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A hybrid deep learning-based method for short-term building energy load prediction combined with an interpretation process

机译：一种基于混合的基于深度学习的短期建筑能量负荷预测方法与解释过程结合

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摘要

Data driven-based building energy load prediction is of great value for building energy management tasks such as fault diagnosis and optimal control. However, there are two challenges for conventional data driven-based prediction methods. The first challenge is that time-lag measurements such as historical cooling loads still cannot be taken full advantage of. To deal with this challenge, a hybrid prediction method is proposed based on long short-term memory networks and artificial neural networks. The second challenge is that data driven-based models are hard to explain by domain knowledge. To deal with this challenge, an interpretation method is proposed based on a dimensionless sensitivity index and a weighted Manhattan distance. Operation data of a public building are utilized to evaluate the proposed methods. Results show that the proposed hybrid prediction method has higher prediction accuracy than conventional prediction methods in one-hour-ahead cooling load prediction. Crucial factors affecting building cooling loads are revealed successfully based on the proposed sensitivity index. Moreover, the weighted Manhattan distance is utilized to quantify the difference between predicted conditions and known conditions of training data. Results show that the prediction accuracy of data driven-based methods is reduced with the increase of the weighted Manhattan distance. It is further discovered that relationships between logarithmic prediction residuals and corresponding logarithmic weighted Manhattan distances are approximatively linear. (C) 2020 Elsevier B.V. All rights reserved.
机译：基于数据驱动的建筑能量负载预测对于建立能源管理任务（如故障诊断和最佳控制）的价值很大。然而，传统数据驱动的预测方法存在两个挑战。第一个挑战是，诸如历史冷却负荷的时间滞后测量仍然无法充分利用。为了处理这一挑战，基于长短期存储网络和人工神经网络提出了一种混合预测方法。第二个挑战是基于数据驱动的模型很难通过域知识来解释。为了解决这一挑战，基于无量纲敏感性指数和加权曼哈顿距离提出了一种解释方法。使用公共建筑的操作数据来评估所提出的方法。结果表明，所提出的混合预测方法具有比一小时前进的冷却负荷预测中的传统预测方法更高的预测精度。基于所提出的敏感性指数，成功地揭示了影响建筑冷却载荷的重要因素。此外，利用加权曼哈顿距离来量化预测条件和已知训练条件之间的差异。结果表明，随着加权曼哈顿距离的增加，基于数据驱动的方法的预测准确性。进一步发现，对数预测残差和对应对数加权曼哈顿距离之间的关系是近似的线性的。 （c）2020 Elsevier B.v.保留所有权利。

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