Energy Pooling Market Price Forecasting using Machine Learning

Y.Shanmukha Manohara Reddy; B.Srilatha; Ch.Yaswanth; P.Dilip

doi:10.36948/ijfmr.2024.v06i03.19133

Energy Pooling Market Price Forecasting using Machine Learning

Author(s)	Y.Shanmukha Manohara Reddy, B.Srilatha, Ch.Yaswanth, P.Dilip
Country	India
Abstract	In competitive electricity markets, accurate price forecasting is required to both power producers and consumers for planning their bidding strategies in order to maximize their own benefits. Price classification is an alternative approach to fore casting where the exact values of future prices are not mandatory. Presently, two efficient algorithms are proposed for both short term price forecasting (STPF) and classification (STPC) purposes. The algorithms include various methodologies like wavelet trans form (WT), fuzzy adaptive particle swarm optimization (FA-PSO) and feed forward neural networks (FFNN). WT is utilized to con vert the pathetic price series to an inviolable price series without losing the originality in the signal. Standard PSO (C) is implemented to tune the fixed architecture FFNN weights and biases. In the present nonlinear problem, linear variation of inertia weight does not resemble exact search process. Hence, dynamic inertia weight is accomplished by implementing the fuzzy systems in the GRADIENTBOOSTINGREGRESSOR approach. The hybrid methodology is implemented on Spanish electricity markets for the year 2002. To validate, three types of price classes and historical price series that are utilized by many researches as input features, are considered. Various statistical indicators are evaluated to compare and validate the proposed approaches with the past approaches available in the literature survey. Index Terms—Forecasting, fuzzy systems, neural networks (NN), particle swarm optimization (PSO), wavelet transform (WT).
Keywords	gradient boosting regressor,random forest, support vector , root mean square error,indian energy exchange ,
Field	Engineering
Published In	Volume 6, Issue 3, May-June 2024
Published On	2024-06-21
Cite This	Energy Pooling Market Price Forecasting using Machine Learning - Y.Shanmukha Manohara Reddy, B.Srilatha, Ch.Yaswanth, P.Dilip - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.19133
DOI	https://doi.org/10.36948/ijfmr.2024.v06i03.19133
Short DOI	https://doi.org/gt2cb6

View / Download PDF File

E-ISSN 2582-2160

doi

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 6 Isu 4 Cover Page Vol 6 Isu 3 Cover Page Vol 6 Isu 2

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9898-5948-55	editor@ijfmr.com

International Journal For Multidisciplinary Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Energy Pooling Market Price Forecasting using Machine Learning

Share this