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 effectiveness of a school-based substance abuse prevention program: EU-Dap cluster randomised controlled trial

OBJECTIVE: To evaluate the effectiveness of the school-based drug abuse prevention program developed in the EU-Dap study (EUropean Drug Abuse Prevention trial) in preventing the use of tobacco, alcohol and drugs at the post-test. METHODS: Cluster Randomised Controlled Trial. Seven European countries participated in the study; 170 schools (7079 pupils 12-14 years of age) were randomly assigned to one of three experimental conditions or to a control condition during the school year 2004/2005. A pre-test survey assessing past and current substance use was conducted before the implementation of the program. The program consisted in 12-hour class-based curriculum based on a comprehensive social-influence approach. A post-test survey was carried out in all participating schools, 3 months after the end of the program. The association between program condition and change in substance use at post-test was expressed as adjusted Prevalence Odds Ratio (POR), estimated by multilevel regression model. RESULTS: Program effects were found for daily cigarette smoking (POR=0.70; 0.52-0.94) and episodes of drunkenness in the past 30 days (POR=0.72; 0.58-0.90 for at least one episode, POR=0.69; 0.48-0.99 for three or more episodes), while effects on Cannabis use in the past 30 days were of marginal statistical significance (POR=0.77; 0.60-1.00). The curriculum was successful in preventing baseline non-smokers or sporadic smokers from moving onto daily smoking, but it was not effective in helping baseline daily smokers to reduce or stop smoking. CONCLUSION: School curricula based on a comprehensive social-influence model may delay progression to daily smoking and episodes of drunkenness

Plasma Diagnostics of the Interstellar Medium with Radio Astronomy

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