Intelligent Method Based Load Forecasting Models And Application Research In Power System

Electric load forecasting is one of the important parts in EMS and SMS and the precondition of the auto-generate electricity control and the economically attemper. It connected the programming and the design of the power system with the economical, security, reliability and stability. With the development of the electric power system and the management of electric grid becoming more and more modern and complexity, more and more researchers pay widely attention to the load forecasting problem. The accurate load forecasting could design the generator groups operational normally, keep up with security and stability of the electric power grid. Preparing the examination and reparation of the generator groups to be ensure the normal production and living; and effectively reducing the cost of generating electricity and enhancing the benefits of the societal and economic. So, the electric load forecasting is an important research topic and research field in modern power system operating and the managementIn this thesis, the electricity load forecasting models and algorithms are discussed mainly using fuzzy mathematics, fuzzy time series theory, grey systems theory and fuzzy multi-objective programming etc the mathematics theory and intelligent method. Firstly, the basic fuzzy time series forecasting models and algorithms are given, Considering the changes of loads itself and temperature et al synthetical factors which affects on the loads, the optimal revised forecasting algorithm are studied again; secondly, a robust regression forecasting algorithm and robust fuzzy regression forecasting algorithm are discussed based on robust statistic theory and fuzzy regression theory, then, discrete grey interval forecasting models and algorithms are obtained based on the discrete grey forecasting model combined with fuzzy multi-objective theory, and at last, the fuzzy time series forecasting algorithm based on genetic algorithm to searching for the optimal parameters and Pareto optimization theory to determined the optimal interval are investigated. Considering the different forecasting models and algorithms, the numerical examples are given to comparing the forecasting results. The main research contents and innovative points are the follows:(1) Research about electricity load forecasting algorithm based on fuzzy time series.Based on the fuzzy time series theory, this paper introduce the fuzzy time series forecasting model and algorithm into the electric power system load forecasting. Firstly, the history loads are pre-disposed to recognize and smooth the outlier. then considering the characters of the loads and the important factors which affect on the load forecasting, the temperature influencing factor, the load changeable factor and the synthetical factors etc are introduced as the weight factors. So the time-variant fuzzy time series forecasting algorithm and maximum load forecasting algorithm based on bi-factor revised fuzzy time series model are proposed. At last, two numerical examples are given to validate our proposed forecasting model and algorithm have better forecasting effects and have an universality.(2) Forecasting model and algorithm research based on robust fuzzy regression.Based on robust statistic theory and Tanaka’s fuzzy regression principle, a robust regression forecasting algorithm and a robust fuzzy regression forecasting model are studied. By considering the choice of the objective function and the weight function and the iterative procedure, The robust regression forecasting algorithm weakens the bad influence of the outlier to the regression equation and strengthens the robustness of the regression equation. On the other hands, the robust fuzzy regression forecasting model is studied, which gives the robust fuzzy forecasting interval by means of the error evaluative index and the Tanaka’s fuzzy regression method. The nonlinear forecasting model is obtained by the optimization and the transform of the parameter. At last, the two numerical examples are given to compare the forecasting accuracy our proposed robust forecasting model and algorithm with the traditional forecasting method, and obviously, our proposed forecasting method could reduce the bad influence which the outlier made to the forecasting effects and could have a better robustness compared with the conventional methods.(3) Research about fuzzy discrete gray interval forecasting model.Based on discrete grey forecasting model combined with fuzzy multi-objective optimization theory, the fuzzy multi-objective discrete grey forecasting model is proposed. Considering the input variables are crisp data or the fuzzy data, the different interval forecasting model and algorithm are obtained.The forecasting results using our proposed forecasting model have a better accuracy compared with the forecasting results of the fuzzy linear regression interval forecasting and the grey fuzzy interval forecasting. Finally, we applied the forecasting model into the power system load interval forecasting and obtain the general results which illuminate that our proposed forecasting model have a universal and extensive applications. (4) Research about the fuzzy time series forecasting model and algorithms based on Multi-objective optimization theory.Because of the forecasting interval partitions is one of the important influence factors which affect the precision of the fuzzy time series forecasting, we first define and partition the forecasting interval with parameters, then using the genetic algorithm to searching for the optimal lengths of forecasting interval with parameters and Pareto optimization theory to identify the optimal solution and choice the initialization parameter. a time-variant ratio-multi objective optimal fuzzy time series forecasting model and a time-invariant multi-objective optimal fuzzy time series forecasting model and algorithms are proposed. When we apply our proposed forecasting model and algorithm to the enrollment forecasting of the Alabama, these forecasting results have a better forecasting precision compared with the other reference literatures. At last, we using our proposed two forecasting models and algorithms into the long term electricity consumption in shanghai and obtain the universal forecasting results which explain that our proposed forecasting methods have availability and applicability.