Abstract

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.

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