Juq373

  • Evaluation Metric: The manuscript relies heavily on RMSE and MAE. While standard, these metrics can be dominated by large loads. It would be beneficial to include Mean Absolute Percentage Error (MAPE) or Coefficient of Variation of RMSE (CVRMSE) to normalize performance across buildings of different scales,

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    In this manuscript, the authors propose a time-series foundation model specifically tailored for building energy forecasting. The study addresses a critical challenge in the energy sector: the scarcity of high-quality labeled data for specific buildings and the need for models that can generalize well across different building types and climatic zones. The authors adapt a transformer-based architecture (likely inspired by recent advances in foundation models like TimesNet or PatchTST) to leverage large-scale pre-training on diverse building datasets, followed by fine-tuning on downstream tasks.

    The topic is timely and highly relevant to the readership of the journal. The transition from specialized, single-building models to generalist foundation models represents a significant shift in the paradigm of energy forecasting. However, while the conceptual framework is sound, the manuscript currently suffers from insufficient detail regarding the pre-training protocol and a lack of rigorous benchmarking against state-of-the-art (SOTA) statistical and machine learning baselines. I notice that "juq373" appears to be a

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