AC Cooling Time Prediction using Common Representation Model

• Published in: IEEE access 2020-01, Vol.8, p.1-1
• Main Authors: Kannan, Ramasamy, Roy, Manasij Sur
• Format: Article
• Language: English
• Subjects: AC Representation Learning; Air Conditioners; Air conditioning; Atmospheric modeling; Auxiliary Task Learning; Control data (computers); Cooling; Cooling Time Prediction; Data models; Deep Learning; Machine learning; Prediction models; Predictive models; Task analysis; Uncertainty
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3009467

The data generated by Air Conditioner (AC) consists mainly of sensor and control data. This paper will use the data generated from 53,528 ACs to predict the AC cooling time. The cooling time is the time taken by the AC to cool to a desired temperature. We have observed certain important issues in the data gathered from ACs deployed in dynamic real world environments. Poor prediction accuracies are observed for about 76% of the total ACs due to the lack of data regarding the device behavior, AC settings selection behavior and environmental conditions. During the AC operation, it is observed that the user selects only a small subset of the various combinations of the overall possible settings. Due to unavailability of data, Machine Learning (ML) models cannot be generated for new ACs. This leads to a cold start problem. This paper proposes a common AC prediction model that is generated through data shared from multiple connected ACs. Additionally an Auxiliary Task Learning (ATL) based deep learning model will be used for improving prediction accuracy. The proposed solution provides prediction capabilities for all ACs, compared to 24% of ACs supported by individual prediction models.