标题： 基于物理引导的记忆网络用于建筑能耗建模 显示英文标题

标题： Physics-Guided Memory Network for Building Energy Modeling

摘要： 准确的能耗预测对于建筑行业的高效资源管理和可持续性至关重要。 深度学习模型非常成功，但在历史数据有限时表现不佳，并且在没有历史数据的情况下变得不可用，例如在新建的建筑物中。 另一方面，基于物理的模型，如EnergyPlus，可以在不依赖历史数据的情况下模拟能耗，但需要大量的建筑参数说明，并且建模一个建筑需要大量时间。 本文介绍了一种物理引导记忆网络（PgMN），这是一种将深度学习和基于物理的模型预测整合的神经网络，以解决它们的局限性。 PgMN包括并行投影层来处理不完整的输入，记忆单元来考虑持续的偏差，以及记忆经验模块，以最优地扩展预测超出其输入范围并产生输出。 理论评估表明，PgMN的组件在数学上是有效的，可以执行各自的任务。 PgMN在小时级的短期能耗预测中进行了评估，这对智能电网和智能建筑系统的操作决策至关重要。 实验验证显示了PgMN在各种场景中的准确性和适用性，例如新建建筑、缺失数据、稀疏历史数据和动态基础设施变化。 本文为动态建筑环境中的能耗预测提供了一个有前景的解决方案，在历史数据有限或不可用的情况下，或者当基于物理的模型不足时，增强了模型的适用性。 显示英文摘要

摘要： Accurate energy consumption forecasting is essential for efficient resource management and sustainability in the building sector. Deep learning models are highly successful but struggle with limited historical data and become unusable when historical data are unavailable, such as in newly constructed buildings. On the other hand, physics-based models, such as EnergyPlus, simulate energy consumption without relying on historical data but require extensive building parameter specifications and considerable time to model a building. This paper introduces a Physics-Guided Memory Network (PgMN), a neural network that integrates predictions from deep learning and physics-based models to address their limitations. PgMN comprises a Parallel Projection Layers to process incomplete inputs, a Memory Unit to account for persistent biases, and a Memory Experience Module to optimally extend forecasts beyond their input range and produce output. Theoretical evaluation shows that components of PgMN are mathematically valid for performing their respective tasks. The PgMN was evaluated on short-term energy forecasting at an hourly resolution, critical for operational decision-making in smart grid and smart building systems. Experimental validation shows accuracy and applicability of PgMN in diverse scenarios such as newly constructed buildings, missing data, sparse historical data, and dynamic infrastructure changes. This paper provides a promising solution for energy consumption forecasting in dynamic building environments, enhancing model applicability in scenarios where historical data are limited or unavailable or when physics-based models are inadequate.