Building air conditioning heat load is a critical demand response resource, and its forecasting serves as a fundamental basis for optimizing building energy consumption control. The existing air conditioning load forecasting model suffers from reduced accuracy and stability due to the significant influence of temperature, humidity, and other factors. This paper proposes a method for building air conditioning heat load forecasting using Long Short-Term Memory neural network (LSTM) optimized by the Improved Sparrow Search Algorithm (ISSA). First, an experimental system for data collection on building air conditioning thermal load is constructed to form a dataset. Subsequently, the Latin Hypercube Sampling (LHS) is introduced to improve the SSA and iteratively optimize the hyperparameters of the LSTM model using ISSA. Finally, different optimization algorithms including Particle Swarm Optimizer (PSO), Crow Search Algorithm (CSA) and Spider Wasp Optimizer (SWO) were developed to optimize the LSTM model to achieve similar purposes, using the coefficient of determination as an indicator for evaluating model accuracy. The results show that the proposed data-driven new method is the most accurate model for forecasting building air conditioning heat load in this study. The R2 of the ISSA-LSTM forecasting model is as high as 0.9971, and compared with the RNN, LSTM, GRU, Bi-LSTM, SWO-LSTM, and SSA-LSTM models, the RMSE of this model in air conditioning heat load forecasting is reduced by 80.9670%, 71.7390%, 87.8040%, 88.3027%, 86.1179%, and 47.3089%, respectively. Employing the ISSA-LSTM method significantly enhances precision in building air conditioning load forecasting and holds promising applications in optimizing building energy consumption control.

中文翻译：

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ISSA-LSTM：一种新的数据驱动的建筑空调热负荷预测方法


建筑空调热负荷是重要的需求响应资源，其预测是优化建筑能耗控制的基础依据。现有的空调负荷预测模型由于受温度、湿度等因素影响较大，准确性和稳定性较低。本文提出了一种使用经改进麻雀搜索算法（ISSA）优化的长短期记忆神经网络（LSTM）构建空调热负荷预测的方法。首先，构建建筑空调热负荷数据采集实验系统，形成数据集。随后，引入拉丁超立方采样（LHS）来改进SSA，并使用ISSA迭代优化LSTM模型的超参数。最后，开发了包括粒子群优化器（PSO）、乌鸦搜索算法（CSA）和蜘蛛黄蜂优化器（SWO）在内的不同优化算法来优化LSTM模型以达到类似的目的，使用确定系数作为评估模型精度的指标。结果表明，所提出的数据驱动新方法是本研究中预测建筑空调热负荷最准确的模型。 ISSA-LSTM预测模型的R2高达0.9971，与RNN、LSTM、GRU、Bi-LSTM、SWO-LSTM和SSA-LSTM模型相比，该模型在空调热负荷预测中的RMSE分别减少了80.9670%、71.7390%、87.8040%、88.3027%、86.1179%和47.3089%。采用ISSA-LSTM方法显着提高了建筑空调负荷预测的精度，在优化建筑能耗控制方面具有广阔的应用前景。