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- Electricity market price analysis using time series clusteringPublication . Martins, Ana Alexandra; Lagarto, João; Cardoso, Maria MargaridaThe creation of the internal market of electricity has long been a goal of the European Union, for which it has established common rules through the directive 2009/72/EC. In this context, the analysis of electricity markets operation of the different countries that will form the internal market is of the utmost importance. In this work, we use clustering techniques to analyze 26 time series of day-ahead electricity prices from European markets between 2015 and 2018 in order to identify different price patterns. The cluster technique proposed uses a combination of three dissimilarity measures for time series: Euclidean, Pearson correlation based and periodogram based. Results show that there is a clear distinction between Northern markets, especially Nord Pool, and Southern markets, MIBEL and Italy. Moreover, results also show that despite some market prices presenting similar behaviors, a full integrated European electricity market is yet to be accomplished.
- Short-term load forecasting using time series clusteringPublication . Martins, Ana Alexandra; Lagarto, João; Canacsinh, Hiren; Reis, Francisco; Cardoso, Maria MargaridaShort-term load forecasting plays a major role in energy planning. Its accuracy has a direct impact on the way power systems are operated and managed. We propose a new Clustering-based Similar Pattern Forecasting algorithm (CSPF) for short-term load forecasting. It resorts to a K-Medoids clustering algorithm to identify load patterns and to the COMB distance to capture differences between time series. Clusters' labels are then used to identify similar sequences of days. Temperature information is also considered in the day-ahead load forecasting, resorting to the K-Nearest Neighbor approach. CSPF algorithm is intended to provide the aggregate forecast of Portugal's national load, for the next day, with a 15-min discretization, based on data from the Portuguese Transport Network Operator (TSO). CSPF forecasting performance, as evaluated by RMSE, MAE and MAPE metrics, outperforms three alternative/baseline methods, suggesting that the proposed approach is promising in similar applications.