The industrial and building sector demands the largest proportion of global energy, therefore adopting energy efficiency related strategies, optimization techniques and management is an important step towards global energy reduction. The use of machine learning techniques in energy forecasting is gaining popularity due to their ability to solve complex non-linear problems, however this is predominately seen in the residential and commercial sector. This study proposes and compares the use of two Deep Neural Networks, Feed Forward and Recurrent, to forecast manufacturing facility energy consumption and workshop conditions based on production schedules, climatic conditions, thermal properties of the facility building, along with building behaviour and use. The feed forward model was able to predict building energy, workshop air temperatures and humidity to an accuracy of 92.4, 99.5 and 64.8 % respectively when the model was provided with a new dataset, with the recurrent model predicting these variables to accuracies of 96.82, 99.40 and 57.60 %. The neural networks were trained with data obtained from the simulation of a medium sized manufacturing facility in the UK. Coupling simulation techniques with machine learning algorithms allows for a low cost, non-intrusive methodology of predicting and optimising building energy consumption in the manufacturing sector. Furthermore, the use of neural networks provided forecasted building energy profiles for the identification of spikes in energy consumption; an undesirable and considerable cost in the manufacturing sector, as well as the predication of manufacturing environmental conditions for condition monitoring of condition sensitive production environments.

工业和建筑部门对全球能源的需求最大，因此采用与能效相关的策略，优化技术和管理是朝着全球节能迈出的重要一步。由于机器学习技术能够解决复杂的非线性问题，因此在能源预测中使用机器学习技术正变得越来越流行，但是，这在住宅和商业领域尤为明显。这项研究提出并比较了两个深层神经网络（前馈和循环）的使用，它们根据生产进度，气候条件，设施建筑物的热特性以及建筑物的行为和用途来预测制造设施的能耗和车间状况。前馈模型能够预测建筑能耗，当向模型提供新的数据集时，车间空气温度和湿度的准确度分别为92.4％，99.5％和64.8％，而递归模型预测这些变量的准确性为96.82％，99.40％和57.60％。使用从英国一家中型制造工厂的模拟获得的数据对神经网络进行了训练。将模拟技术与机器学习算法结合在一起，可实现一种低成本，非侵入性的方法，用于预测和优化制造业的建筑能耗。此外，神经网络的使用提供了预测的建筑能耗曲线，以识别能耗峰值。制造业中不希望的，可观的成本，