Citation: Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 15, pages 697–700 (2018)
Abstract. Industrial administrators are promoting approaches to improve energy efficiency and developing smart homes and appliances. Development of green technology requires accurate models. Real-time Measurement and Verification (M\&V) procedure is used to quantify energy performance. It is conducted through short-term on-site measurements and engineering calculation. The period of this procedure lasts for several months or up to a year so the failure to immediately detect abnormal energy efficiency decreases energy performance so timely correction of appliances will be unable and the opportunity to adjust or repair them will be missed. In this study, a cloud computing platform is established to measure the washing machine energy consumption parameters and calculate energy savings which consist of load sensors and fuzzy control. Time-series data are transmitted to the cloud environment through the network and saved in databases. On this platform, for constructing accurate models, integration of the particle swarm optimization (PSO), M\&V methodologies and multivariate regression analysis are used. After uploading energy consumption data directly, pre-installation energy baseline model is created and post-installation real-time energy performance calculation is obtained. Observing fluctuations of washing machine energy consumptions provides real-time monitoring or correction of the operating performance of the appliance or system and then good energy performance can be obtained. The aim of this study is to gain real-time and long-term energy performance information and automatic calculations of energy savings on washing machines. Using this cloud platform for home appliances could help the manufacturers to promote energy efficiency programs on smart appliances.
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