Semantic Prediction Assistant Approach applied to Energy Efficiency in Tertiary Buildings

Tracking #: 1609-2821

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Iker Esnaola-Gonzalez
Jesús Bermúdez
Izaskun Fernandez
Aitor Arnaiz

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Guest Editors ST Built Environment 2017

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Full Paper
Fulfilling occupants' comfort whilst reducing energy consumption is still an unsolved problem in most of tertiary buildings. However, the expansion of the Internet of Things (IoT) and Knowledge Discovery in Databases (KDD) techniques lead to research this matter. In this paper the EEPSA (Energy Efficiency Prediction Semantic Assistant) process is presented, which takes leverage of the Semantic Web Technologies (SWT) to enhance the KDD process for achieving energy efficiency in tertiary buildings while maintaining comfort levels. This process guides the data analyst through the different KDD phases in a semi-automatic manner and supports prescriptive HVAC system activation strategies. That is, temperature of a space is predicted simulating the activation of HVAC systems at different time and intensities, so that the facility manager can choose the strategy that best fits both the user's comfort needs and energy efficiency. The proposed solution is abstract enough to reuse it in similar use-cases of the same domain and it has been proved that improves the accuracy of predictions.
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