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Application of Markov Chain in Midlong Term Load Combination Forecasting
Author: HuangYinHua
Tutor: PengJianChun
School: Hunan University
Course: Electrical Engineering
Keywords: Power Systems Longterm load forecasting Combined Forecast Markov chain Transition probability
CLC: F407.61
Type: Master's thesis
Year: 2010
Downloads: 353
Quote: 5
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Abstract
Longterm load forecasting is the basis for urban power grid planning work, one of which is essential for network planning provides basic data, the power system is to ensure reliable power supply and economic operation of the premise. Reasonable and accurate load forecasting power investment will directly affect the network layout and operation is reasonable, has great economic and social benefits. Since longterm load forecast by many uncertain factors, single forecasting model difficult to guarantee in any case be able to obtain satisfactory predictions. How to get simple, practical, good stability prediction model and how to improve the accuracy of load forecasting has become the focus of research scholars. In this paper, learn and digest Markov chains, based on the combination of its introduction into the long term load forecasting, respectively, from the series combination of prediction and forecasting the parallel combination of two aspects of the analysis period. This paper first outlines the power load forecasting current research and the purpose and significance of load forecasting, load forecasting are discussed classification and long term load forecasting method, also introduced the basic theory of Markov for the later development to do the groundwork. On this basis, considering the characteristics of Markov chain and apply it to combination forecasting go. For the series combination of forecasts, combined with Markov chain transition probabilities may reflect the influence of random factors applicable to random fluctuations of the dynamic characteristics of the process will be conducted with the combination of gray prediction model. This method makes up gray prediction model in predicting the outcome in terms of accuracy and trustworthiness demonstrated the inherent defects. Prediction results show that the method is feasible to improve forecasting accuracy; For the parallel combination of forecasts, on the one hand, for a single model has a specific scope and conditions of use of Markov process without aftereffect will be characterized by their application Screening of the load model. Study confirmed the relative use of the model has not been screened forecast screening model predictions with the predicted results are more satisfactory. The other hand, gives the smallest error as a criterion states and state Markov chain probability preliminary estimates, then the probability distribution of Markov chain fitting timevarying state laws. Bystep transition probability matrix estimation problem into multiple constraints autoregressive model, and then use onestep transition probability matrix of the initial state probability distribution estimates and dynamically acquire portfolio weights. Examples show that this method small amount of calculation, high accuracy. Finally, combining Markov in the application of longterm load forecasting, analysis and summarizes the prediction error sources and further explore ways to improve the accuracy of load forecasting.

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