Algorithm for Increasing Accuracy of Solar Power Forecasting When Applying Nearest Neighbor Method
DOI:
https://doi.org/10.20535/RADAP.2024.95.39-46Keywords:
nearest neighbor method, power forecasting, clustering, solar energy, solar irradiation, regressionAbstract
This paper presents the prediction of the output power of a solar power plant based on the nearest neighbor regression method. Data from a 50 MW solar power plant in China with solar radiation, ambient temperature, pressure, and humidity values and corresponding output power values from 01/01/2019 to 12/31/2020 with 15-minute intervals were used to create the output power forecast. Before using the data, they were cleaned of outliers using the standard interquartile range method, data points were divided into test and training groups, and feature scaling was applied using the standardization method to correctly calculate the Euclidean distance between data points. The application of clustering of weather parameters by the k-means method is proposed, which allows for individual selection of the number of neighbors for each cluster and to exclude the influence of points of one cluster on the number of neighbors of another cluster. The number of clusters is selected by determining the silhouette coefficient, the training group of weather parameters with their corresponding output power values is distributed among clusters based on the Euclidean distance to the centroid of the clusters. The test group of weather parameters is divided into clusters, after which forecasting by the method of nearest neighbors takes place within each cluster separately. The output power of the solar power plant is calculated as the weighted arithmetic average of the neighbors of each point of the test group. At the end of the algorithm, the sequence of points of weather parameters of the test group is restored and a time series of the output power forecast is created. The proposed algorithm made it possible to reduce the MSE, RMSE, MAPE, MAE forecast errors for 1 day by 0.5348, 0.2265, 0.38%, 0.1448, respectively, for 7 days, the errors became smaller by 0.1992, 0.0384, 0.1%, 0.0193, respectively. As a result, the relative error in forecasting for 24 hours is 4.22%.
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