Discovering Human Activity Patterns Using Smart Meter Data
DS Bhupal Naik1, D. Venkatesulu2, V Ramakrishna Sajja3, A Sridevi4
1DS Bhupal Naik, Department of Computer Science and Engineering, VFSTR deemed to be University, Vadlamudi, Guntur (Andhra Pradesh), India.
2Dr. D. Venkatesulu, Department of Computer Science and Engineering, VFSTR deemed to be University, Vadlamudi, Guntur (Andhra Pradesh), India.
3V Ramakrishna Sajja, Department of Computer Science and Engineering, VFSTR deemed to be University, Vadlamudi, Guntur (Andhra Pradesh), India.
4A Sridevi, Department of Commerce, Government Degree College, Bhoopalpally (Telangana), India.
Manuscript received on 12 February 2019 | Revised Manuscript received on 02 March 2019 | Manuscript Published on 08 June 2019 | PP: 45-48 | Volume-7 Issue-5S4, February 2019 | Retrieval Number: E10090275S419/19©BEIESP
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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: Population lived in rural zone contributes to 68% and urban zones contribute to 32% of the total world population. According to 1995 census, the proportion of rural to urban population of the world was 55% and 45% respectively. By 2025, the increase in the urban population (59%) ratio would be drastic raise to the rural population (41%). The statistics shows that, most of the citizens are moving from rural to urban areas and habituated to the smart technology and least bother about their health. Health care services are a standout amongst the most difficult viewpoints that is extraordinarily influenced by the colossal surge of individuals to city culture. Consequently, urban communities around the globe are putting vigorously in advanced change with an end goal to give more advantageous to individuals. In such a change, a huge number of homes are being furnished with smart gadgets (e.g., smart meters, sensors, etc.,). A well-being health care application is proposed using smart meter data for discovering human activity patterns. A frequent pattern growth algorithm, K-means algorithm and Network aggregator is used to measure and analyze the energy usage by occupants’ behaviour.
Keywords: Smart Meter, Smart Technology, FP Growth, K Means, Network Aggregator, Healthcare Application.
Scope of the Article: Software Design Patterns