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http://dx.doi.org/10.9717/kmms.2016.19.8.1530

Use of the Moving Average of the Current Weather Data for the Solar Power Generation Amount Prediction  

Lee, Hyunjin (Dept. of Computer Science & Software, Korea Soongsil Cyber University)
Publication Information
Journal of Korea Multimedia Society / v.19, no.8, 2016 , pp. 1530-1537 More about this Journal
Abstract
Recently, solar power generation shows the significant growth in the renewable energy field. Using the short-term prediction, it is possible to control the electric power demand and the power generation plan of the auxiliary device. However, a short-term prediction can be used when you know the weather forecast. If it is not possible to use the weather forecast information because of disconnection of network at the island and the mountains or for security reasons, the accuracy of prediction is not good. Therefore, in this paper, we proposed a system capable of short-term prediction of solar power generation amount by using only the weather information that has been collected by oneself. We used temperature, humidity and insolation as weather information. We have applied a moving average to each information because they had a characteristic of time series. It was composed of min, max and average of each information, differences of mutual information and gradient of it. An artificial neural network, SVM and RBF Network model was used for the prediction algorithm and they were combined by Ensemble method. The results of this suggest that using a moving average during pre-processing and ensemble prediction models will maximize prediction accuracy.
Keywords
Prediction of Solar Power Generation; Neural Networks; Moving Average; Ensemble;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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