Power system short-term load

The general objective of this work is to provide power system dispatchers with an accurate and convenient short-term load forecasting (STLF) system, which helps to increase the power system reliability and reduce the system operation cost. On the whole, this thesis is composed of three parts: historical data treatment, individual algorithms proposed for load forecasting, and the design of an integrative and convenient system combining different algorithms. The concepts of forward second order difference (FSOD) and backward second order difference (BSOD) are introduced. This research regards a load curve as the sum of two load curves: an essential load curve that represents the basic load requirement, and a vibrating curve that contains the information of sudden change of the large consumers' state. This work applies the regression tree algorithm to the load forecasting problem. The algorithm can automatically classify the data and assign a value for every tree node without a prior knowledge. The result of the algorithm has the form of "if...then...", which can be easily understood. Although the original purpose of applying a regression tree is to avoid a prior knowledge, it is found that good understanding of the system helps to improve the regression tree design for a better forecasting result. Therefore some special treatments are added to the regression tree according to the expert experience. This work proposes an SVM-based forecasting method. Two methods are proposed to solve this problem: a holiday regression tree and imaginary load method. This thesis proposes to combine RT and SVM to take advantage of the merits and avoid the demerits of the two algorithms. Firstly RT is established. If the target load falls into a leaf node with a large number of similar samples and very low dispersion, the leaf node output value can be taken as its forecasted value. Otherwise, SVM is executed to analyze the behavior of the samples in the same node. A combination method is proposed to employ more single load forecasting algorithms and take more advantage of the more appropriate ones. Different power systems have different load behaviours. In this work three-table frame and related programming modules are designed for different users to input the system load properties and the calculation requirement