电力负荷预测毕业论文中英文资料外文翻译文献(5)

中英文资料

Daily peak load forecasting plays an important role in all aspects of economic, reliable, and secure strategies for power system. Specifically, the short-term load forecasting (STLF) of daily electricity usage is crucial in unit commitment, maintenance, power interchange and task scheduling of both power generation and distribution facilities. Short-term load forecasting (STLF) aims at predicting electric loads for a period of minutes, hours, days or weeks. The quality of the short-term load forecasts with lead times ranging from one hour to several days ahead has a significant impact on the efficiency of operation of any power utility, because many operational decisions, such as economic dispatch scheduling of the generating capacity, unit commitment, scheduling of fuel purchase as well as system security assessment are based on such forecasts [1]. Traditional short-term load forecasting models can be classified as time series models or regression models [2,3,4]. Usually, these techniques are effective for the forecasting of short-term load on normal days but fail to yield good results on those days with special events [5,6,7]. Furthermore, because of their complexities, enormous computational efforts are required to produce acceptable results.

The grey system theory, originally presented by Deng[8,9,10], focuses on model uncertainty and information insufficiency in analyzing and understanding systems via research on conditional analysis, forecasting and decision making. The grey system puts each stochastic variable as a grey quantity that changes within a given range. It does not rely on statistical method to deal with the grey quantity. It deals directly with the original data, and searches the intrinsic regularity of data[11]. The grey forecasting model utilises the essential part of the grey system theory.Therewith, grey forecasting can be said to define the estimation done by the use of a grey system, which is in between a white system and a black-box system.

A system is defined as a white one if the information in it is known; otherwise, a system will be a black box if nothing in it is clear. The grey model GM (1, 1) is the main model of grey theory of prediction, i.e. a single variable first order grey model, which is created with few data (four or more) and still we can get fine forecasting result [12]. The grey forecasting models are given by grey differential equations, which are groups of abnormal differential equations with variations in behavior parameters, or grey difference equations which are groups of abnormal difference equations with variations in structure, rather than the first-order differential equations or the difference equations in conventional cases [13]. The grey model GM (1, l) has parameter

? which was often set to 0.5 in many articles,and this constant ? might not be optimal,

because different questions might need different ? value,which produces wrong results. In order to correct the above-mentioned defect, this paper attempts to estimate ? by genetic algorithms.

16

中英文资料

Genetic algorithms (GA) were firstly described by John Holland, who presented them as an abstraction of biological evolution and gave a theoretical mathematical framework for adaptation [14].The distinguishing feature of a GA with respect to other function optimization techniques is that the search towards an optimum solution proceeds not by incremental changes to a single structure but by maintaining a population of solutions from which new structures are created using genetic operators[15].Usually, the binary representation was applied to many optimization problems, but in this paper genetic algorithms (GA) adopted improved decimal-code representation scheme.

This paper proposed GM(1,1)-improved genetic algorithm (GM(1,1)-IGA)to solve short-term load forecasting (STLF) problems in power system. The traditional GM (1, 1) forecasting model often sets the coefficient ? to 0.5, which is the reason why the background value z(1)(k) may be unsuitable. In order to overcome the above-mentioned drawbacks, the improved decimal-code genetic algorithm was used to obtain the optimal coefficient ? value to set proper background value z(1)(k).What is more, the one-point linearity arithmetical crossover was put forward, which can greatly improve the speed of crossover and mutation so that the proposed GM(1,1)-IGA can forecast the short-term daily load successfully. …… 此处隐藏：2448字，全部文档内容请下载后查看。喜欢就下载吧 ……