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Forecasting heating and cooling loads of buildings: a comparative performance analysis
, Samui P, Nagtode I, Jain H, Shivaramakrishnan V, Mohammadi-ivatloo B.
Published in Springer Science and Business Media LLC
2020
Volume: 11
   
Issue: 3
Pages: 1253 - 1264
Abstract
Heating load and cooling load forecasting are crucial for estimating energy consumption and improvement of energy performance during the design phase of buildings. Since the capacity of cooling ventilation and air-conditioning system of the building contributes to the operation cost, it is ideal to develop accurate models for heating and cooling load forecasting of buildings. This paper proposes a machine-learning technique for prediction of heating load and cooling load of residential buildings. The proposed model is deep neural network (DNN), which presents a category of learning algorithms that adopt nonlinear extraction of information in several steps within a hierarchical framework, primarily applied for learning and pattern classification. The output of DNN has been compared with other proposed methods such as gradient boosted machine (GBM), Gaussian process regression (GPR) and minimax probability machine regression (MPMR). To develop DNN model, the energy data set has been divided into training (70%) and testing (30%) sets. The performance of proposed model was benchmarked by statistical performance metrics such as variance accounted for (VAF), relative average absolute error (RAAE), root means absolute error (RMAE), coefficient of determination (R2), standard deviation ratio (RSR), mean absolute percentage error (MAPE), Nash–Sutcliffe coefficient (NS), root means squared error (RMSE), weighted mean absolute percent error (WMAPE) and mean absolute percentage Error (MAPE). DNN and GPR have produced best-predicted VAF for cooling load and heating load of 99.76% and 99.84% respectively. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.
About the journal
JournalData powered by TypesetJournal of Ambient Intelligence and Humanized Computing
PublisherData powered by TypesetSpringer Science and Business Media LLC
ISSN1868-5137
Open Access0