Building energy prediction plays critical roles in the study of green building and smart city. The most challenging issue is to predict energy demand profiles over multiple time steps, which may have inconsistent timescales. Due to complex temporal dependence, existing prediction approaches cannot satisfy certain requirements in multistep (MS) or multitimescale (MTS) applications. In this article, a deep-chain echo state network (DCESN) is proposed to enhance the mapping capability for the MS demand prediction. The DCESN composed of many submodules of echo state network (ESN) belongs to the single-input multi-output (SIMO) model, and neuron states generated by sequential steps are utilized to prevent accumulative error in the recursive chain. Due to the novel learning mechanism of DCESN, numerical coefficients of temporal dependence are presented to explain the short-term and long-term impacts on future energy consumption. Experimental results in four cases indicate that the proposed DCESN has promising performance on MS and MTS prediction, and temporal dependence can be explained in a visible way. Comparative results of DCESN, sliding-window ESN, and long-short term memory (LSTM) demonstrate that the proposed learning mechanism could prevent error accumulation effectively.

中文翻译：

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具有可解释时间依赖性的深链回波状态网络，用于复杂建筑能量预测


建筑能耗预测在绿色建筑和智慧城市的研究中起着至关重要的作用。最具挑战性的问题是预测多个时间步长的能源需求概况，这些时间步长可能不一致。由于复杂的时间依赖性，现有的预测方法无法满足多步（MS）或多时间尺度（MTS）应用中的某些要求。在本文中，提出了一种深链回声状态网络（DCESN）来增强MS需求预测的映射能力。由回声状态网络（ESN）的多个子模块组成的DCESN属于单输入多输出（SIMO）模型，利用顺序步骤生成的神经元状态来防止递归链中的累积误差。由于 DCESN 新颖的学习机制，提出了时间依赖性的数值系数来解释对未来能源消耗的短期和长期影响。四种情况的实验结果表明，所提出的 DCESN 在 MS 和 MTS 预测方面具有良好的性能，并且可以以可见的方式解释时间依赖性。 DCESN、滑动窗口ESN和长短期记忆（LSTM）的比较结果表明，所提出的学习机制可以有效防止错误积累。