Probabilistic　wind　power　forecasting　not　only　produces　the　expectation　of　wind　power　output,　but　also　gives　quantitative　information　on　the　associated　uncertainty,　which　is　essential　for　making　better　decisions　about　power　system　and　market　operations　with　the　increasing　penetration　of　wind　power　generation.　This　paper　presents　a　novel　kernel　density　estimator　for　probabilistic　wind　power　forecasting,　addressing　two　characteristics　of　wind　power　which　have　adverse　impacts　on　the　forecast　accuracy,　namely,　the　heavily　skewed　and　double-bounded　nature　of　wind　power　density.　Logarithmic　transformation　is　used　to　reduce　the　skewness　of　wind　power　density,　which　improves　the　effectiveness　of　the　kernel　density　estimator　in　a　transformed　scale.　Transformations　partially　relieve　the　boundary　effect　problem　of　the　kernel　density　estimator　caused　by　the　double-bounded　nature　of　wind　power　density.　However,　the　case　study　shows　that　there　are　still　some　serious　problems　of　density　leakage　after　the　transformation.　In　order　to　solve　this　problem　in　the　transformed　scale,　a　boundary　kernel　method　is　employed　to　eliminate　the　density　leak　at　the　bounds　of　wind　power　distribution.　The　improvement　of　the　proposed　method　over　the　standard　kernel　density　estimator　is　demonstrated　by　short-term　probabilistic　forecasting　results　based　on　the　data　from　an　actual　wind　farm.　Then,　a　detailed　comparison　is　carried　out　of　the　proposed　method　and　some　existing　probabilistic　forecasting　methods.　(C)　2015　Elsevier　Ltd.　All　rights　reserved.