Name
Title | ||
|---|---|---|
Accurately predicting building energy performance using evolutionary multivariate adaptive regression splines |
Abstract | ||
|---|---|---|
This paper proposes using evolutionary multivariate adaptive regression splines (EMARS), an artificial intelligence (AI) model, to efficiently predict the energy performance of buildings (EPB). EMARS is a hybrid of multivariate adaptive regression splines (MARS) and artificial bee colony (ABC). In EMARS, MARS addresses learning and curve fitting and ABC carries out optimization to determine the fittest parameter settings with minimal prediction error. The proposed model was constructed using 768 experimental datasets from the literature, with eight input parameters and two output parameters (cooling load (CL) and heating load (HL)). EMARS performance was compared against five other AI models, including MARS, back-propagation neural network (BPNN), radial basis function neural network (RBFNN), classification and regression tree (CART), and support vector machine (SVM). A 10-fold cross-validation approach found EMARS to be the best model for predicting CL and HL with 65% and 45% deduction in terms of RMSE, respectively, compared to other methods. Furthermore, EMARS is able to operate autonomously without human intervention or domain knowledge; represent derived relationship between response (HL and CL) with predictor variables associated with their relative importance. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.asoc.2014.05.015 | Applied Soft Computing |
Keywords | Field | DocType |
artificial bee colony,cooling load,artificial intelligence,multivariate adaptive regression splines,energy performance of buildings,heating load | Mars Exploration Program,Multivariate adaptive regression splines,Decision tree,Domain knowledge,Curve fitting,Support vector machine,Mean squared error,Artificial intelligence,Artificial neural network,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
22 | 1 | 1568-4946 |
Citations | PageRank | References |
10 | 1.04 | 13 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Min-Yuan Cheng | 1 | 174 | 19.84 |
| Minh-Tu Cao | 2 | 31 | 3.13 |