Research On Techniques Of Medium And Long Term Load Forecasting For Power Grid Planning

Power grid planning is the basis of power grid construction, load forecasting is the basis for power grid planning, so the research on techniques of medium and long term load forecasting for power grid planning has important practical value and theoretical significance. This paper based on the characteristics, content and prediction steps of medium and long term load forecasting, combined with the research current status, describes the problem of load forecasting study nowadays. Work around medium and long term load forecasting data preprocessing, linear regression limitation, removing the redundant information component, load forecasting model selection, new combination model and coordination of forecasting result is discussed.As a small sample of medium and long term load forecasting, in the process of basic data collection and sorting, some missing or abnormal samples will have a major impact to the effect of forecast mode. To improve the traditional method of abnormal data identification and missing data filling, this paper proposes a graph based on T" ellipse to identify abnormal data and support vector machine method to fill the missing data, besides, reduce negative effect of abnormal data through the robust regression.When medium and long term load forecasting model established through multiple linear regression, the multiple correlations between variables will seriously undermine the stability of prediction models.To improve traditional linear regression modeling inefficiently in situation of less samples or multiple correlation, this paper introduces the fundamental tenets and detailed calculating steps of partial least square method to ameliorate the work scope of linear regression in medium and long term load forecasting process.To decrease negative effect of components imbalance explanatory ability to variable system as load forecasting model built through partial least square regression, the view which combined partial least square method with the improved orthogonal signal correction was proposed. The method can improve forecasting accuracy of partial least square regression effectively with eliminating redundant orthogonal information and increaseing the explanatory ability of model's component under the limited components condition.It is a effective way to improve medium and long term forecasting with excluding unreliable, redundant, less effective forecasting model before combination.For the lack of filtering method, a forecasting model selection step, through three aspects-cointegration test, optimal combination determining and redundancy test is proposed. Contact with the actual situation of power grid planning, the priority of the filtering process is analyzed.In actual load forecasting work, the accuracy of each forecast model change with time while traditional combination methods of medium and long term load forecasting, the weight coefficient is dependent on the prediction methods, so the model can not reflect the changes of load development. Therefore, a new combination model based on induced ordered weighted geometry averaging operator and ordered weighted Markov chain is proposed. According to the level of accuracy, this model assigns the weight to each individual method to achieve the correlation between weight coefficient and fitting accuracy in any time point. Since ordered weighted Markov chain has qualitatively forecasted the accuracy of each method of the target year, the weight coefficient can be determined for forecasting.Forecasting result coordination exists in load forecasting task widely.This paper proposes a coordination opinion for dimension 1 level 2 or dimension 2 level 2 medium and long term load forecasting result coordination situation. In the coordination process, the opinion views the global square sum of relative correction as target, achieve the total demand and sub demand unified while the amendment minimizing.