Forecasting of electricity prices by a novel hybrid approach

What is it about?

In this paper, interval forecasting of market clearing prices is conducted based on a novel approach within two consecutive steps. In the first step, a new hybrid method is proposed to estimate point forecasts; combination of wavelet transformation, feature selection, Extreme Learning Machine (ELM), and bootstrap approaches in an ensemble structure is employed. The second step consists of following stepwise parts: calculating the variance of the model uncertainties based on the extracted data from the ensemble structure, estimating the noise variance by using the Maximum Likelihood Estimation (MLE), and improving the accuracy of interval forecasting by using Particle Swarm Optimization (PSO) algorithm.

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Why is it important?

In general, Developing point and probabilistic forecasting of the electricity prices for the next hour and the next day in a novel, quick and better operational structure is the main aim of this paper.