017}, volume = {171}, issn = {0196-8904}, title = {Association rule mining based quantitative analysis approach of household characteristics impacts on residential electricity consumption patterns}, keyword = {Electricity consumption pattern, Household characteristics, Association rule mining, Clustering, Apriori algorithm} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


Citati:





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