ABSTRACT: Environmental conditions in broiler houses, specifically temperature, are key factors that should be controlled to ensure appropriate environment for broiler rearing. In countries with tropical/subtropical climate, like Brazil, high temperatures produce heat stress to animals, affecting the production process. This research proposes a real-time model to control temperature inside broiler houses. The controller is a self-correcting model that makes real-time decisions on the ventilation system operation (exhaust fans) together with temperature prediction at the facility. The model involves partial differential equations (PDE) whose parameters are updated according to data registered in real-time. Some experiments were carried out at a pilot farm in the municipality of Jundiaí, São Paulo State, Brazil, for different periods during winter and summer. The results based on simulations in comparison with the current automatic ventilation system show that the model is consistent to keep temperature under control for an efficient production. The model achieved a bias of 0.6 °C on average in comparison with the ideal temperature, whereas the automatic controller measured a bias of 3.3 °C, respectively. Future lines suggest that this approach could be useful in many other situations that involve environmental control for livestock production.

ABSTRACT Ventilation systems are incorporated at intensive poultry farms to control environment conditions and thermal comfort of broilers. The ventilation system operates based on environmental data, particularly measured by sensors of temperature and relative humidity. Sensors are placed at different positions of the facility. Quality, number and positioning of the sensors are critical factors to achieve an efficient performance of the system. For this reason, a strategic positioning of the sensors associated to controllers could support the maintenance and management of the microclimate inside the facility. This research aims to identify the three most representative points for the positioning of sensors in order to support the ventilation system during the critical period from 12h00 to 15h00 on summer days. Temperature, relative humidity and wind speed were measured in four different tunnel ventilated barns at the final stage of the production cycle. The descriptive analysis was performed on these data. The Temperature and Humidity Index (THI) was also calculated. Then, the geostatistical analysis of THI was performed by GS+ and the position of sensors was determined by ordinary kriging. The methodology was able to detect the most representative points for the positioning of sensors in a case study (southeastern Brazil). The results suggested that this strategic positioning would help controllers to obtain a better inference of the microclimate during the studied period (the hottest microclimate), considered critical in Brazil. In addition, these results allow developing a future road map for a decision support system based on 24 h monitoring of the ventilation systems in broiler houses.

ABSTRACT Clustering is commonly used to group data in order to represent the behaviour of a system as accurately as possible by obtaining patterns and profiles. In this paper, clustering is applied with partitioning-clustering techniques, specifically, Partitioning around Medoids (PAM) to analyse load curves from a city of South-eastern Brazil in São Paulo state. A top-down approach in time granularity is performed to detect and to label profiles which could be affected by seasonal trends and daily/hourly time blocks. Time-granularity patterns are useful to support the improvement of activities related to distribution, transmission and scheduling of energy supply. Results indicated four main patterns which were post-processed in hourly blocks by using shades of grey to help final-user to understand demand thresholds according to the meaning of dark grey, light grey and white colours. A particular and different behaviour of load curve was identified for the studied city if it is compared to the classical behaviour of urban cities.