Semisupervised refrigeration plant cooling disaggregation by means of deep neural network ensemble
| Autor: | Juan Antonio Ortega, Jesus A. Carino, Daniel Zurita, Josep Cirera |
|---|---|
| Přispěvatelé: | Universitat Politècnica de Catalunya. Doctorat en Enginyeria Electrònica, Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica, Universitat Politècnica de Catalunya. MCIA - Motion Control and Industrial Applications Research Group |
| Rok vydání: | 2019 |
| Předmět: |
Artificial intelligence
Load modeling Test bench Artificial neural network Computer science Intel·ligència artificial 020209 energy Refrigeration plant 0211 other engineering and technologies 02 engineering and technology Fault detection and isolation Reliability engineering 021105 building & construction Cooling power 0202 electrical engineering electronic engineering information engineering Semisupervised learning Informàtica::Intel·ligència artificial [Àrees temàtiques de la UPC] Instrumentation (computer programming) Multi-layer neural network Cooling Energy (signal processing) |
| Zdroj: | ISIE UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) Recercat. Dipósit de la Recerca de Catalunya instname |
| Popis: | The awareness of the energy usage has become a recurrent topic during the last decades. Identifying the end-use energy of each individual device can lead to a substantial improvement in efficiency and fault detection. The cost of instrumentation and especially the ones which involve fluids, makes the monitoring unfeasible. Hereby, the necessity of Non-Intrusive Load Monitoring (NILM) techniques has increased in order to avoid the aforementioned associated costs. In this paper, the cooling power of a refrigeration plant is disaggregated to identify the cooling power spent in each compartment. A data-driven methodology based on a semisupervised deep neural network ensemble is presented, which takes advantage of the data acquired from the typical installed sensors in a refrigeration plant. The proposed strategy is able to disaggregate accurately the cooling power without the necessity of introducing any additional sensing device in the installation. The proposed methodology is validated with a test bench simulation and also with real refrigeration plant data. |
| Databáze: | OpenAIRE |
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