Hybrid Power Market Forecasting Using an3 Interval-Valued Hidden Markov and Fuzzy Relational4 Particle Swarm Optimization Model
DOI:
https://doi.org/10.29020/nybg.ejpam.v18i4.6539Keywords:
Hidden Markov model, Fuzzy relational matrix, Particle Swarm OptimizationAbstract
The Hidden Markov Model (HMM), one of the Bayesian analysis tools, has a wide range of applications. In this paper, we present a unique method of merging particle swarm optimization (PSO) for parameter optimization with fuzzy hidden Markov models (FHMMs) to increase the accuracy of power price projections. The suggested model is created using the parameters of the
Interval Valued Fuzzy Relational Hidden Markov Model (iHMM-FRPSO), which are appropriately established in accordance with the power forecasting system. PSO is then used in MATLAB to optimize and modulate the model.The study’s primary goal is to reduce the likelihood of mistakes, namely mean square error (MSE), root mean square error (RMSE), and mean absolute percent
error (MAPE), while optimizing the constraint parameters. The Indian Energy Exchange (IEX) Day-Ahead Markets and Real-Time Markets datasets are used to train the algorithm. Findings from the training set’s mean directional forecasting accuracy from January 2021 to December 2021 indicate that the best optimized MAPE for day-ahead markets is 14.400?
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