ProgramsView all programs
From the news
- Chancellor Amma Addresses the Parliament of World’s Religions
- Amrita Students Qualify for the European Mars Rover Challenge
Publication Type : Journal Article
Publisher : IOS Press
Source : Journal of Intelligent & Fuzzy Systems (IOS Press), vol. 40, no. 1, pp. 745-757, 2021.
Keywords : Day-ahead tariff, decision tree, home energy management, random forest, support vector machine
Campus : Chennai
School : School of Engineering
Center : Amrita Innovation & Research
Department : Electronics and Communication
Verified : Yes
Year : 2021
Abstract : Day-ahead electricity tariff prediction is advantageous for both consumers and utilities. This article discusses the home energy management (HEM) scheme consisting of an electricity tariff predictor and appliance scheduler. The random forest (RF) technique predicts a short-term electricity tariff for the next 24 hours using the past three months of electricity tariff information. This predictor provides the tariff information to schedule the appliances at the most preferred time slot of a consumer with minimum electricity tariff, aiming high consumer comfort and low electricity bill for consumers. The proposed approach allows a user to be aware of their demand and their comfort. The proposed approach makes use of present-day (D) tariff and immediate previous 30 days (D-1, D-2, ... , D-30) of tariff information for training achieves minimum error values for next day electricity tariff prediction. The simulation results demonstrate the benefits of the RF approach for tariff prediction by comparing it with the support vector machine (SVM) and decision tree (DT) predicted tariffs against the actual tariff, provided by the utility day-ahead. The outcomes indicate that the RF produces the best results compared to SVM and DT predictions for performance metrics and end-user comfort.
Cite this Research Publication : Ganesh Kumar Chellamani, M. Firdouse Ali Khan, and Premanand Venkatesh Chandramani, “Supervised Electricity Tariff Prediction using Random Forest validated through User Comfort and Constraint for a Home Energy Management Scheme”, Journal of Intelligent & Fuzzy Systems (IOS Press), vol. 40, no. 1, pp. 745-757, 2021.