| Abstract:Aiming at the problem that abnormal values of mutation, invalidation and so on in electric power big data analysis results in the difficulty to mine the true law, this paper proposes a big data analysis model based on improved GRU. The model first analyzes the effects of abnormal values leading to data redundancy, errors, etc., and use adaptive threshold wavelet filtering to eliminate the above effects. Then, the data is segmented with a period into several data segments. The memory summation at the same time points, and the average value obtained by the summation result is used as the standard memory. Finally, the GRU memory capacity is improved according to the quality of the data segment, that is, the memory of good quality data segment is retained, and the data segment memory of poor quality is deleted. In order to verify the performance of the model, experiments were performed on photovoltaic power generation data. The results show that the prediction accuracy of this model when the data quality is high is 61%, 30% and 25% higher than that of ARIMA, LSTM and standard GRU respectively. The data quality The prediction accuracy rates of poorer forecasts increased by 76%, 16%, and 11%, respectively. |
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