Supporting Predictive Models Results Interpretation for Comfortable Workplaces

Kiln October 10, 2018 12:00 - 12:15

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Iker Esnaola-Gonzalez, Jesús Bermúdez, Izaskun Fernandez and Aitor Arnaiz.  

Abstract:  Approximately 90% of people spend most of their time in buildings, so feeling comfortable while staying indoors is a must. Although many times being an overlooked factor, research has proved that having an uncomfortable thermal situation involves many risks including clinical diseases, health impairments, and reduced human performance and work capacity. Therefore, there is a need to establish HVAC (Heating, Ventilation and Air Conditioning) control strategies that ensure comfortable thermal situations in these environments. KDD (Knowledge Discovery in Databases) processes may be applied by data analysts to create predictive models that identify optimal HVAC control strategies that will ensure thermal comfort within a workplace. The EEPSA (Energy Efficiency Prediction Semantic Assistant) process assists data analysts through this KDD process, which can be arduous and very time-consuming when there is a lack of sufficient domain knowledge. For that purpose, it takes leverage of the Semantic Technologies such as the EEPSA ontology which aims to capture all the necessary expert knowledge related to buildings, sensing and actuating devices, and their corresponding observations and actuations. EROSO (thERmal cOmfort SOlution) is a framework that combines KDD processes and Semantic Technologies for ensuring thermal comfort in workplaces. Specifically, EROSO supports the KDD's Interpretation phase where Semantic Technologies are used to obtain an explanation of predictive model's temperature predictions with regards to the thermal comfort regulations they satisfy. Furthermore, this result interpretation supports facility managers in the task of selecting the optimal HVAC control strategies.

Keywords:  Semantic Technologies;  Knowledge Discovery in Databases (KDD);  Thermal Comfort