Probabilistic　forecasting　provides　quantitative　information　of　energy　uncertainty,　which　is　very　essential　for　making　better　decisions　in　power　system　operation　with　increasing　penetration　of　wind　power　and　solar　power.　On　the　basis　of　k-nearest　neighbor　and　kernel　density　estimator　method,　this　paper　presents　a　general　framework　of　probabilistic　forecasts　for　renewable　energy　generation.　Firstly,　the　k-nearest　neighbor　algorithm　is　modified　to　find　the　days　with　similar　weather　conditions　in　historical　dataset.　Then,　kernel　density　estimator　method　is　applied　to　derive　the　probability　density　from　k　nearest　neighbors.　This　approach　is　demonstrated　by　an　application　in　probabilistic　solar　power　forecasting.　The　effectiveness　of　our　proposed　approach　is　validated　with　the　real　data　provided　by　Global　Energy　Forecasting　Competition　2014.