Energy consumption forecasting based on spatio-temporal behavioral analysis for demand-side management

Understanding user-level energy demand is pivotal for sustainable development and smart grid implementation, as it facilitates optimal resource allocation and energy conservation. Traditional machine learning models, however, often ignored the potential connections between users, limiting comprehension of user-level energy demands. Capturing behavioral correlations among users in high-dimensional temporal data remains challenging. In this paper, a novel scheme is proposed for revealing the implicit energy behavior correlation, which serves as prior knowledge to enhance predictive model performance. In addition, a spatio-temporal feature extraction framework is introduced to fuse spatial and temporal information, capturing the coherence of energy consumption data. This approach captures the correlation between the energy consumption patterns of different users, achieving highly accurate demand forecasting at the user level. In fine-grained demand forecasting experiments, the prediction accuracy of the proposed approach was improved by 14% compared with the baseline model. Based on fine-grained prediction results, an innovative visual analytical interface is also developed to characterize the migration of energy demand over time in both physical and topological space, offering valuable insights into demand-side energy management. In the empirical study, we found that there is an obvious mental inertia in the energy behavior of urban residents, which leads to energy waste. Our research provides critical insights for policymakers and planners in addressing the sustainability challenges of urban energy systems.