IEEE International Conference on Fuzzy Systems (FUZZ) 2023
Materiały konferencyjne (red.)
ISBN: 978-8-3503-3228-5
| Status: | |
| Redakcja: | Rhee Frank Chung-Hoon, Choi Byung-Jae |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2023 |
| Serie: |
IEEE International Conference on Fuzzy Systems [FUZZ-IEEE], ISSN (print): 1544-5615, ISSN (on-line): 1558-4739
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| Wersja dokumentu: | Elektroniczna |
| Język: | angielski |
| Liczba stron: | online resource |
| Miejsce wydania: | Piscataway |
| Wydawnictwo: | Institute of Electrical and Electronics Engineers (IEEE) |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | TAK |
| Nazwa konferencji: | 2023 IEEE International Conference on Fuzzy Systems |
| Skrócona nazwa konferencji: | FUZZ - IEEE 2023 |
| URL serii konferencji: | LINK |
| Termin konferencji: | 13 sierpnia 2023 do 17 sierpnia 2023 |
| Miasto konferencji: | Incheon |
| Państwo konferencji: | KOREA POŁUDNIOWA |
| Publikacja OA: | NIE |
| Abstrakty: | angielski |
| The problem of proper management of electricity consumption in the era of high energy prices is a very im- portant issue. In the case of large enterprises that consume significant a mounts o f e nergy, t here i s a n a dditional p roblem of correctly determining the tariff to be contracted. Development of an efficient e nergy c onsumption m anagement s ystem based on the use of additional energy sources from photovoltaic panels requires taking into account an effective system that allows to predict the level of insolation in the following hours. The paper presents an innovative model for forecasting the level of insolation based on historical data. Due to the constantly changing climatic conditions, a two-level of fuzzyfication w as a pplied. A t t hefirst level, the level of membership to various descriptors describing the length of a given day was determined. On the second level of fuzzyfication, b ased o n h istorical d ata, t he d egree of membership to classes representing different levels of sunshine at a given hour was determined. As part of numerical experiments, selected machine learning tools were used to predict the degree of membership to particular classes based on appropriately selected historical data. The obtained results confirm t he very high prediction accuracy of the innovative solution that allows to determine the degree of insolation. |
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