Research On Key Problem And Method Of Short-Term Load Forecasting

The forecasting for the load of electric power system is fundamental to ensure safe andeconomic operation of power system. For a power system, safe and economic operation ofpower grid as well as good electric quality depends on correct estimation of electric load.In short-term load forecasting, it is essential to have a correct understanding and analysisof factors affecting electric load. Based on weather information, this dissertation has analyzedvarious factors and has made a profound study of short-term load forecasting, thus a short-termforecasting method has been proposed based on analyzing load-affecting factors. The researchmethods for the factors affecting electric load mainly cover load-affecting factors selection,determined factors analyzing, undetermined factors analyzing and load processing withinsufficient data, etc.Load- affecting factors selection: A method is presented for selecting factors of affectingload, aiming to determine factors and remove badly-affecting factors. Based on data miningtheory and cluster analysis, this method draws on major components of Partial least-squareregression, realizing a selection of load- affecting factors by analyzing factors importance,therefore reducing work load of forecasting model and ensure forecasting accuracy.Analyzing of determined factors: It is critical to give a correct understanding of determinedfactors such as weather temperature and weather conditions. Especially when figures ofdetermined factors change, forecasting models can't judge the changing trend, thus reducingforecasting accuracy. Whereupon, Structural neural networks method is suggested. This methodis to learn sub-network and the whole network so as to know the effects by different factors;based on actual situation of factors affecting, load changing direction is mastered so thatfeasible basis is made for quantitatively studying various factors with priority of accuracy.Analyzing undetermined factors: Except for determined factors, usually there areundetermined regular factors unknown clearly for us. According to the facts that electric load isregular and some factors are undetermined, RBF neural network is used to search for generallaw of changing load. Fuzzy reasoning is applied to analyze peak and valley value. Combiningthe above two, we solve the difficult problem about undetermined factors. This methodemploying neural network and fuzzy reasoning deals with undetermined factors, and hasadvantages and a higher accuracy.Load processing with insufficient data: For special days with abnormal weather condition,historical reference data is not enough and changing trend is not fixed. If we use generalmethods, forecasting accuracy is difficult to guarantee. This dissertation proposes improvedGM(1,1) model and correction system to analyze short-term load. The method use grey modelcharacterized by less data and no need of load distribution law, etc, and create improved greymodel. Meanwhile, considering that special day affect load change, latest information is used tocorrect the result, so accuracy is higher. After the above four parts of work are completed, this dissertation puts forward a short-term forecasting method based on a comprehensive considering of various factors at first time.This forecasting method provides analyzing functions & calculation models of load-affectingfactors selection, determined factors, undetermined factors and load processing with insufficientdata etc, and gathers information contained in a single model and makes optimal combination. Itbuilds up a high accuracy and result-analyzing model, and offer a powerful theoretical basis forhigh quality and reliable power supply.It's proved by examples that short-term forecasting methods shown in this dissertationhave practicability and validity, and it can improve short-term forecasting precision further.