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Building load forecasting is critical for energy saving and carbon emission reduction, as building loads account for a rising percentage of energy consumption. Existing load forecasting methods only target a single building and do not consider the spatial characteristics of different building loads in an architectural complex, making it impossible to effectively forecast loads for all buildings in an architectural complex simultaneously. In this paper, considering the spatial correlation among building loads in an architectural complex, a novel short-term load forecasting method is proposed to improve the load forecasting accuracy for the architectural complex. First, Pearson correlation analysis is carried out to determine the key influencing factors of load, which are treated as multi-information. Then, to realize the load forecasting of all buildings in an architectural complex at the same time, a multi-information fusion model combined with a graph convolutional network and a long-short term memory network is proposed, and spatio-temporal attention modules are included to extract the dynamic spatio-temporal correlations of load. The performance of the proposed model is evaluated using actual load data from a public university in Beijing. When compared to other current methods, the experimental results show that the proposed model not only achieves the best results for architectural complex load forecasting (the largest MAPE is only 6.86%, the smallest R2 and Accuracy reach 0.87 and 0.85, respectively), but also maintains forecasting stability to some extent over a longer forecast period. The proposed method is a meaningful step towards achieving fast and accurate architectural complex load forecasting. [ABSTRACT FROM AUTHOR]

Load forecasting analysis plays an important role for regional electric power project planning as well as consumption management. For improving the long-term load forecasting accuracy and usability, this paper proposes a new residual-type combined Grey Model-Least Squares Support Vector Machine forecasting model for the component loads by extracting the load characteristics. In this model, each component decomposed load is forecasted using characteristic-matched model. Meanwhile, Moran's I and α convergence are used to identify the spatial-temporal distribution characteristics and levels of electric consumption intensity from the perspective of spatial econometrics. The results show that the proposed model can improve the forecasting accuracy, and can avoid the poor usability in the application phase. According to a case study for Beijing, the electric consumption intensity of the city has a positive spatial correlation, and the low electric consumption intensity districts are mainly concentrated in the central region, which reveals a trend of spreading from the center to periphery. The overall electric consumption intensity shows a downward trend, but the internal differences increase with time. The results may help to carry out electric construction planning, and to formulate regionally energy-saving policies. • Electric load characteristics are captured with Empirical Mode Decomposition. • The component decomposed loads are forecasted by characteristic-matched models. • Building a residual-type combined Grey Model-Least Squares Support Vector Machine model for load forecasting. • Electric anomalies and substitution potential districts are identified from spatial econometrics. • Analyzing convergence trend and level of electric consumption intensity. [ABSTRACT FROM AUTHOR]