Evaluation of building energy demand forecast models using multi-attribute decision making approach

With the existence of several conventional and advanced building thermal energy demand forecast models to improve the energy efficiency of buildings, it is hard to find an appropriate, convenient, and efficient model. Evaluations based on statistical indexes (MAE, RMSE, MAPE, etc.) that characterize the accuracy of the forecasts do not help in the identification of the efficient building thermal energy demand forecast tool since they do not reflect the efforts entailed in implementation of the forecast model, i.e., data collection to production/use phase. Hence, this work presents a Gini Index based Measurement of Alternatives and Ranking according to COmpromise Solution (GI-MARCOS), a hybrid Multi Attribute Decision Making (MADM) approach for the identification of the most efficient building energy demand forecast tool. GI-MARCOS employs (i) GI based objective weight method: assigns meaningful objective weights to the attributes in four phases (1: pre-processing, 2: implementation, 3: post-processing, and 4: use phase) thereby avoiding unnecessary biases in the expert's opinion on weights and applicable to domains where there is a lack of domain expertise, and (ii) MARCOS: provides a robust and reliable ranking of alternatives in a dynamic environment. A case study with three alternatives evaluated over three to six attributes in four phases of implementation (pre-processing, implementation, post-processing and use) reveals that the use of GI-MARCOS improved the accuracy of alternatives MLR and BM by 6% and 13%, respectively. Moreover, additional validations state that (i) MLR performs best in Phase 1 and 2, while ANN performs best in Phase 3 and 4 with BM providing a mediocre performance in all four phases, (ii) sensitivity analysis: provides robust ranking with interchange of weights across phases and attributes, and (iii) rank correlation: ranks produce by GI-MARCOS has a high correlation with GRA (0.999), COPRAS (0.9786), and ARAS (0.9775).