Featuring Periodic Correlations Via Dual Granularity Inputs Structuredrnnsensemble Load Forecaster

With the development of Energy Internet and the widespread use of smart meters, the application of user-side data has been attracting more and more attention, one of which is residential short-term load forecasting (SLTF). The ability of traditional prediction methods is limited due to a large amount of fine-grained data with high uncertainty, and many researchers have applied deep learning to SLTF thereby. However, perspectives to study and develop deep neural network forecasters are yet to be freed from time domain. Frequency domain analysis provides a sufficient path to assess the periodic characteristics of load. By insights from the spectrum of load data, significant periodic fluctuation characteristics of load are revealed. Based on this, a dual time granularity inputs structured RNNs ensemble STLF model is proposed, which unites two independent input-featured RNNs to learn more time-frequency characteristics implied in fine-grained load. One RNN is mainly responsible for mining time-domain features of load, the other focuses on learning frequency-domain features. Case study indicates a positive correlation between capturing more spectrum characteristics and performing a better prediction over accepted criteria, which has been achieved by the proposed model. In addition, experiments show that the proposed forecaster can improve both individual household and aggregated load STLF accuracy.