Multi-memory Enhanced Separation Network for Indoor Temperature Prediction.

Indoor temperature prediction is vital to predictive control on district heating systems. Due to the data collection in practice, there always exist residential areas with limited historical data. Transferring the knowledge from residential areas with sufficient data is of great help to address the data scarcity problem. However, it is still challenging as the data distribution shifts among residential areas and shifts over time. In this paper, we proposed a Multi-Memory enhanced Separation Network (MMeSN) to predict indoor temperature for residential areas with limited data. MMeSN is a parameter-based multi-source transfer learning method, mainly consisting of two components: Source Knowledge Memorization and Memory-enhenced Aggregation. Specifically, the former component jointly decouples the domain-independent & domain-specific information which separately memorize the specific historical patterns for each source. The latter component memorizes the historical relationships between the target and multiple sources and further aggregates the domain-specific & domain-independent information. We conduct extensive experiments on a real-world dataset, and the results demonstrate the advantages of our approach.