Machine learning for estimation of building energy consumption and performance:a review

Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy eciency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most eective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy eciency at a very early design stage. On the other hand, ecient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, articial intelligence (AI) in general and machine learning (ML) techniques in specic terms have been proposed for forecasting of building energy consumption and performance. This paperprovides a substantial review on the four main ML approaches including articial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance

The effect and results of the optimum insulation thickness on energy saving for deni̇zli̇

In the countries provide a major amount of their energy from abroad, using of the energy effectively and so obtaining of energy saving become more and more important. In this study, when the different energy sources (coal and fuel oil) were used for heating in the buildings in Denizli, optimum insulation thicknesses, energy savings and payback periods were calculated. Rock wool was used as the insulation material for the external walls. The optimum insulation thickness was calculated according to interest and inflation rates. The calculations were based on a life-cycle cost analysis (LCCA). When coal was used as an energy source, the optimum insulation thickness, the energy saving, and payback period were obtained 0.048 m, 42%, and 2.4 years, respectively