With the proportion of urban building energy consumption in total energy consumption continuing to rise, building an accurate energy consumption prediction and evaluation system has become the core requirement of building energy conservation management. Based on the real data of building energy consumption in the United States, this paper conducts a systematic analysis of the spatiotemporal characteristics, multi-source heterogeneity and data quality issues of energy consumption data, proposes an integrated modeling method that integrates machine learning and deep learning, and creates a prediction algorithm based on variational autoencoders and attention mechanisms. Based on this foundation, a multidimensional evaluation index system including energy efficiency, load response and operation fluctuation characteristics is constructed, and a hierarchical evaluation and dynamic benchmarking mechanism suitable for different building types is proposed. The empirical research of three cities, San Francisco, Chicago and Austin, verifies the advantages of the constructed model in prediction accuracy and evaluation stability. The research results can provide theoretical basis and technical guidance for intelligent building energy consumption control, regional energy conservation policy formulation and the development of low-carbon buildings.

Hao Pan. Construction of Integrated Prediction and Benchmark Evaluation System for Building Energy Consumption Data Based on Complex Data Processing. International Journal of Engineering Technology and Construction (2025), Vol. 6, Issue 1: 55-64. https://doi.org/10.38007/IJETC.2025.060106.

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