Short-term load forecasting system using data mining
In this paper, by means of data mining techniques, a platform of data warehouse is designed after preprocessing the huge amounts original data of power system, and a system for short term load forecasting (STLF) is developed, in which there is the synthetic technology of both fuzzy clustering and robust regression model in the platform. The useful data excavated from large amounts of data can offer the effective and accurate load forecasting information for reliable and economic operation of power systems. The validity of the designed system for STLF is shown by the simulation results of an actual power system in China.
We model cross-market Bitcoin prices as long-memory processes and study dynamic interdependence in a fractionally cointegrated VAR framework. We find long-memory in both the individual markets and the system of markets depicting non-homogeneous informational inefficiency. Moreover, Bitcoin markets are found to be fractionally cointegrated, where uncertainty negatively impacts this type of cointegration relationship.