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...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-06-01
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| Series: | Energy and Built Environment |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666123323000132 |
| _version_ | 1827794083637100544 |
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| author | Nivethitha Somu Anupama Kowli |
| author_facet | Nivethitha Somu Anupama Kowli |
| author_sort | Nivethitha Somu |
| collection | DOAJ |
| description | 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). |
| first_indexed | 2024-03-11T18:27:42Z |
| format | Article |
| id | doaj.art-3078f1cff1a444388cc91e909dc6a737 |
| institution | Directory Open Access Journal |
| issn | 2666-1233 |
| language | English |
| last_indexed | 2024-03-11T18:27:42Z |
| publishDate | 2024-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Energy and Built Environment |
| spelling | doaj.art-3078f1cff1a444388cc91e909dc6a7372023-10-13T13:56:24ZengKeAi Communications Co., Ltd.Energy and Built Environment2666-12332024-06-0153480491Evaluation of building energy demand forecast models using multi-attribute decision making approachNivethitha Somu0Anupama Kowli1Corresponding author.; Department of Electrical Engineering, Indian Institute of Technology-Bombay, Mumbai, Maharashtra 400076, IndiaDepartment of Electrical Engineering, Indian Institute of Technology-Bombay, Mumbai, Maharashtra 400076, IndiaWith 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).http://www.sciencedirect.com/science/article/pii/S2666123323000132Building energy demandMulti-attribute decision makingObjective weightsForecast modelsSensitivity analysis |
| spellingShingle | Nivethitha Somu Anupama Kowli Evaluation of building energy demand forecast models using multi-attribute decision making approach Energy and Built Environment Building energy demand Multi-attribute decision making Objective weights Forecast models Sensitivity analysis |
| title | Evaluation of building energy demand forecast models using multi-attribute decision making approach |
| title_full | Evaluation of building energy demand forecast models using multi-attribute decision making approach |
| title_fullStr | Evaluation of building energy demand forecast models using multi-attribute decision making approach |
| title_full_unstemmed | Evaluation of building energy demand forecast models using multi-attribute decision making approach |
| title_short | Evaluation of building energy demand forecast models using multi-attribute decision making approach |
| title_sort | evaluation of building energy demand forecast models using multi attribute decision making approach |
| topic | Building energy demand Multi-attribute decision making Objective weights Forecast models Sensitivity analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2666123323000132 |
| work_keys_str_mv | AT nivethithasomu evaluationofbuildingenergydemandforecastmodelsusingmultiattributedecisionmakingapproach AT anupamakowli evaluationofbuildingenergydemandforecastmodelsusingmultiattributedecisionmakingapproach |