FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2114035071> ?p ?o ?g. }

• W2114035071 abstract "` Abstract— This paper presents artificial neural networks and particle swarm optimization (ANN-PSO) based short-term load forecasting model with improved generalization technique for the Regional Power Control Center of Saudi Electricity Company, Western Operation Area (SEC-WOA) of Saudi Arabia. Weather, load demand, wind speed, wind direction, heat, sunlight, etc. are quite different in a desert land than other places. Thus this model is different from a typical forecasting model considering inputs and outputs. In this research, two models are implemented - firstly load forecasting model for prediction; however, it is not sufficient for desired level of accurate forecasting, and secondly, optimization to improve the results up to at least better than existing results. This paper includes ANN and PSO models for 24-hours ahead load forecasting. ANN is a mathematical tool for mapping complex relations; it is well proved for the successful use of prediction, function approximation with dynamics, categorization, classification, and so forth. In this research, 24- step ahead calculations are performed in the ANN model and results are moderate. On the other hand, PSO is the most promising optimization tool. It is a swarmed based optimization method; it has better information sharing and conveying mechanism; it has better balance of local and global searching abilities; it can handle huge multi-dimensional optimization problems efficiently with hundreds of thousands of constraints. Thus PSO is chosen as the optimization tool that is applied on the weight matrix of ANN to improve results. In this research, PSO reliably and accurately tracks the continuously changing weights of ANN for uncertain load demand. By analyzing the model of ANN for the load-forecasting problem of SEC-WOA with hundreds of thousands of data and changing-uncertain load demand, the PSO is applied for the ANN weight adjustment and to optimize the uncertain load demand, as the ANN is not an optimization method. Results show that the proposed ANN-PSO performs much better than ANN alone for the load forecasting in a desert like Saudi Arabia." @default.
• W2114035071 created "2016-06-24" @default.
• W2114035071 creator A5057011237 @default.
• W2114035071 creator A5058690742 @default.
• W2114035071 date "2009-11-01" @default.
• W2114035071 modified "2023-09-27" @default.
• W2114035071 title "Load Forecasting of a Desert: a Computational Intelligence Approach" @default.
• W2114035071 cites W1984051156 @default.
• W2114035071 cites W1992178840 @default.
• W2114035071 cites W1992778383 @default.
• W2114035071 cites W1997294204 @default.
• W2114035071 cites W2010448349 @default.
• W2114035071 cites W2020416411 @default.
• W2114035071 cites W2026241078 @default.
• W2114035071 cites W2035622873 @default.
• W2114035071 cites W2036959914 @default.
• W2114035071 cites W2076769467 @default.
• W2114035071 cites W2100108878 @default.
• W2114035071 cites W2105916576 @default.
• W2114035071 cites W2121955751 @default.
• W2114035071 cites W2151767444 @default.
• W2114035071 cites W2152195021 @default.
• W2114035071 cites W2152375058 @default.
• W2114035071 cites W2168138569 @default.
• W2114035071 cites W2171216686 @default.
• W2114035071 cites W2279232293 @default.
• W2114035071 cites W2402846924 @default.
• W2114035071 cites W3011460294 @default.
• W2114035071 cites W3123622325 @default.
• W2114035071 cites W2462703089 @default.
• W2114035071 doi "https://doi.org/10.1109/isap.2009.5352850" @default.
• W2114035071 hasPublicationYear "2009" @default.
• W2114035071 type Work @default.
• W2114035071 sameAs 2114035071 @default.
• W2114035071 citedByCount "5" @default.
• W2114035071 countsByYear W21140350712016 @default.
• W2114035071 countsByYear W21140350712017 @default.
• W2114035071 countsByYear W21140350712018 @default.
• W2114035071 crossrefType "proceedings-article" @default.
• W2114035071 hasAuthorship W2114035071A5057011237 @default.
• W2114035071 hasAuthorship W2114035071A5058690742 @default.
• W2114035071 hasConcept C119857082 @default.
• W2114035071 hasConcept C124101348 @default.
• W2114035071 hasConcept C126255220 @default.
• W2114035071 hasConcept C127413603 @default.
• W2114035071 hasConcept C134306372 @default.
• W2114035071 hasConcept C139502532 @default.
• W2114035071 hasConcept C154945302 @default.
• W2114035071 hasConcept C177148314 @default.
• W2114035071 hasConcept C193809577 @default.
• W2114035071 hasConcept C33923547 @default.
• W2114035071 hasConcept C41008148 @default.
• W2114035071 hasConcept C42475967 @default.
• W2114035071 hasConcept C50644808 @default.
• W2114035071 hasConcept C85617194 @default.
• W2114035071 hasConceptScore W2114035071C119857082 @default.
• W2114035071 hasConceptScore W2114035071C124101348 @default.
• W2114035071 hasConceptScore W2114035071C126255220 @default.
• W2114035071 hasConceptScore W2114035071C127413603 @default.
• W2114035071 hasConceptScore W2114035071C134306372 @default.
• W2114035071 hasConceptScore W2114035071C139502532 @default.
• W2114035071 hasConceptScore W2114035071C154945302 @default.
• W2114035071 hasConceptScore W2114035071C177148314 @default.
• W2114035071 hasConceptScore W2114035071C193809577 @default.
• W2114035071 hasConceptScore W2114035071C33923547 @default.
• W2114035071 hasConceptScore W2114035071C41008148 @default.
• W2114035071 hasConceptScore W2114035071C42475967 @default.
• W2114035071 hasConceptScore W2114035071C50644808 @default.
• W2114035071 hasConceptScore W2114035071C85617194 @default.
• W2114035071 hasLocation W21140350711 @default.
• W2114035071 hasOpenAccess W2114035071 @default.
• W2114035071 hasPrimaryLocation W21140350711 @default.
• W2114035071 hasRelatedWork W1513154819 @default.
• W2114035071 hasRelatedWork W160156565 @default.
• W2114035071 hasRelatedWork W1970817965 @default.
• W2114035071 hasRelatedWork W2029514205 @default.
• W2114035071 hasRelatedWork W2092275282 @default.
• W2114035071 hasRelatedWork W2112201824 @default.
• W2114035071 hasRelatedWork W2112641490 @default.
• W2114035071 hasRelatedWork W2117915583 @default.
• W2114035071 hasRelatedWork W2125603750 @default.
• W2114035071 hasRelatedWork W2130342757 @default.
• W2114035071 hasRelatedWork W2153541934 @default.
• W2114035071 hasRelatedWork W2168205713 @default.
• W2114035071 hasRelatedWork W2548067624 @default.
• W2114035071 hasRelatedWork W2964803388 @default.
• W2114035071 hasRelatedWork W3001781903 @default.
• W2114035071 hasRelatedWork W3022392400 @default.
• W2114035071 hasRelatedWork W3194328959 @default.
• W2114035071 hasRelatedWork W3195305740 @default.
• W2114035071 hasRelatedWork W2310231560 @default.
• W2114035071 hasRelatedWork W2965332139 @default.
• W2114035071 isParatext "false" @default.
• W2114035071 isRetracted "false" @default.
• W2114035071 magId "2114035071" @default.
• W2114035071 workType "article" @default.