Predicting short-term wholesale prices on the Irish single electricity market with artificial neural networks

The ability to operate effectively on electricity spot markets relies on the capability to devise appropriate bidding strategies. These in turn can benefit from the inclusion of a reliable forecast of short term system marginal prices (SMPs). In a market with an increasing percentage of renewable generators, reliable forecasts must necessarily consider additional factors such as meteorological forecasts, forecasted demand and constraints imposed by network topology. Traditional time series forecasting algorithms (e.g. based on AutoRegressive Integrated Moving Average models) can perform reasonably well in this context but rely on assumptions being made on behavior over different temporal windows to yield consistent results. Research studies have demonstrated that an adaptive or self-adaptive approach to forecasting would appear more suited to the task of predicting energy demands in territory such as Ireland. Implementing an in-house self-adaptive model should yield good results in the dynamic uncertain Irish energy market. We have identified the features that such a model demands and outline it here.