Point and prediction interval estimation for electricity markets with machine learning techniques and wavelet transforms
A growing number of countries all over the world are switching over to deregulated or the market structure of electricity sector with a view to enhance productivity, efficiency and to lower the prices. Barring a few cases, the deregulated structure is doing quite well in most of the countries. However a persistent issue that plagues the involved parties such as producers, traders, retailers etc., is the uncertainty that prevails in the system. Due to a number of known, unknown factors, the electricity prices exhibit fluctuating characteristics which is difficult to control as well as predict. Several forecasting techniques have been developed and successfully implemented for existing markets around the world with comparable performance. However, the uncertainty aspect of the point forecasts has not been analyzed significantly. In this work, an attempt is made to quantify such uncertainties existing in the market using statistical techniques like prediction intervals. Hybrid models using neural networks and Extreme Learning machines with wavelets as preprocessors are developed and applied for point as well as prediction interval forecasting for Ontario Electricity Market, PJM Day-Ahead and Real time markets.
prediction intervals wavelet transforms uncertainty artificial neural network (ann) bootstraps extreme learning machine (elm)