The precise prediction of thermal load has consistently garnered attention owing to its significant impact on energy conservation in buildings. The methodologies employed primarily concentrate on modeling within steady-state conditions, utilizing time-series data or mechanism model on fixed parameters. However, given the pronounced time-varying and multifaceted disturbance characteristics associated with building loads, current approaches exhibit constrained efficacy in addressing abrupt fluctuations in demand load and managing data noise. This limitation consequently undermines the accuracy of predictions. This paper proposes a novel hybrid model and an error-trigger adjusting strategy for predicting the thermal load in super-high buildings. The model is constructed by combining a thermodynamic model and an error cancellation model. The former, derived from an examination of the variations of material and energy in buildings, is proposed in the form of an approximate resistance-capacitance structure. The latter is developed using a wavelet threshold denoising technique, in conjunction with a convolutional neural network and a long short-term memory network. A self-adaptive state transition algorithm has been proposed, which relies on dynamically adjusting factors within the feasible region to optimize the selection of unknown parameters in the thermodynamic model. To enhance the flexibility of the hybrid model in effectively respond to the intricacies and fluctuations within the thermal conditions of buildings, an error-trigger adaptive updating strategy and a parameter calibration method based on sensitivity analysis are established. The real-world application results demonstrate the effectiveness of the presented hybrid model and the adjusting strategy.

中文翻译：

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一种采用超高层建筑热负荷误差触发调整方法的混合预测模型


热负荷的精确预测因其对建筑物节能的重大影响而一直受到关注。所采用的方法主要集中在稳态条件下的建模，利用时间序列数据或固定参数的机制模型。然而，鉴于与建筑负载相关的明显时变和多方面干扰特性，当前方法在解决需求负载的突然波动和管理数据噪声方面表现出有限的效率。因此，这种限制会破坏预测的准确性。本文提出了一种新的混合模型和误差触发调整策略，用于预测超高层建筑的热负荷。该模型是通过结合热力学模型和误差消除模型构建的。前者源自对建筑物中材料和能量变化的检查，以近似电阻电容结构的形式提出。后者是使用小波阈值去噪技术结合卷积神经网络和长短期记忆网络开发的。提出了一种自适应状态转换算法，该算法依赖于在可行区域内动态调整因子来优化热力学模型中未知参数的选择。为了增强混合模型在有效响应建筑物热条件的复杂性和波动性方面的灵活性，建立了一种误差触发自适应更新策略和基于敏感性分析的参数标定方法。实际应用结果证明了所提出的混合模型和调整策略的有效性。