Citation:

H. Jeyalakshmi, M. Mariammal, 2023. "Smart Electricity Demand Forcasting by Using Improved LSTM Algorithm" ESP Journal of Engineering & Technology Advancements  3(2): 65-71.

Abstract:

Demand forcasting, which concerns the estimation of future electricity demand, is needed for the operation and management of power systems. Effective Electricity demand forecasting can relieve the conflict between power supply and demand. Furthermore, effective load forecasting can improve the efficiency of power stations and ensure the safety of the grid. It is suggested that a reduction of a few percentage points in prediction accuracy would have significant cost impact on companies operating in highly competitive power markets. In our project we are going to forecast the electricity demand by using a Deep learning algorithm which is called as Improved Long Short Term Memory. By using Improved LSTM we can able to get accurate Future predicted output.

References:

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[5] Nitin Sonaji Magar, Zafar Ul Hasan, Anand B.Humbe, 2023. "Design and Development of Optimized Cardiovascular Disease Prediction Model using Artificial Intelligence" ESP International Journal of Advancements In Science & Technology (ESP- IJAST) Volume 1, Issue 2 : 20-27
[6] Ameer Baig, Shum Sundar Bachu,Vejandla Meghana, M. Ravi Kumar, 2023. "Smart Irrigation System Using IoT" ESP International Journal of Communication Engineering & Electronics Technology (ESP- IJCEET) Volume 1, Issue 1 : 14-21

Keywords:

Long Short Term Memory, Forcasting, Power Supply, Demand, Effective Load Forcasting.