This study combined the Kmeans clustering algorithm with the traditional K-nearest neighbor (KNN) algorithm and proposed a Kmeans-KNN fusion algorithm suitable for energy self-sustaining indoor visible light positioning (VLP) systems. This algorithm considered both low complexity and high precision. Based on using the Kmeans clustering algorithm to divide the specially designed fingerprint library to achieve coarse positioning, the KNN algorithm was used for precise positioning. This study further introduced the proposed Kmeans-KNN fusion algorithm into an energy self-sustaining VLP system and analyzed the positioning performance of the system under different conditions. The results show that compared with the traditional KNN algorithm, the Kmeans-KNN fusion algorithm's positioning accuracy is significantly improved; the average positioning error of the system is 0.141 m. In addition, the calculation amount of the proposed algorithm is reduced by 94.7%. Therefore, the system energy consumption is significantly reduced, which is conducive to the realization of high-precision energy self-sustaining of the VLP system.