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Moreover, the evolvement of power grids into smart grids where the end users continuously participate in the power market by forming energy prices and\/or by adjusting their energy needs according to their own agenda, adds high volatility to load demand. In that sense, with regard to predictive methods, a plain single point prediction application may not be enough. The aim of this study is to develop and evaluate a method in order to further enhance this type of applications by providing Predictive Intervals (PIs) regarding ampacity overloading in smart power systems through the use of Artificial Neural Networks (ANNs).<\/p>","DOI":"10.4018\/ijmstr.2016070101","type":"journal-article","created":{"date-parts":[[2017,2,7]],"date-time":"2017-02-07T10:50:36Z","timestamp":1486464636000},"page":"1-20","source":"Crossref","is-referenced-by-count":5,"title":["Backpropagation Neural Network for Interval Prediction of Three-Phase Ampacity Level in Power Systems"],"prefix":"10.4018","volume":"4","author":[{"given":"Rafik","family":"Fainti","sequence":"first","affiliation":[{"name":"Purdue University, West Lafayette, IN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miltiadis","family":"Alamaniotis","sequence":"additional","affiliation":[{"name":"Nuclear Engineering Program, University of Utah, Salt Lake City, UT, USA & Applied Intelligent Systems Laboratory, School of Nuclear Engineering, Purdue University, West Lafayette, IN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lefteri H.","family":"Tsoukalas","sequence":"additional","affiliation":[{"name":"School of Nuclear Engineering, Purdue University, West Lafayette, IN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJMSTR.2016070101-0","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2012.04.022"},{"key":"IJMSTR.2016070101-1","doi-asserted-by":"crossref","unstructured":"Chassin, D. 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