Large Scale Predictive Analysis for Real-Time Energy Management
K. Sateesh Kumar1, D. Krishna2, Ch. Himabindu3
1K. Sateesh Kumar, Assistant Professor, Department of EEE, Anurag Group of Institutions, Venkatapur(V), Hyderabad (Tamil Nadu), India.
2D. Krishna, Assistant Professor, Department of EEE, Anurag Group of Institutions, Venkatapur(V), Hyderabad (Tamil Nadu), India.
3Ch.Himabindu, M.Tech Student, Department of EEE, Anurag Group of Institutions, Venkatapur(V), Hyderabad (Tamil Nadu), India.
Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 392-395 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6162048419/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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 recent days consumption of energy is high. As population increases gradually, usage of power is also increasing. In this paper the energy management is predicted by using the LSTM Algorithm and to manage the real-time energy. The data is given in the form of data sets. It has been observed that large amount of energy required in future to train data depending on the consumed energy in future. It can observe training progress in the real time environment. To estimate future event in this paper considered energy consumed per day around 440 values and iterations are about 250. The results are validated by using MATLAB/Simulink
Keywords: Time-Series Analysis, Curve-Fitting Loop, Rmse, Lstm Network Algorithm, Ann.
Scope of the Article: Predictive Analysis