Performance analysis of demand forecasting in energy consumption based on ensemble model

• Main Authors: Jaganathan, D. (Author), Natarajan, A. (Author)
• Format: Article
• Language: English
• Published: Institute of Advanced Engineering and Science 2022
• Subjects: Ensemble model; K-nearest neighbors; Logistic regression; Machine learning; Random forest; Southern regional load dispatch centre
• Online Access: View Fulltext in Publisher

 Over the previous decade, energy usage has increased exponentially all over the world. The machine learning algorithms are used to classify the demand and requirement of off and evening peak load of southern regional load dispatch centre (SRLDC) data. In this paper, data are classified based on demand and requirement of both evening and off peak of day wise southern regional grid of Andhra Pradesh, Karnataka, Kerala, Tamilnadu, and Pondicherry of different states are proposed. The machine learning algorithms like k-nearest neighbors (KNN), random forest, and logistic regression have been adopted to classify the model. To improve this model efficiency, an ensemble learning method is used to increase the accuracy. The performance measure of state-wise outcome is determined by classifying its demand and requirement needs over its state energy consumption and with different classification algorithms and it is improved by using a combined method of ensemble model with accuracy of 86%. © 2022, Institute of Advanced Engineering and Science. All rights reserved.