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Lasshof, B and Stoy, C (2016) Estimation models for heating energy and electricity costs. Construction Management and Economics, 34(09), 622-40.
- Type: Journal Article
- Keywords: life cycle costs; cost estimation; energy costs; running costs; regression analysis; construction; cost estimates; heating
- ISBN/ISSN: 0144-6193
- URL: https://doi.org/10.1080/01446193.2016.1190025
- Abstract:
Heating energy and electricity represent a significant proportion of the overall financial expenditures over the life cycle of buildings and play a decisive role in the assessment of ecological quality. To ensure ecological and economical sustainability, the planning and construction of energy efficient buildings have become increasingly important. The present study introduces statistical models for the estimation of energy costs during the planning process as well as for the purpose of monitoring and controlling costs during operation including a wide range of facility types. Based on a data sample of 206 occupied facilities located in Germany, regression models are presented in detail including significant predictor variables. The provision of correctly specified models as well as a consistent and unbiased estimation beyond the employed data is verified by validation tests. The practical significance of the determined variables is presented by the calculation of various scenarios. The best estimation accuracy is indicated for regression models with transformed response and predictor variables achieving mean absolute percentage errors of 20.5% (heating energy) and 20.7% (electricity), respectively. An extension of the data sample, a consideration of further facility types, and an implementation of additional statistical methods may be considered by future approaches to improve the estimation accuracy.; Heating energy and electricity represent a significant proportion of the overall financial expenditures over the life cycle of buildings and play a decisive role in the assessment of ecological quality. To ensure ecological and economical sustainability, the planning and construction of energy efficient buildings have become increasingly important. The present study introduces statistical models for the estimation of energy costs during the planning process as well as for the purpose of monitoring and controlling costs during operation including a wide range of facility types. Based on a data sample of 206 occupied facilities located in Germany, regression models are presented in detail including significant predictor variables. The provision of correctly specified models as well as a consistent and unbiased estimation beyond the employed data is verified by validation tests. The practical significance of the determined variables is presented by the calculation of various scenarios. The best estimation accuracy is indicated for regression models with transformed response and predictor variables achieving mean absolute percentage errors of 20.5% (heating energy) and 20.7% (electricity), respectively. An extension of the data sample, a consideration of further facility types, and an implementation of additional statistical methods may be considered by future approaches to improve the estimation accuracy.;
Sacks, R (2016) What constitutes good production flow in construction?. Construction Management and Economics, 34(09), 641-56.
Saunders, F C, Sherry, A H and Gale, A W (2016) Dualities and dilemmas: Contending with uncertainty in large-scale safety-critical projects. Construction Management and Economics, 34(09), 657-75.
Townsend, K, Loudoun, R and Markwell, K (2016) The role of line managers in creating and maintaining healthy work environments on project construction sites. Construction Management and Economics, 34(09), 611-21.