Matches in SemOpenAlex for { <https://semopenalex.org/work/W4385688712> ?p ?o ?g. }
Showing items 1 to 72 of
72
with 100 items per page.
- W4385688712 abstract "In low-voltage smart grids, data are received from advanced measurement systems (smart meters). These data play a fundamental role in monitoring and control. However, as these data are being received in wide snapshot sample times, real-time monitoring is a huge challenge. The next time-step data forecasting seems to be a promising solution to improve the monitoring, observability, and controllability of low-voltage smart grids. The existing fluctuations in current and voltage profiles due to the stochastic behavior of each household lead to imprecise short- term forecasting results. However, for the preventive control method, precise forecasting is not necessary. In this paper, the neural network method is used to forecast the next time-step data. Input and output data for the neural network are based on three different strategies. According to the simulation results, the accuracy of forecasting such volatile data is low. Thus, the neural network method is combined with the pseudo-worst-case forecast method. In our previous publication, this method was introduced to forecast worst-case scenarios instead of exact values. The pseudo-worst-case forecast method is a heuristic algorithm that can be modified based on operator knowledge and grid behavior. To obtain an almost stand-alone method able to adjust itself without operator support, the pseudo-worst-case forecast method is improved by the neural network method. The paper presents this combined heuristic method and validates it through simulations done in MATLAB software using data from real low-voltage grids located in Germany. The proposed method's success rate in forecasting the probable worst-case scenario is more than 98%." @default.
- W4385688712 created "2023-08-10" @default.
- W4385688712 creator A5027229492 @default.
- W4385688712 creator A5088019652 @default.
- W4385688712 creator A5092794054 @default.
- W4385688712 date "2023-06-25" @default.
- W4385688712 modified "2023-09-27" @default.
- W4385688712 title "Pseudo-Worst-Case Forecast with Neural Networks in Low Voltage Grids" @default.
- W4385688712 cites W1970676140 @default.
- W4385688712 cites W2002225679 @default.
- W4385688712 cites W2090742183 @default.
- W4385688712 cites W2151767444 @default.
- W4385688712 cites W2268418619 @default.
- W4385688712 cites W2315506216 @default.
- W4385688712 cites W2549148602 @default.
- W4385688712 cites W2736645571 @default.
- W4385688712 cites W2743306721 @default.
- W4385688712 cites W2943926435 @default.
- W4385688712 cites W2979430336 @default.
- W4385688712 cites W3019765971 @default.
- W4385688712 doi "https://doi.org/10.1109/powertech55446.2023.10202768" @default.
- W4385688712 hasPublicationYear "2023" @default.
- W4385688712 type Work @default.
- W4385688712 citedByCount "0" @default.
- W4385688712 crossrefType "proceedings-article" @default.
- W4385688712 hasAuthorship W4385688712A5027229492 @default.
- W4385688712 hasAuthorship W4385688712A5088019652 @default.
- W4385688712 hasAuthorship W4385688712A5092794054 @default.
- W4385688712 hasConcept C10558101 @default.
- W4385688712 hasConcept C119599485 @default.
- W4385688712 hasConcept C124101348 @default.
- W4385688712 hasConcept C127413603 @default.
- W4385688712 hasConcept C154945302 @default.
- W4385688712 hasConcept C165801399 @default.
- W4385688712 hasConcept C173801870 @default.
- W4385688712 hasConcept C28826006 @default.
- W4385688712 hasConcept C33923547 @default.
- W4385688712 hasConcept C36299963 @default.
- W4385688712 hasConcept C41008148 @default.
- W4385688712 hasConcept C48209547 @default.
- W4385688712 hasConcept C50644808 @default.
- W4385688712 hasConcept C79403827 @default.
- W4385688712 hasConceptScore W4385688712C10558101 @default.
- W4385688712 hasConceptScore W4385688712C119599485 @default.
- W4385688712 hasConceptScore W4385688712C124101348 @default.
- W4385688712 hasConceptScore W4385688712C127413603 @default.
- W4385688712 hasConceptScore W4385688712C154945302 @default.
- W4385688712 hasConceptScore W4385688712C165801399 @default.
- W4385688712 hasConceptScore W4385688712C173801870 @default.
- W4385688712 hasConceptScore W4385688712C28826006 @default.
- W4385688712 hasConceptScore W4385688712C33923547 @default.
- W4385688712 hasConceptScore W4385688712C36299963 @default.
- W4385688712 hasConceptScore W4385688712C41008148 @default.
- W4385688712 hasConceptScore W4385688712C48209547 @default.
- W4385688712 hasConceptScore W4385688712C50644808 @default.
- W4385688712 hasConceptScore W4385688712C79403827 @default.
- W4385688712 hasLocation W43856887121 @default.
- W4385688712 hasOpenAccess W4385688712 @default.
- W4385688712 hasPrimaryLocation W43856887121 @default.
- W4385688712 hasRelatedWork W1983142522 @default.
- W4385688712 hasRelatedWork W1986570998 @default.
- W4385688712 hasRelatedWork W2037921533 @default.
- W4385688712 hasRelatedWork W2068126039 @default.
- W4385688712 hasRelatedWork W2083680097 @default.
- W4385688712 hasRelatedWork W2332386680 @default.
- W4385688712 hasRelatedWork W2378889616 @default.
- W4385688712 hasRelatedWork W2508171592 @default.
- W4385688712 hasRelatedWork W2782529250 @default.
- W4385688712 hasRelatedWork W2801563517 @default.
- W4385688712 isParatext "false" @default.
- W4385688712 isRetracted "false" @default.
- W4385688712 workType "article" @default.