Cloud-based Non-intrusive Load Monitoring System (NILM)
Keh-Kim Kee1, Yun Seng Lim2, Jianhui Wong3, Kein Huat Chua4
1Keh-Kim Kee, University College of Technology Sarawak, Sibu, Sarawak, Malaysia.
2Yun Seng Lim, Universiti Tunku Abdul Rahman, Sungai Long Campus, Cheras, Selangor, Malaysia.
3Jianhui Wong, Universiti Tunku Abdul Rahman, Sungai Long Campus, Cheras, Selangor, Malaysia.
4Kein Huat Chua, Universiti Tunku Abdul Rahman, Sungai Long Campus, Cheras, Selangor, Malaysia.
Manuscript received on 27 September 2019 | Revised Manuscript received on 09 November 2019 | Manuscript Published on 22 November 2019 | PP: 125-131 | Volume-8 Issue-6S3 September 2019 | Retrieval Number: F10210986S319/19©BEIESP | DOI: 10.35940/ijeat.F1021.0986S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Design and development of a cloud-based non-intrusive load monitoring System (NILM) is presented. It serves for monitoring and disaggregating the aggregated data such as smart metering into appliance-level load information by using cloud computing and machine learning algorithms implemented in cloud. The existing NILM systems are lack of scalability and limited in computing resources (computation and data storage) due to dedicated, closed and proprietary-based characteristics. They are inaccessible to variety of heterogeneity data (electrical and non-electrical data) openly for improving NILM performance. Therefore, this paper proposed a novel cloud-based NILM system to enable collection of these open data for load monitoring and other energy-related services. The collected data such as smart meter or data acquisition unit (DAQ), is pre-processed and uploaded to the cloud platform. A classifier algorithm based on Artificial Neural Network (ANN) is implemented in Azure ML Studio (AzureML), followed by the classifier testing with different combinations of feature set for the performance comparison. Furthermore, a web service is deployed for web APIs (Application Programming Interfaces) of applications such as smart grid and smart cities. The results shows that the ANN classifier for multiclass classification has improved performance with additional features of harmonics, apart from active and reactive powers used. It also demonstrates the feasibility of proposed cloud-based classifier model for load monitoring. Therefore, the proposed solution offers a convenient and cost-effective way of load monitoring via cloud computing technology for smart grid and smart home applications. Further work includes the use of other ML algorithms for classifier, performance analysis, development of cloud-based universal appliance data and use cases.
Keywords: Non-Intrusive, Load Monitoring, Cloud Computing, Artificial Neural Network, Application Programming Interface.
Scope of the Article: Cloud Computing