Particle dynamics and airflow patterns in a ventilated two-zone enclosure

Understanding the transport mechanisms of aerosol particles in enclosures has broad ramifications in the context of cleaning strategies, and health risk assessment (e. g., occupational exposure). This paper addresses airflow pattern and aerosol transport mechanism in a ventilated two-zone enclosure with the outlet (exhaust location) situated at different locations. A numerical approach that combines a Eulerian simulation of turbulent flow with a Lagrangian particle-tracking algorithm is used. Simulations are carried out using solid suspensions with different sizes (1 to 100 micron) and densities (240 and 2300 kg/m3). The effect of location of the outlet (exhaust) on airflow patterns and aerosol dynamics is analyzed and quantified.

GA-ANN Short-Term Electricity Load Forecasting

This paper presents a methodology for short-term load forecasting based on genetic algorithm feature selection and artificial neural network modeling. A feed forward artificial neural network is used to model the 24-h ahead load based on past consumption, weather and stock index data. A genetic algorithm is used in order to find the best subset of variables for modeling. Three data sets of different geographical locations, encompassing areas of different dimensions with distinct load profiles are used in order to evaluate the methodology. The developed approach was found to generate models achieving a minimum mean average percentage error under 2 %. The feature selection algorithm was able to significantly reduce the number of used features and increase the accuracy of the models.