Day-ahead electricity market forecasting using kernels

Weather and life cycles, fuel markets, reliability rules, scheduled and random outages, renewables and demand response programs, all constitute pieces of the electricity market puzzle. In such a complex environment, forecasting electricity prices is a very challenging task; nonetheless, it is of paramount importance for market participants and system operators. Day-ahead price forecasting is performed in the present paper using a kernel-based method. This machine learning approach offers unique advantages over existing alternatives, especially in systematically exploiting the spatio-temporal nature of locational marginal prices (LMPs), while nonlinear cause-effect relationships can be captured by carefully selected similarities. Beyond conventional time-series data, non-vectorial attributes (e.g., hour of the day, day of the week, balancing authority) are transparently utilized. The novel approach is tested on real data from the Midwest ISO (MISO) day-ahead electricity market over the summer of 2012, during which MISO's load peak record was observed. The resultant day-ahead LMP forecasts outperform price repetition and ordinary linear regression, thus offering a promising inference tool for the electricity market.