Fuzzy-based weighting long short-term memory network for demand forecasting
One of the main challenges in short-term electrical load forecasting is extraction of nonlinear relationships and complex dependencies among different time instances of the load time series. To deal with this difficulty, a hybrid forecasting method is proposed in this paper that uses the fuzzy exper...
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| Vydané v: | The Journal of supercomputing Ročník 79; číslo 1; s. 435 - 460 |
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| Médium: | Journal Article |
| Jazyk: | English |
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New York
Springer US
01.01.2023
Springer Nature B.V |
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| ISSN: | 0920-8542, 1573-0484 |
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| Abstract | One of the main challenges in short-term electrical load forecasting is extraction of nonlinear relationships and complex dependencies among different time instances of the load time series. To deal with this difficulty, a hybrid forecasting method is proposed in this paper that uses the fuzzy expert systems and deep learning methods. In the first step, dependency of previous time instances to the next instance to be load forecasted is achieved through a fuzzy system with 125 rules. Then, the obtained weights are used beside the actual load values as the input of a long short-term memory network for load forecasting. The obtained results on two popular datasets show the superior performance of the proposed method in terms of various evaluation measures. |
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| AbstractList | One of the main challenges in short-term electrical load forecasting is extraction of nonlinear relationships and complex dependencies among different time instances of the load time series. To deal with this difficulty, a hybrid forecasting method is proposed in this paper that uses the fuzzy expert systems and deep learning methods. In the first step, dependency of previous time instances to the next instance to be load forecasted is achieved through a fuzzy system with 125 rules. Then, the obtained weights are used beside the actual load values as the input of a long short-term memory network for load forecasting. The obtained results on two popular datasets show the superior performance of the proposed method in terms of various evaluation measures. |
| Author | Imani, Maryam |
| Author_xml | – sequence: 1 givenname: Maryam orcidid: 0000-0002-1924-9776 surname: Imani fullname: Imani, Maryam email: maryam.imani@modares.ac.ir organization: Faculty of Electrical and Computer Engineering, Tarbiat Modares University |
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| CitedBy_id | crossref_primary_10_1016_j_epsr_2024_110534 crossref_primary_10_1007_s44444_025_00012_y crossref_primary_10_1007_s11227_023_05193_4 |
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| SubjectTerms | Artificial intelligence Compilers Computer Science Deep learning Electrical loads Expert systems Forecasting Genetic algorithms Interpreters Mathematics Methods Neural networks Power Processor Architectures Programming Languages Time series Wavelet transforms |
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