No abstract

With the increasing demand of the power industry for load forecasting, improving the accuracy of power load forecasting has become increasingly important. In this paper, we propose an ultra short-term power load forecasting method based on similar day clustering and EEMD (Ensemble Empirical Mode Decomposition). In detail, the K-means clustering algorithm was utilized to divide the historical data into different clusters. Through EEMD, the load data of each cluster were decomposed into several sub-sequences with different time scales. The LSTNet (Long- and Short-term Time-series Network) was adopted as the load forecasting model for these sub-sequences. The forecast results for different sub-sequences were combined as the expected result. The proposed method predicts the load in the next 4 h with an interval of 15 min. The experimental results show that the proposed method obtains higher prediction accuracy than other comparable forecasting models.

Hybrid lithium-ion capacitors (HyLICs) have received considerable attention because of their ability to combine the advantages of high-energy lithium-ion batteries and high-power supercapacitors. State of charge (SOC) is the main factor affecting the practical application of HyLICs; therefore, it is essential to estimate the SOC accurately. In this paper, a partition SOC-estimation method that combines electrochemical and external characteristics is proposed. The discharge process of the HyLICs was divided into three phases based on test results of electrochemical characteristics. To improve the estimation accuracy and reduce the amount of calculation, the Extended Kalman Filter (EKF) method was applied for SOC estimation at the interval where the capacitor energy storage characteristics dominated, and the Ampere-hour (Ah) method was used to estimate the SOC at the interval where battery energy storage characteristics dominated. The proposed method is verified under different operating conditions. The experimental results show good agreement with the estimation results, which indicates that the proposed method can estimate the SOC of the HyLICs accurately.