Forecasting electricity demand in Japan: A Bayesian spatial autoregressive ARMA approach
Forecasting electricity demand in Japan: A Bayesian spatial autoregressive ARMA approach
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DOI：
10.1016/j.csda.2009.06.002
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发表时间：
2010-11-01
影响因子：
1.8
通讯作者：
Kakamu, Kazuhiko
中科院分区：
文献类型：
--
作者：
Ohtsuka, Yoshihiro;Oga, Takashi;Kakamu, Kazuhiko
使用贝叶斯方法研究了日本的区域电力需求和区域间的空间相互作用。针对日本电力需求的特点，提出了一种基于空间自回归(SAR)的ARMA模型，并构造了马尔可夫链蒙特卡罗(MCMC)方法对模型参数进行估计。从实证结果中选择了空间自回归ARMA(1，1)模型，发现空间相互作用对日本电力需求起着重要的作用。此外，对数预测密度表明，该SAR-ARMA模型的性能优于单变量ARMA模型。经验证，该时空模型提高了日本未来电力需求的预测性能。(C)2009爱思唯尔B.V.保留所有权利。
Regional electricity demand in Japan and spatial interaction among the regions using a Bayesian approach were examined. A spatial autoregressive (SAR) ARMA model was proposed to consider the features of electricity demand in Japan and a strategy of Markov chain Monte Carlo (MCMC) methods was constructed to estimate the parameters of the model. From empirical results, the spatial autoregressive ARMA (1, 1) model was selected, and it was found that spatial interaction plays an important role in electricity demand in Japan. Moreover, log predictive density showed that this SAR-ARMA model performs better than a univariate ARMA model. It was confirmed that the space-time model improves the performance of forecasting future electricity demand in Japan. (C) 2009 Elsevier B.V. All rights reserved.