The Use of Markov Chain Models for the Prediction of Power Demand and Market Share in Retail Electricity Markets
Pages 341 - 345
Abstract
Retail electricity markets are highly complex, and fluctuations in market share and electricity demand among suppliers have important implications for market participants and policy makers. In order to better understand the dynamics of the market and future trends, this study employs Markov chains as a modelling tool. The study uses a Markov chain model to analyse the market shares of different suppliers in the retail electricity market. The model considers transfer probabilities between market states, which are based on historical data and market conditions. The study also used a system dynamics approach to analyse trends in electricity demand, including seasonal and cyclical fluctuations. Through Markov chain modelling, the market shares of different suppliers were successfully estimated and future changes in market shares were predicted. The study also provides insights into the demand for electricity, which helps market participants to plan resources and supply better. The study also considers the impact of policy factors on market share and demand. In particular, policy factors have significantly influenced market dynamics during policy adjustments and unbundling in the electricity market. This study provides electricity market participants with important information on market share and demand for electricity, which can help better manage market risk and decision making. Future research directions include further improving the market model and considering the effects of more external factors to improve the accuracy of market forecasts.
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November 2023
764 pages
ISBN:9798400708299
DOI:10.1145/3640115
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Published: 26 March 2024
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ICITEE 2023
ICITEE 2023: 6th International Conference on Information Technologies and Electrical Engineering
November 3 - 5, 2023
Changde, Hunan, China
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