Kankanala, PadmavathyDas, SanjoyPahwa, Anil2014-03-272014-03-272013-09-24http://hdl.handle.net/2097/17272Environmental factors, such as weather, trees, and animals, are major causes of power outages in electric utility distribution systems. Of these factors, wind and lightning have the most significant impacts. The objective of this paper is to investigate models to estimate wind and lighting related outages. Such estimation models hold the potential for lowering operational costs and reducing customer downtime. This paper proposes an ensemble learning approach based on a boosting algorithm, AdaBoost+, for estimation of weather-caused power outages. Effectiveness of the model is evaluated using actual data, which comprised of weather data and recorded outages for four cities of different sizes in Kansas. The proposed ensemble model is compared with previously presented regression, neural network, and mixture of experts models. The results clearly show that AdaBoost+ estimates outages with greater accuracy than the other models for all four data sets.en-USThis Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).Artificial intelligenceEnsemble learningEnvironmental factorsPower distribution systemsPower system reliabilityText