Â Heat demand prediction is a notable research topic in intelligent energy networks (IENs), due to the rapid growth of heat demand in cities. Given that hourly heat demand data can be considered as a time series data recording and well analyzed by time series seasonal decomposition algorithms, we develop a variant of the recurrent neural network (RNN), namely time frequency-domain memory (TFDM). The TFDM combines fast Fourier transform (FFT) and long short-term memory (LSTM) model to preserve memory of the series in both time and frequency domains, and cascades a residual block to introduce the impact factors (e.g., weathers). In the experiments, we compare the proposed TFDM with various referred methods on a heat demand dataset. The experimental results show that the proposed TFDM has significant performance improvement in the heat demand prediction.
Keywords Intelligent energy networks(IENs), heat demand prediction, time series prediction, time-frequency domain analysis, long short-term memory(LSTM)