Federated Learning with XGBoost and LSTM for Electric Vehicle Energy Demand Prediction

S Suneel; T. RajyaLakshmi

Federated Learning with XGBoost and LSTM for Electric Vehicle Energy Demand Prediction

Authors: S Suneel, T. RajyaLakshmi

Unique Paper ID: 175777
Volume: 11
Issue: 11
PageNo: 4668-4671

Keywords: Electric Vehicles (EVs) Blockchain Federated Learning Energy Demand Prediction Blockchain-Federated Learning (BCFL) Linear Regression Power Grid Machine Learning Model

Abstract:
The growing popularity of electric vehicles (EVs) is driven by their advantages over traditional gas-powered cars, but integrating them into the power grid poses challenges like increased energy demand and peak loads. To address this, we propose a blockchain-based federated learning approach for predicting EV energy demands using linear regression algorithms. Data from EVs is securely stored on the blockchain, accessible only by authorized users. Each EV trains a machine learning model using federated learning, and the resulting model parameters are shared on the blockchain. Our approach also tackles communication delays and overheads within the blockchain-federated learning (BCFL) framework, optimizing system performance. Results demonstrate the efficiency of our method in predicting EV energy needs accurately.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 11
PageNo: 4668-4671

Federated Learning with XGBoost and LSTM for Electric Vehicle Energy Demand Prediction

Available:https://ijirt.org/Article?manuscript=175777

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