用于预测加拿大格雷修女大楼用电量的深度卷积神经网络

IF 3.1 Q2 CONSTRUCTION & BUILDING TECHNOLOGY
N. Elshaboury, Eslam Mohammed Abdelkader, A. Al-Sakkaf, A. Bagchi
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引用次数: 0

摘要

目的随着消耗大量能源的建筑业的不断发展,建筑的能源效率一直受到重视。为此,本文的研究目的是对能源消耗进行预测,以改进能源资源的规划和管理。设计/方法/方法本研究提出了卷积神经网络(CNN)在估计加拿大Grey Nuns大楼用电量方面的应用。将所提出的模型的性能与长短期记忆(LSTM)和多层感知器(MLP)神经网络的性能进行了比较。使用Concordia设施部门提供的月度用电量记录(即2009年5月至2021年12月)对模型进行培训和测试。统计测量(例如确定系数[R2]、均方根误差[RMSE]、平均绝对误差[MAE]和平均绝对百分比误差[MAPE])用于评估模型的结果。结果显示,CNN模型在未来6个月和12个月的预测中优于其他模型。它将LSTM和MLP模型报告的R2、RMSE、MAE和MAPE的性能指标分别提高了4%、6%、42%和46%以上。因此,所提出的模型使用可用数据来预测未来6个月和12个月的电力消耗。2022年6月和12月,总用电量预计为195312 kWh和254737 kWh。原创性/价值本研究讨论了一个有效的时间序列模型的开发,该模型可以预测加拿大遗产建筑的未来用电量。深度学习技术首次被用于预测加拿大格雷修女大楼的用电量。此外,它还评估了深度学习和机器学习方法使用既定性能指标预测电力消耗的有效性。通过提高能源和环境效率,认识到建筑物的电力消耗对公用事业提供商、设施管理者和最终用户都是有益的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A deep convolutional neural network for predicting electricity consumption at Grey Nuns building in Canada
Purpose The energy efficiency of buildings has been emphasized along with the continual development in the building and construction sector that consumes a significant amount of energy. To this end, the purpose of this research paper is to forecast energy consumption to improve energy resource planning and management. Design/methodology/approach This study proposes the application of the convolutional neural network (CNN) for estimating the electricity consumption in the Grey Nuns building in Canada. The performance of the proposed model is compared against that of long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks. The models are trained and tested using monthly electricity consumption records (i.e. from May 2009 to December 2021) available from Concordia’s facility department. Statistical measures (e.g. determination coefficient [R2], root mean squared error [RMSE], mean absolute error [MAE] and mean absolute percentage error [MAPE]) are used to evaluate the outcomes of models. Findings The results reveal that the CNN model outperforms the other model predictions for 6 and 12 months ahead. It enhances the performance metrics reported by the LSTM and MLP models concerning the R2, RMSE, MAE and MAPE by more than 4%, 6%, 42% and 46%, respectively. Therefore, the proposed model uses the available data to predict the electricity consumption for 6 and 12 months ahead. In June and December 2022, the overall electricity consumption is estimated to be 195,312 kWh and 254,737 kWh, respectively. Originality/value This study discusses the development of an effective time-series model that can forecast future electricity consumption in a Canadian heritage building. Deep learning techniques are being used for the first time to anticipate the electricity consumption of the Grey Nuns building in Canada. Additionally, it evaluates the effectiveness of deep learning and machine learning methods for predicting electricity consumption using established performance indicators. Recognizing electricity consumption in buildings is beneficial for utility providers, facility managers and end users by improving energy and environmental efficiency.
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来源期刊
Construction Innovation-England
Construction Innovation-England CONSTRUCTION & BUILDING TECHNOLOGY-
CiteScore
7.10
自引率
12.10%
发文量
71
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