Short-term multi-energy load forecasting of integrated energy systems based on temporal convolutional network and multi-task learning

Abstract

Accurate forecasting of electrical, heating, and cooling demands is crucial for the optimal design, operation, and coordinated planning of multi-energy coupling systems. In this study, we present a short-term multi-energy load forecasting method for integrated energy systems based on a temporal convolutional network and multi-task learning. First, the Pearson correlation coefficient was used to analyze the correlations among meteorological variables. Then, the maximum overlap discrete wavelet transform (MODWT) module decomposed the input time-series data into components at different frequency levels. These decomposed time series datasets were input into the multi-task learning model for sparse sharing. Subsequently, the learned time series datasets of different frequencies were input into the temporal convolutional network for temporal scale feature extraction. To improve prediction accuracy, the improved energy valley optimizer (IEVO) was introduced to optimize the model parameters. The feature vectors output by the optimized temporal convolutional network were then fed into the corresponding Transformer models for adaptive weight adjustment. Finally, the MODWT module reconstructed the predictions at different frequency levels to obtain the final multi-energy load forecasting results.

Keywords

integrated energy systems / multi-energy load forecasting / multi-task learning / temporal convolutional network / improved energy valley optimizer

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