Day-ahead Price Forecasting in Ontario Electricity Market Using Variable-segmented Support Vector Machine-based Model

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Abstract
In this article, the wholesale price of the Ontario electricity market has been forecasted by splitting the time series into 24 series, one for each hour of the day. Then, a one-step ahead forecast for each hour of the next day for a test period of three years has been made using the respective hour–time series and by employing a support vector machine. A detailed sensitivity analysis was performed for the selection of model parameters. Furthermore, the performance of a support vector machine model has been compared with a heuristic technique, simulation model, linear regression model, neural network model, neuro-fuzzy model, autoregressive integrated moving average model, dynamic regression model, and transfer function model. It has been shown that the proposed variable-segmented support vector machine model possessed better forecasting abilities than the other models and its performance was least affected by the volatility.

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