Hybrid Models for Adaptive Allocation of Electricity for Households
Midhush Manohar T.K.1, Naveen Suresh2, Srikumar Subramanian3, Gowri Srinivasa4
1Midhush Manohar T.K., Department of Computer Science and Engineering, PES University RR Campus, Bangalore (Karnataka), India.
2Naveen Suresh, Department of Computer Science and Engineering, PES University RR Campus, Bangalore (Karnataka), India.
3Srikumar Subramanian, Department of Computer Science and Engineering, PES University RR Campus, Bangalore (Karnataka), India.
4Gowri Srinivasa, PES Center for Pattern Recognition, Department of Computer Science and Engineering, PES University EC Campus, Bangalore (Karnataka), India.
Manuscript received on 05 December 2019 | Revised Manuscript received on 13 December 2019 | Manuscript Published on 31 December 2019 | PP: 369-376 | Volume-9 Issue-2S December 2019 | Retrieval Number: B10291292S19/2019©BEIESP | DOI: 10.35940/ijitee.B1029.1292S19
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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: In this paper, we analyze, model, predict and cluster Global Active Power, i.e., a time series data obtained at one minute intervals from electricity sensors of a household. We analyze changes in seasonality and trends to model the data. We then compare various forecasting methods such as SARIMA and LSTM to forecast sensor data for the household and combine them to achieve a hybrid model that captures nonlinear variations better than either SARIMA or LSTM used in isolation. Finally, we cluster slices of time series data effectively using a novel clustering algorithm that is a combination of density-based and centroid-based approaches, to discover relevant subtle clusters from sensor data. Our experiments have yielded meaningful insights from the data at both a micro, day-to-day granularity, as well as a macro, weekly to monthly granularity.
Keywords: Time Series, Forecasting, SARIMA, LSTM, RNN, Clustering.
Scope of the Article: Adaptive Systems