The real-world building can be regarded as a comprehensive energy engineering system; its actual energy consumption depends on complex affecting factors, including various weather data and time signature. Accurate energy consumption forecasting and effective energy system management play an essential part in improving building energy efficiency. The multi-source weather profile and energy consumption data could enable integrating data-driven models and evolutionary algorithms to achieve higher forecasting accuracy and robustness. The proposed building energy consumption forecasting system consists of three layers: data acquisition and storage layer, data pre-processing layer and data analytics layer. The core part of the data analytics layer is a hybrid genetic algorithm (GA) and long-short term memory (LSTM) neural network model for accurate and robust energy prediction. LSTM neural network is adopted to capture the interrelationship between energy consumption data and time. GA is adopted to select the optimal architecture for LSTM neural networks to improve its forecasting accuracy and robustness. The hyper-parameters for determining LSTM architecture include the number of LSTM layers, number of neurons in each LSTM layer, dropping rate of each LSTM layer and network learning rate. Meanwhile, the effects of historical weather profile and time horizon of past information are also investigated. Two real-life educational buildings are adopted to test the performance of the proposed building energy consumption forecasting system. Experiments reveal that the proposed adaptive LSTM neural network performs better than the existing feedforward neural network and LSTM-based prediction models in accuracy and robustness. It also outperforms those LSTM networks whose hyper-parameters are determined by grid search, Bayesian optimisation and PSO. Such accurate energy consumption prediction can play an essential role in various areas, including daily building energy management, decision making of facility managers, building information model designs, net-zero energy operation, climate change mitigation and circular economy.

现实世界的建筑可视为一个综合的能源工程系统；其实际能耗取决于复杂的影响因素，包括各种天气数据和时间特征。准确的能耗预测和有效的能源系统管理对于提高建筑能效至关重要。多源天气概况和能源消耗数据可以集成数据驱动模型和进化算法，以实现更高的预测准确性和鲁棒性。提出的建筑能耗预测系统由三层组成：数据采集和存储层、数据预处理层和数据分析层。数据分析层的核心部分是混合遗传算法 (GA) 和长短期记忆 (LSTM) 神经网络模型，用于准确和稳健的能量预测。采用 LSTM 神经网络来捕捉能耗数据与时间之间的相互关系。采用遗传算法为 LSTM 神经网络选择最优架构，以提高其预测精度和鲁棒性。用于确定 LSTM 架构的超参数包括 LSTM 层数、每个 LSTM 层中的神经元数、每个 LSTM 层的丢包率和网络学习率。同时，还研究了历史天气剖面和过去信息时间范围的影响。采用两座现实生活中的教育建筑来测试所提出的建筑能耗预测系统的性能。实验表明，所提出的自适应 LSTM 神经网络在准确性和鲁棒性方面的性能优于现有的前馈神经网络和基于 LSTM 的预测模型。它还优于那些超参数由网格搜索、贝叶斯优化和 PSO 确定的 LSTM 网络。这种准确的能耗预测可以在各个领域发挥重要作用，包括日常建筑能源管理、设施管理者的决策、建筑信息模型设计、净零能耗运营、减缓气候变化和循环经济。它还优于那些超参数由网格搜索、贝叶斯优化和 PSO 确定的 LSTM 网络。这种准确的能耗预测可以在各个领域发挥重要作用，包括日常建筑能源管理、设施管理者的决策、建筑信息模型设计、净零能耗运营、减缓气候变化和循环经济。它还优于那些超参数由网格搜索、贝叶斯优化和 PSO 确定的 LSTM 网络。这种准确的能耗预测可以在各个领域发挥重要作用，包括日常建筑能源管理、设施管理者的决策、建筑信息模型设计、净零能耗运营、减缓气候变化和循环经济。