Forecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm. (15th August 2019)

• Record Type: Journal Article
• Authors: Wang, Wei; Hong, Tianzhen; Xu, Xiaodong; Chen, Jiayu; Liu, Ziang; Xu, Ning
• Is Part Of: Applied energy. Volume 248(2019)
• Page Start: 217
• Page End: 230
• Publication Date: 2019-08-15
• Subjects: District-scale building energy modeling -- Data-driven prediction -- Building network -- Long short-term memory networks; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2019.04.085
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Incorporated inter-building effects and energy consumption correlations. Integrated building network and long short-term memory algorithm. Five building groups on two campuses was used for validation. The proposed model can forecast accurate energy dynamics at district-scale. Abstract: With the development of data-driven techniques, district-scale building energy prediction has attracted increasing attention in recent years for revealing energy use patterns and reduction potentials. However, data acquisition in large building groups is difficult and adjacent buildings also interact with each other. To reduce data cost and incorporate the inter-building impact with the data-driven building energy model, this study proposes a deep learning predictive approach that fuses the building network model with a long short-term memory learning model for district-scale building energy modeling. The building network was constructed based on correlations between the energy use intensity of buildings, which can significantly reduce the computational complexity of the deep learning models for energy dynamic prediction. Five typical building groups with energy use data from 2015 to 2018 on two institutional campuses were selected to perform the validation experiment with TensorFlow. Based on the prediction error assessments, the results suggest that for total building energy use intensity prediction, the proposed model can achieve a mean absolute percentage error of 6.66% and a root mean square error of 0.36 kWh/m 2, compared to 12.05% and 0.63 kWh/m 2 of the conventional artificial neural network model and to 11.06% and 0.89 kWh/m 2 for the support vector regression model.