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Understanding energy consumption patterns is crucial for energy demand-side management. Unlike traditional data mining or machine learning-based methods, this paper presents visual analysis methods for exploring energy consumption data from spatial, temporal, and spatiotemporal dimensions, including variability, segmentation, and energy demand shifts. To support the proposed methods, we develop a visual analysis tool that allows users to explore consumption data and validate their hypotheses based on visual results through human–client–server interactions. In particular, we propose a novel potential flow-based method to model energy demand shift patterns and have integrated it into the proposed analysis tool. We comprehensively evaluate the proposed methods and the tool using real-world electricity consumption data from the Shanghai Pudong district, and compare with traditional data mining methods. The results demonstrated the effectiveness and superiority of the proposed visual analysis methods, including its ability to discover the spatiotemporal variability of energy demand, customer groups, and demand shift patterns across different geographical areas and time horizons. All results can be well explained by knowledge of the energy consumption in the study region. • Visualization and visual analysis for energy demand-side management. • A visual based method for exploring consumption variability, customer segmentation, and demand shift patterns. • A potential flow method to model energy demand dynamics. [ABSTRACT FROM AUTHOR]

Energy demand-side management, especially empowered by the fine-grained smart meter data, plays a significant role in the rational allocation of energy, monitoring and supervision of energy consumption behaviors. Through the in-depth demand analysis including quantification of energy consumption dynamics and consumer preferences, energy decision-makers can develop reasonable and forethoughtful energy efficiency plans and demand-response programs. Previous work in energy-demand behavioral research relied primarily on ideal socio-economic models or data-driven approaches, both of which lack flexibility, intuition and interpretability. This paper proposes a novel spatio-temporal visual analysis approach for urban energy consumption pattern discovery in order to identify energy-saving potentials, plan energy supply and improve energy efficiency. In this approach, energy consumption time series are embeded into a two-dimensional scatterplot for coordinated visual exploration. Users can interactively explore and discover different patterns for decision-making purposes. In addition, we propose the method for modeling energy demand shift patterns based on a potential flow method and integrate it into a pattern exploration tool. The proposed approach is comprehensively evaluated through empirical studies using the real-world electricity consumption data from Pudong district, Shanghai. We identify five typical energy consumption patterns and demand shift patterns across different geographical locations, which can be well interpreted by the knowledge of energy consumption in the area of interest. The results demonstrate the effectiveness of the proposed approach and the tool. This tool can be integrated into smart energy systems for a better understanding of user energy consumption behaviors and preferences. • Understand the spatial and temporal dynamics of urban energy use through visual analysis and visualization. • A visual analysis tool to facilitate the discovery of energy consumption patterns for smart energy systems. • Development of dimension reduction and visualization methods for high-dimensional time series. • Discovery of typical energy consumption patterns and spatio-temporal shift patterns through an empirical case study. [ABSTRACT FROM AUTHOR]

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. • Mapped high-dimensional energy data in 2D space, identified 6 basic energy patterns. • Developed a user-level energy forecasting model with spatio-temporal correlations. • Created a visual tool to track demand migration, aided demand-side management. [ABSTRACT FROM AUTHOR]

Understanding urban demand profiles is an important determinant for energy dispatch and the optimization of the electric energy supply. For the design of the energy supply system, an important consideration is, to express the characteristics of urban household energy demand as a function of space and time. However, the focus of most research activities is only on the modeling of time series data. High-resolution forecasting models for the spatial–temporal distribution of energy were rarely reported in current literature. In this paper, we propose a spatio-temporal forecasting model based on potential-flow for urban energy demand forecasting. Compared with previous studies, potential-flow can describe energy migration in space with a high resolution. Based on the orientation of vectors, our model can predict the direction and intensity of spatial migrations in energy demand and identify energy transfer events. Extensive experiments on real-world data sets verify that our approach can achieve a better prediction accuracy compared with traditional methods. In further empirical studies, we find that the temporal electricity demand flow shows locally concentrating behavior for different regions of the city. In addition to temporal factors such as seasons, peaks and valleys, such clustering behavior also depend on local populations and major industries (financial, commercial, residential, etc.). Finally, we use entropy to quantitatively describe the intensity of this clustering phenomenon and explore its relationship with meteorological factors. Our research demonstrates a unified visual prediction approach to support exploratory demand analysis. We anticipate that the process will be expanded to support more forms of energy in the future. • A novel method to visualize the spatio-temporal variation of energy demand. • A variable-resolution spatio-temporal energy prediction framework. • An intuitive and user-friendly visualization analysis interface. • Energy demand entropy is proposed to analyze the energy demand variation. [ABSTRACT FROM AUTHOR]

• A novel attention mechanism based energy demand forecasting algorithm. • Textual data mined to improve the forecasting capability. • Validated on a real-world electricity consumption dataset. Forecasting energy demand of residential buildings plays an important role in the operation of smart cities, as it forms the basis for decision-making in the planning and operation of urban energy systems. Deep learning algorithms are commonly used to reliably predict potential energy usage since they can overcome the issue of dependency on long-distance data in energy forecasting relative to the standard regression model. However, there are still two problems to be solved for energy forecasting, including the encoding of categorical characteristics and adaptive extraction of the most relevant characteristics for the use in predictions. To address the problems, we proposed a sequential forecasting model for medium- and long-term energy demand forecasting based on an embedding mechanism and a two-stage attention-based long-term memory neural network. An empirical study was conducted on three years of daily electricity consumption data from the residential buildings of the Pudong district of Shanghai to evaluate the model. The results show that the model can effectively extract the key features that are highly correlated with energy consumption dynamics by employing long-term dependencies in time series. In addition, the hybrid model outperforms others in terms of long-term forecasting capability. This paper also discusses future research directions and the possibilities for applying deep learning techniques in the energy sector. [ABSTRACT FROM AUTHOR]