标题: 应用灰理论及遗传规划法建构改良及混合式能源需求及消耗之预测模型
Building Improved and Hybrid Forecasting Models for Energy Demand and Consumption Using Grey Theory and Genetic Programming
作者: 唐丽英
TONG LEE-ING
国立交通大学工业工程与管理学系(所)
关键字: Grey theory;ARIMA;Genetic programming;ANN;Regression Analysis;Hybrid grey forecasting model;Grey relation analysis
公开日期: 2011
摘要: 由于高科技与经济的发展均需要大量的能源,能源危机已成为全球的重要议题。从
世界各国的统计年报可以发现各国对能源的需求都呈现稳定成长的现象;而地球温室效
应的问题使得气温渐趋酷热或酷寒,造成有些国家水源短缺等问题,因此,对政府能源
管理单位而言,极需建立一套国家能源管理系统,以准确地预测能源需求量及消耗量,
进而制定出符合经济效益之能源管理政策,减少不必要的成本。目前中外文献大多是利
用计量经济模型(econometrics model),灰预测模型(Grey forecasting model),混合预测
模型(hybrid forecasting model)来探讨上述问题。在计量经济模型中常用到自我廻归整合
移动平均(Autoregressive Integrated Moving Average, ARIMA)方法来预测时间序列资
料。由于实际之能源资料是属于非平稳型数列(non-stationary series),需使用ARIMA 的
差分方法将其转换成平稳型数列(stationary series)才能建构预测模型。然而 ARIMA
方法需符合一些统计假设,如预测模型须为线性模型,资料须符合常态分配;此外,由于
能源资料通常很少笔,能源管理决策者在此情况下利用ARIMA 所建构之能源预测模型无
法准确地预测未来能源趋势的走向,而无法制定出符合经济效益的能源管理政策。在建
构灰预测模型以及混合灰预测模型时不需任何统计假设;且只要四笔以上资料即可,故
灰预测模型比 ARIMA 模型更能符合能源资料的实际情况。由于实际能源资料取得不易
以及当实际资料量少于四笔时,利用灰预测模型建模时会有不稳定的残差讯号
(residual sign),虽已有文献提出用类神经网路(Artificial Neural Network, ANN)
方法来估计残差讯号,然而类神经网路必须要大量的资料才能建构良好的网路架构;而
在混合灰预测模型中,过去文献上有利用灰预测模型结合ARIMA 方法在残差资料的改良
来提升整体的预测准确度,然而ARIMA 方法有其统计上的假设问题;除此之外,影响能
源需求以及消耗之因素众多,传统的灰预测模型(GM(1,1))仅能使用作单一预测变数来
建模,当有多个预测变数,GM(1,1)不再适用。为了解决这些问题本计画建立一套合理
且精确的能源需求量或消耗量预测模型以供政府能源管理单位参考之用。本计画共分三
年完成,第一年的工作是先依据灰预测模型建构能源需求量或消耗量之预测模型,再利
用遗传规划法(Genetic Programming, GP)来估计残差讯号,可避免使用类神经网路在
少量资料预测残差讯号能力的不足,来建立一套不限资料笔数且有效之能源管理预测模
型。本计划第二年的工作是利用混合灰预测模型来建构另一个能源预测模型,本计画利
用灰预测模型结合遗传规划法在少量资料以及非线性的特性提出一个新的混合灰预测
模型,可以免除如:ARIMA 在其残差资料的假设。本计画第三年的工作则是利用灰色关联
在少量资料中找出影响国家能源的 N 个重要因子,再结合灰预测模型(GM(1,N)),以及
遗传规划法来建立一个精简有效的预测模型。本计画最后将利用台湾或其它国家的能源
消耗量作为实际案例,来说明本计画所构建之能源预测模型之有效性。
With the high-tech and economical development of a country, energy demand and
consumption become important issues. From the Statistical Year Book of each country, the
energy demand demonstrates stable growing trend annually. The problem of global warming
changes the temperature dramatically. Hence, it is very important to build an accurate energy
demand or consumption model for the energy policy makers of the government. If the energy
demand or consumption can be predicted accurately, some unnecessary cost will be saved.
Many forecasting models are proposed such as the autoregressive integrated moving average
(ARIMA) model, the grey model (GM), and the hybrid forecasting model. The ARIMA
model is often utilized to predict the time-series data. Since the energy data are nonstationary,
differential rule need to be utilized to transform the data into the stationary one.
Moreover, the ARIMA model requires some statistical assumptions such as the model is
linear, large sample size and data follow a normal distribution. The data collected on energy
demand or consumption are often very few or non-normal Past studies developed two models
(i.e., the grey forecasting model and the hybrid forecasting model) for forecasting the energy
data Although a grey forecasting model, based on grey theory, can be constructed for at least
four observations or ambiguity data, it yield large forecasting errors. To minimize such errors,
the main objective of this three-year project is to build an improved grey forecasting and a
hybrid grey model, respectively. The three-year project is divided into three phases. In the
first year, the grey forecasting model and the genetic programming will be utilized to build a
novel improved grey forecasting model through the forecasting ability of grey forecasting in
small data and the detection ability of Genetic Programming (GP) in residual signs. In the
second year, a hybrid grey forecasting model will be established to further improve the
accuracy of the forecasted errors using the grey forecasting model and Genetic Programming
(GP) in estimating the residual values. In the third year, the grey relation analysis is utilized to
determine the important factors which influence the energy demand or consumption data. The
grey forecasting model and GP are then utilized to build an energy forecasting model. Finally,
the energy demand or consumption data from Taiwan, China or other countries were adopted
to demonstrate the effectiveness of the proposed forecasting models.
官方说明文件#: NSC99-2221-E009-086-MY2
URI: http://hdl.handle.net/11536/99145
https://www.grb.gov.tw/search/planDetail?id=2199311&docId=350199
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