Next Article in Journal
Overall Adaptive Controller Design of PMSG Under Whole Wind Speed Range: A Perturbation Compensation Based Approach
Previous Article in Journal
Comparison of the Economy and Controllability of Pressure Swing Distillation with Two Energy-Saving Modes for Separating a Binary Azeotrope Containing Lower Alcohols
Previous Article in Special Issue
A Novel MPC with Actuator Dynamic Compensation for the Marine Steam Turbine Rotational Control with a Novel Energy Dynamic Model
Open AccessArticle

Neural-Network-Based Building Energy Consumption Prediction with Training Data Generation

1
Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
2
Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
3
Department of Architecture, Xi’an Jiaotong‐Liverpool University, Suzhou 215123, China
*
Author to whom correspondence should be addressed.
Processes 2019, 7(10), 731; https://doi.org/10.3390/pr7100731
Received: 20 June 2019 / Revised: 30 September 2019 / Accepted: 8 October 2019 / Published: 12 October 2019
(This article belongs to the Special Issue Dynamic Modeling and Control in Chemical and Energy Processes)
The importance of neural network (NN) modelling is evident from its performance benefits in a myriad of applications, where, unlike conventional techniques, NN modeling provides superior performance without relying on complex filtering and/or time-consuming parameter tuning specific to applications and their wider ranges of conditions. In this paper, we employ NN modelling with training data generation based on sensitivity analysis for the prediction of building energy consumption to improve performance and reliability. Unlike our previous work, where insignificant input variables are successively screened out based on their mean impact values (MIVs) during the training process, we use the receiver operating characteristic (ROC) plot to generate reliable data with a conservative or progressive point of view, which overcomes the issue of data insufficiency of the MIV method: By properly setting boundaries for input variables based on the ROC plot and their statistics, instead of completely screening them out as in the MIV-based method, we can generate new training data that maximize true positive and false negative numbers from the partial data set. Then a NN model is constructed and trained with the generated training data using Levenberg–Marquardt back propagation (LM-BP) to perform electricity prediction for commercial buildings. The performance of the proposed data generation methods is compared with that of the MIV method through experiments, whose results show that data generation using successive and cross pattern provides satisfactory performance, following energy consumption trends with good phase. Among the two options in data generation, i.e., successive and two data combination, the successive option shows lower root mean square error (RMSE) than the combination one by around 400~900 kWh (i.e., 30%~75%). View Full-Text
Keywords: energy management; building modelling; neural network (NN); receiver operating characteristic (ROC); mean impact value (MIV) energy management; building modelling; neural network (NN); receiver operating characteristic (ROC); mean impact value (MIV)
Show Figures

Figure 1

MDPI and ACS Style

Lee, S.; Cha, J.; Kim, M.K.; Kim, K.S.; Pham, V.H.; Leach, M. Neural-Network-Based Building Energy Consumption Prediction with Training Data Generation. Processes 2019, 7, 731.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop