Forecdiction of electricity price intervals for dynamic Bayesian networks

Forecdiction of electricity price intervals for dynamic Bayesian networks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Forecdiction of electricity price intervals for dynamic Bayesian networks Hongtao Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6684326/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Oct, 2025 Read the published version in Energy Informatics → Version 1 posted 10 You are reading this latest preprint version Abstract The increasing volatility of electricity prices, driven by the growing share of renewable energy in the market, calls for a new approach to interval prediction. This paper proposes a dynamic Bayesian network (DBN) method for electricity price range forecasting. The model uses predicted values of wind power generation, total power generation, total electricity consumption, and historical electricity prices as input data. The network structure is determined using a greedy search algorithm, and the model parameters are estimated through maximum likelihood estimation (MLE).By treating the predicted values of wind power generation, total power generation, and total electricity consumption as reasoning evidence, the method employs joint tree reasoning to generate discrete values and posterior probabilities for electricity price predictions, enabling interval forecasting. The DBN-based interval prediction results achieve a forecast interval coverage probability (PICP) of 95.24%, a normalized average width of forecast interval (PINAW) of 9.25%, and a cumulative width deviation (AWD) of 0.56%.The proposed method?s effectiveness was evaluated by comparing its predictions with actual electricity prices and with results from PSO-KELM methods. This innovative approach not only provides prediction intervals for electricity prices but also associates them with corresponding probabilities, offering significant potential to enhance market participants' decision-making and mitigate price risks. Dynamic Bayesian network electricity price forecasting interval prediction prediction interval coverage probability Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Oct, 2025 Read the published version in Energy Informatics → Version 1 posted Editorial decision: Revision requested 30 Aug, 2025 Reviews received at journal 13 Aug, 2025 Reviewers agreed at journal 27 Jul, 2025 Reviews received at journal 02 Jul, 2025 Reviewers agreed at journal 19 Jun, 2025 Reviewers agreed at journal 18 Jun, 2025 Reviewers invited by journal 16 Jun, 2025 Editor assigned by journal 12 Jun, 2025 Submission checks completed at journal 12 Jun, 2025 First submitted to journal 16 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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