IoT and Data Research in Industrial Power Management
B. Pavitra1, D.Narendhar Singh2
1B.Pavitra, Assistant Professor, Department of ECE, Anurag Group of Institutions, Hyderabad (Telangana), India.
2D.Narendhar Singh, Assistant Professor, Department of ECE, Anurag Group of Institutions, Hyderabad (Telangana), India.
Manuscript received on 15 August 2019 | Revised Manuscript received on 27 August 2019 | Manuscript Published on 06 September 2019 | PP: 57-60 | Volume-8 Issue- 6S, August 2019 | Retrieval Number: F10110886S19/19©BEIESP | DOI: 10.35940/ijeat.F1011.0886S19
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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: IoT plays an important role in collecting data and machine learning for prediction in variety of applications like homecare, healthcare and energy management. In energy management there are various variables such as future power demands, generation status weather conditions and current battery status hard to expect high efficiency. Here, in this proposed idea, for higher efficiency of renewable energy, an IOT system is needed to monitor and collect these Statuses and provide energy management services. Energy will be consumed of passive operation according to hourly variation in price and battery status will be predicted by using machine learning algorithms like Logistic regression, SVM, and k-NN. We trained the system by considering five random variables in datasheet such as Current time, Current cost, predicted time, predicted cost and Solar battery status from the device. This integrated system is used for uploading power related details of Grid and Solar to IBM cloud. Depending on previous datasheet, analytics will be done by resulting which source has to be triggered to drive the load either Solar or Grid. APIs and NodeRed Tool were used for wiring sensor data and Model predicted output. In future power demands, this design will help to predict the price according to hourly variation based on the units and to trigger the source.
Keywords: ML, Logistic Regression, SVM, k-NN, Node Red Tool, Esp8266.
Scope of the Article: IoT