Residential Consumer Portrait Modelling Based on Multi-Dimensional Behaviour Analysis in Smart Grid

With the rapid deployment of distributed energy resources and electricity market reforms, residential users have transformed from passive consumers to active prosumers, making accurate characterization of behavioural features critical for grid flexibility and efficiency. However, existing research lacks systematic methods spanning from data processing to application decision-making. This research proposes a residential user profiling framework based on multi-dimensional behaviour analysis and applies it to critical smart grid operational and market decision-making. A data preprocessing framework is constructed, achieving missing value imputation through Random Forest Regression optimized by Improved Quantum-Inspired Genetic Algorithm and Empirical Mode Decomposition for anomaly detection. A multi-dimensional label system covering energy configuration, resource characteristics, and behaviour patterns is established with significance quantified through Analysis of Variance. A dynamic Time Warping Path-Aware Weighted K-Shape clustering algorithm identifies typical load shapes, while seasonal structures are mined through Principal Component Analysis and Agglomerative Hierarchical Clustering. A seven-dimensional evaluation system is established to ensure profiling quality. The constructed profiles are then applied to three key areas. In distributed photovoltaic forecasting, generation reconstruction is achieved for surplus feed-in scenarios, Density-Based Spatial Clustering performs grouping, three deep learning models are trained at cluster levels, transfer learning based on profile similarity enables knowledge sharing, and a Twin Delayed Deep Deterministic Policy Gradient reinforcement learning algorithm optimizes multi-model weights. In load forecasting, a two-stage retrieval mechanism using Gaussian similarity and Grey Relational Analysis is designed, and a cross-attention fusion framework aligns profiles, historical patterns, and meteorological signals. For demand side management, a cross-market optimization framework handles uncertainty through chance constraints, unifying allocation and bidding strategies across energy and Frequency Control Ancillary Services markets and analysing optimal Battery Energy Storage System capacity. Case studies on real Australian data validate effectiveness in profile modelling, forecasting accuracy, and market revenue optimization, providing a systematic, precise, and collaborative solution for residential users in smart grids.