Enhancing Short-Term Load Forecasting Through K-Shape Clustering and Deep Learning Integration
Objective
This project integrates K-Shape Clustering with Deep Learning, specifically a Multi-Layer Perceptron (MLP), for short-term load forecasting. By extracting patterns and leveraging neural networks, it enhances forecasting accuracy, optimizing grid management and improving energy system efficiency.
Abstract
Keywords:
Short-Term Load Forecasting, K-Shape Clustering, Deep Learning, Time-Series Clustering, Multi-Layer Perceptron (MLP), Energy Management, Power Demand Prediction, Feature Extraction, Grid Optimization, Neural Network Integration, Smart Grids, Energy Systems.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
SOFTWARE REQUIREMENS
Operating System : Windows 7/8/10
Server side Script : HTML, CSS, Bootstrap & JS
Programming Language : Python
Libraries :Flask, Pandas, Torch, Keras, Sklearn,Numpy , Seaborn
IDE/Workbench : VSCode
Server Deployment : Xampp Server
Database : MySQL
HARDWARE REQUIREMENTS
Processor - I3/Intel Processor
RAM - 8GB (min)
Hard Disk - 128 GB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - Any
Demo Video
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