@article{GautamZhangLandwehretal.2021,
  author    = {Gautam, Khem Raj and Zhang, Guoqiang and Landwehr, Niels and Adolphs, Julian},
  title     = {Machine learning for improvement of thermal conditions inside a hybrid ventilated animal building},
  series = {Computers and electronics in agriculture : COMPAG online ; an international journal},
  volume    = {187},
  journal   = {Computers and electronics in agriculture : COMPAG online ; an international journal},
  publisher = {Elsevier Science},
  address   = {Amsterdam [u.a.]},
  issn      = {0168-1699},
  doi       = {10.1016/j.compag.2021.106259},
  pages     = {10},
  year      = {2021},
  abstract  = {In buildings with hybrid ventilation, natural ventilation opening positions (windows), mechanical ventilation rates, heating, and cooling are manipulated to maintain desired thermal conditions. The indoor temperature is regulated solely by ventilation (natural and mechanical) when the external conditions are favorable to save external heating and cooling energy. The ventilation parameters are determined by a rule-based control scheme, which is not optimal. This study proposes a methodology to enable real-time optimum control of ventilation parameters. We developed offline prediction models to estimate future thermal conditions from the data collected from building in operation. The developed offline model is then used to find the optimal controllable ventilation parameters in real-time to minimize the setpoint deviation in the building. With the proposed methodology, the experimental building's setpoint deviation improved for 87\% of time, on average, by 0.53 degrees C compared to the current deviations.},
  language  = {en}
}
@article{HempelAdolphsLandwehretal.2020,
  author    = {Hempel, Sabrina and Adolphs, Julian and Landwehr, Niels and Willink, Dilya and Janke, David and Amon, Thomas},
  title     = {Supervised machine learning to assess methane emissions of a dairy building with natural ventilation},
  series = {Applied Sciences},
  volume    = {10},
  journal   = {Applied Sciences},
  number    = {19},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2076-3417},
  doi       = {10.3390/app10196938},
  pages     = {21},
  year      = {2020},
  abstract  = {A reliable quantification of greenhouse gas emissions is a basis for the development of adequate mitigation measures. Protocols for emission measurements and data analysis approaches to extrapolate to accurate annual emission values are a substantial prerequisite in this context. We systematically analyzed the benefit of supervised machine learning methods to project methane emissions from a naturally ventilated cattle building with a concrete solid floor and manure scraper located in Northern Germany. We took into account approximately 40 weeks of hourly emission measurements and compared model predictions using eight regression approaches, 27 different sampling scenarios and four measures of model accuracy. Data normalization was applied based on median and quartile range. A correlation analysis was performed to evaluate the influence of individual features. This indicated only a very weak linear relation between the methane emission and features that are typically used to predict methane emission values of naturally ventilated barns. It further highlighted the added value of including day-time and squared ambient temperature as features. The error of the predicted emission values was in general below 10\%. The results from Gaussian processes, ordinary multilinear regression and neural networks were least robust. More robust results were obtained with multilinear regression with regularization, support vector machines and particularly the ensemble methods gradient boosting and random forest. The latter had the added value to be rather insensitive against the normalization procedure. In the case of multilinear regression, also the removal of not significantly linearly related variables (i.e., keeping only the day-time component) led to robust modeling results. We concluded that measurement protocols with 7 days and six measurement periods can be considered sufficient to model methane emissions from the dairy barn with solid floor with manure scraper, particularly when periods are distributed over the year with a preference for transition periods. Features should be normalized according to median and quartile range and must be carefully selected depending on the modeling approach.},
  language  = {en}
}