Electricity Consumption Prediction Using XGBoost Based on Discrete Wavelet Transform



The purpose of this paper is to predict the daily electricity consumption of the next month. It is considerably important for people to cope with the problem well. Although few articles mentions the topic of electricity consumption prediction, numerous papers include some topic similar to the topic in this paper, such as rainfall forecasting, wind speed prediction and water flow forecasting. Moreover, a number of techniques and algorithms are employed to cope with those issues and achieve outstanding performance. Those techniques and algorithms are considerably remarkable, but the accuracy of them is not excellent enough on the long-term prediction of time series. In this paper, we propose a hybrid model which integrate discrete wavelet transform and XGBoost to forecast the electricity consumption time series data on the long-term prediction, namely DWT-XGBoost. The original time series data can decompose into approximate time series data and detail time series data by the discrete wavelet transform. And those time series data by decomposition are as features input into the prediction model that is XGBoost. Furthermore, the parameters of XGBoost are obtained by a grid search method. The performance of the proposed model in this paper is measured against with other hybrid models such as integrating discrete wavelet transform and support vector regression, integrating discrete wavelet transform and artificial neural networks, and unitary XGBoost. The comparison results show that the DWT-XGBoost outperforms other models and is a novel method on the long-term prediction of time series.


Wavelet Transform, Discrete Wavelet Transform (DWT), XGBoost, DWTXGBoost, Extracting Features



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