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Multi-scale Time sequence Neural Net for electricity consumption forecasting

Accurate electricity consumption forecasting can be treated as a reliable guidance for power production and power supply. While electricity consumption data shows the approximate periodicity and the instantaneous fluctuation, it is difficult for traditional electricity consumption forecasting models to simultaneously capture the periodicity and the fluctuation. For effectively predicting electricity consumption, this paper proposes a Multi-scale time sequence neural network (MSTNN), which regards the electricity consumption data as combinations of approximate periodic components and fluctuation components. To capture the periodicity of the data, the MSTNN decomposes the power consumption data using the ensemble empirical mode decomposition (EEMD) method to obtain intrinsic mode functions (IMFs) in different time scales, and then, attention LSTMs with different scales are used to predict the IMF components. The experimental results show that compared with the commonly used electricity prediction models, the MSTNN captures the characteristics of electricity data and improves the accuracy of electricity prediction.