Virtual Sensing of Temperatures in Indoor Environments: A Case Study
| Autor: | Angelo Montanari, Andrea Urgolo, Martin Kraft, Andrea Brunello, Federico Pittino |
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| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Computer science particle filters Real-time computing Context (language use) 02 engineering and technology 010501 environmental sciences 01 natural sciences Temperature measurement law.invention 020901 industrial engineering & automation law virtual sensing machine learning neural networks particle filters Baseline (configuration management) 0105 earth and related environmental sciences SIMPLE (military communications protocol) business.industry Deep learning virtual sensing Ranging neural networks Thermostat machine learning Artificial intelligence business Efficient energy use |
| Zdroj: | ICDM (Workshops) |
| DOI: | 10.1109/icdmw51313.2020.00117 |
| Popis: | Real-time measurement of temperatures in indoor environments is important for several reasons, among which we mention the maintenance of comfort levels, the satisfaction of legal requirements, and the energy efficiency. However, placing a sufficient number of sensors at the required locations to guarantee a uniform monitoring of the temperature in a given premise may be difficult, with the result that typically just one or a few sensors are deployed. This is the case, for instance, with thermostats in buildings. Virtual sensing is a technique by which values from physical sensors are replaced by those obtained from virtual ones, which take readings from real sensors and calculate their outputs by means of some process models. In this paper, we consider the case study of temperature monitoring in an open office at Silicon Austria Labs, in Villach (Austria). We perform a comprehensive evaluation of various techniques for the prediction of temperatures recorded by physical sensors on the basis of other sensors, ranging from simple baseline methodologies to more complex classical machine learning and deep learning approaches that allow one to take into account temporal and spatio-temporal relationships in the data. The outcome is that, in this context, it is possible to reach a satisfactory prediction performance by using relatively simple models. |
| Databáze: | OpenAIRE |
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