Accurate building cooling load prediction is beneficial in managing optimal operation to conserve energy user and operational cost. Several physics-based and data-driven models proposed to forecast building cooling load focus on one-step-ahead prediction. Deep learning-based Long-term memory (LSTM) models are shown to perform well for one-step short-term (1Hour Ahead) building cooling load prediction. However, no prior studies have examined the prediction performance of shallow machine learning methods over deep learning models to forecast building cooling demand over multi-steps across diverse real-world datasets. A multi-step model learns a single parametric function from input time series and forecasts an array of building cooling load values (multi-step) simultaneously. A comprehensive study has been carried out to evaluate the performance of six data-driven models (2 shallow learning, 3 deep sequential learning, and 1 heuristic method) to predict multi-step long-term (1Day Ahead) building cooling load. Our results demonstrate variant of the LSTM model, the Recurrent Neural Network Multi-Input Multi-Output (RNN-MIMO) network architecture, performs consistently well compared to its deep learning counterparts and shallow machine learning techniques, both tree boosting and support vector regression. Notable conclusions from results obtained are twofold: Firstly, Long short-term memory (LSTM) based RNN-MIMO architecture performs well in both short-term (1Hour Ahead) and long-term (1Day Ahead) multi-step forecast horizon. RNN-MIMO is up to 33% more accurate, on average, in terms of mean absolute error over existing, state-of-the-art shallow machine learning models both Support Vector Regression (SVR) and tree boosting techniques (XGBoost). Our findings have significant implications for practice. Notably, machine learning models trained on one-step-ahead predictions cannot be deployed readily to predict multiple time steps into the future since longer prediction horizons impose additional training and fine-tuning efforts over each of the multiple steps. RNN-MIMO model’s ability to predict multiple time steps simultaneously eliminates the need for manual fine-tuning of individual models for each required forecast horizon.

准确的建筑冷负荷预测有利于管理优化运行以节省能源用户和运营成本。提出的几种基于物理和数据驱动的模型来预测建筑冷负荷，重点是提前一步预测。基于深度学习的长期记忆 (LSTM) 模型在一步短期（提前 1 小时）建筑冷负荷预测方面表现良好。然而，之前没有研究检查过浅层机器学习方法相对于深度学习模型的预测性能，以预测跨不同现实世界数据集的多步骤建筑冷却需求。多步模型从输入时间序列中学习单个参数函数，并同时预测一系列建筑冷负荷值（多步）。已经进行了一项综合研究，以评估六种数据驱动模型（2 种浅层学习、3 种深度顺序学习和 1 种启发式方法）预测多步长期（提前 1 天）建筑冷负荷的性能。我们的结果证明了 LSTM 模型的变体，即循环神经网络多输入多输出 (RNN-MIMO) 网络架构，与其深度学习对应物和浅层机器学习技术（树提升和支持向量回归）相比，始终表现良好。获得的结果有两个值得注意的结论：首先，基于长期短期记忆 (LSTM) 的 RNN-MIMO 架构在短期（提前 1 小时）和长期（提前 1 天）多步预测范围内表现良好。就平均绝对误差而言，RNN-MIMO 的准确度平均比现有的高 33%，最先进的浅层机器学习模型包括支持向量回归 (SVR) 和树提升技术 (XGBoost)。我们的发现对实践具有重要意义。值得注意的是，在一步超前预测上训练的机器学习模型无法轻易部署来预测未来的多个时间步，因为更长的预测范围会在多个步骤中的每一个上施加额外的训练和微调工作。RNN-MIMO 模型能够同时预测多个时间步长，无需为每个所需的预测范围手动微调各个模型。由于较长的预测范围会在多个步骤中的每一个步骤上施加额外的训练和微调工作，因此无法轻松部署在一步超前预测上训练的机器学习模型来预测未来的多个时间步骤。RNN-MIMO 模型能够同时预测多个时间步长，无需为每个所需的预测范围手动微调各个模型。由于较长的预测范围会在多个步骤中的每一个步骤上施加额外的训练和微调工作，因此无法轻松部署在一步超前预测上训练的机器学习模型来预测未来的多个时间步骤。RNN-MIMO 模型能够同时预测多个时间步长，无需为每个所需的预测范围手动微调各个模型。