Probabilistic　forecasts　provide　quantitative　information　in　relation　to　energy　uncertainty,　which　is　essential　for　making　better　decisions　on　the　operation　of　power　systems　with　an　increasing　penetration　of　wind　power.　On　the　basis　of　the　k-nearest　neighbors　algorithm　and　a　kernel　density　estimator　method,　this　paper　presents　a　general　framework　for　the　probabilistic　forecasting　of　renewable　energy　generation,　especially　for　wind　power　generation.　It　is　a　direct　and　non-parametric　approach.　Firstly,　the　k-nearest　neighbors　algorithm　is　used　to　find　the　k　closest　historical　examples　with　characteristics　similar　to　the　future　weather　condition　of　wind　power　generation.　Secondly,　a　novel　kernel　density　estimator　based　on　a　logarithmic　transformation　and　a　boundary　kernel　is　used　to　construct　wind　power　predictive　density　based　on　the　k　closest　historical　examples.　The　effectiveness　of　this　approach　has　been　confirmed　on　the　real　data　provided　for　GEFCom2014.　The　evaluation　results　show　that　the　proposed　approach　can　provide　good　quality,　reliable　probabilistic　wind　power　forecasts.　(C)　2015　International　Institute　of　Forecasters.　Published　by　Elsevier　B.V.　All　rights　reserved.