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This study proposes a comprehensive framework integrating eXplainable Artificial Intelligence (XAI) techniques with clustering-based context extraction to enhance energy consumption forecasting in modern office buildings. By leveraging explanation vectors derived from state-of-the-art XAI methods such as SHAP and LIME, our framework identifies latent operational contexts from sensor data aggregated at 15-min intervals. These contexts enable the tailoring of predictive models through feature augmentation, context-specific training, and transfer learning strategies, thereby improving forecasting accuracy compared to conventional approaches. To identify the best-performing models for each context, hyperparameter optimization via grid search is employed across multiple algorithms–including Gradient Boosting, Random Forest, and K-Nearest Neighbors. Extensive experiments demonstrate that context-aware models significantly outperform baseline methods, achieving up to a 7% improvement in the coefficient of determination ( \(R^2\) ) and a marked reduction in error metrics. Our findings underscore the importance of integrating XAI with data-driven modeling to enhance predictive performance and model interpretability, which are critical for practical energy management and decision-making in complex building environments.

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Leveraging XAI Techniques for Context-Aware Energy Consumption Forecasting

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

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  • Brígida Teixeira,
  • Tiago Pinto,
  • Zita Vale

摘要

This study proposes a comprehensive framework integrating eXplainable Artificial Intelligence (XAI) techniques with clustering-based context extraction to enhance energy consumption forecasting in modern office buildings. By leveraging explanation vectors derived from state-of-the-art XAI methods such as SHAP and LIME, our framework identifies latent operational contexts from sensor data aggregated at 15-min intervals. These contexts enable the tailoring of predictive models through feature augmentation, context-specific training, and transfer learning strategies, thereby improving forecasting accuracy compared to conventional approaches. To identify the best-performing models for each context, hyperparameter optimization via grid search is employed across multiple algorithms–including Gradient Boosting, Random Forest, and K-Nearest Neighbors. Extensive experiments demonstrate that context-aware models significantly outperform baseline methods, achieving up to a 7% improvement in the coefficient of determination ( \(R^2\) ) and a marked reduction in error metrics. Our findings underscore the importance of integrating XAI with data-driven modeling to enhance predictive performance and model interpretability, which are critical for practical energy management and decision-making in complex building environments.