A SOM clustering pattern sequence-based next symbol prediction method for day-ahead direct electricity load and price forecasting

• A novel pattern sequence-based direct time series forecasting method was proposed.
• Due to the use of SOM’s topology preserving property, only SOM can be applied.
• SCPSNSP only deals with the cluster patterns not each specific time series value.
• SCPSNSP performs better than recently developed forecasting algorithms.

In this paper, we propose a new day-ahead direct time series forecasting method for competitive electricity markets based on clustering and next symbol prediction. In the clustering step, pattern sequence and their topology relations are obtained from self organizing map time series clustering. In the next symbol prediction step, with each cluster label in the pattern sequence represented as a pair of its topologically identical coordinates, artificial neural network is used to predict the topological coordinates of next day by training the relationship between previous daily pattern sequence and its next day pattern. According to the obtained topology relations, the nearest nonzero hits pattern is assigned to next day so that the whole time series values can be directly forecasted from the assigned cluster pattern. The proposed method was evaluated on Spanish, Australian and New York electricity markets and compared with PSF and some of the most recently published forecasting methods. Experimental results show that the proposed method outperforms the best forecasting methods at least 3.64%.