An accurate short-term electric load forecasting is critical for modern electric power systems' safe and economical operation. Electric load forecasting can be formulated as a multi-variate time series problem. Residential houses in the same neighborhood may be affected by similar factors and share some latent spatial dependencies. However, most of the existing works on electric load forecasting fail to explore such dependencies. In recent years, graph neural networks (GNNs) have shown impressive success in modeling such dependencies. However, such GNN based models usually would require a large amount of training data. We may have a minimal amount of data available to train a reliable forecasting model for houses in a new neighborhood area. At the same time, we may have a large amount of historical data collected from other houses that can be leveraged to improve the new neighborhood's prediction performance. In this paper, we propose an attentive transfer learning-based GNN model that can utilize the learned prior knowledge to improve the learning process in a new area. The transfer process is achieved by an attention network, which generically avoids negative transfer by leveraging knowledge from multiple sources. Extensive experiments have been conducted on real-world data sets. Results have shown that the proposed framework can consistently outperform baseline models in different areas.

Plug-in Hybrid Electric Vehicles (PHEVs) are becoming increasingly popular all over the world. Therefore, some associated public infrastructures will have to be established, such as smart parking zones, charging stations, and charging piles, etc. This paper only focuses on the smart charging station. A comprehensive analysis upon the impacts of electricity price on the return of investment (ROI) of the charging station with a coordinating charging is presented. First, a coordinated charging and discharging model is presented which considers the profits of all three partners, namely the utility, the charging station and the customers. Second, the bidirectional money flow model of the station with both utility and customer are presented as well. Third, the hierarchical optimization model is introduced to solve the Mixed Integer Linear Programming problems. Fourth, a simulation based on a real database of a typical city is run and the Bass' diffusion model is applied to forecast the amount of PHEVs arriving at the charging station per day. Lastly, it is proved that an optimal coordinating charging strategy will reduce the years of ROI significantly and a figure presenting the relationship between price and ROI years is given.

In building energy management area, many studies have been done on energy saving technologies of the high-power loads like HVACs, pumps and compressors, which will exert a great influence on demand side management. However, another group of loads defined as plug-in loads are also widely used and consume over 37% of the electricity in buildings. Usage of plug-in loads is typically directly affected by the consumer and real time pricing, and an effective management of these loads will also provide significant energy saving. To better understand the loads in residential home, this paper presents a comprehensive study of the plug-in load topologies at residential home.