共查询到17条相似文献,搜索用时 328 毫秒
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将诱导有序加权调和平均算子和马尔科夫链相结合,提出一种基于诱导有序加权调和平均算子和马尔科夫链的组合预测模型,该模型可以克服传统的组合预测方法赋予不变的权系数和以单一误差指标作为预测精度衡量的缺陷,同时采用马尔科夫链推出各单项预测模型在各个预测时间点预测精度的状态,从而得到组合模型的权系数。文中首先采用回归法、指数平滑法及灰色预测法分别建立了陕西省某市年用电量单项预测模型,随后引进诱导有序加权调和平均算子和马尔科夫链的概念,建立了年用电量的组合预测模型,并对年用电量进行了实证分析。实例分析表明了新模型能有效地提高组合预测精度,降低预测的风险性,从而证明这种组合模型具有较好的实用性。 相似文献
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《可再生能源》2017,(1)
针对风电功率超短期预测精度不高的问题,提出了一种结合Theil不等系数与改进诱导有序加权算子的组合预测方法。由于预测时刻的实际风电功率值未知,因此无法直接利用该方法进行预测。文章利用各单项预测模型的前几个时刻的预测精度均值作为预测时刻风电功率的诱导值,对诱导有序加权算子进行了改进,解决了预测时刻诱导值未知的问题。采用误差信息矩阵对单项模型进行冗余度分析,得到优选单项模型,然后建立基于Theil不等系数和3种改进诱导有序加权算子的组合预测模型。通过分析和实例验证表明,结合Theil不等系数和诱导有序加权算数平均算子(IOWA)的组合模型能有效地提高风电功率预测精度。 相似文献
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基于熵值法的组合模型用电量预测方法研究 总被引:1,自引:0,他引:1
电力系统的中长期负荷预测是配电网规划的基础,对实现电网的安全经济运行起着重要作用。以年度用电量预测作为研究的对象,年度用电量预测采用4种主要方法,即分别按照年度、季度、月度和行业用电量预测得到对应年用电量预测值,在此基础上再按其发展序列预测结合起来,建立了一种线性组合预测模型。并采用熵值法对组合模型的权系数进行求解,实证分析表明该模型使预测精度得到了明显提高,具有良好的预测效果。 相似文献
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随着风力发电的大规模并网,由风速的波动引起的网侧不稳定现象越来越显著。为了提高风电场风速预测的精度,首先建立了ARMA模型,利用拉格朗日乘数法检验ARMA模型残差的条件异方差效应,从而建立ARMA-ARCH模型;其次建立BP神经网络预测模型;最后分别以固定权和时变权方差-协方差(MV)法将ARMA-ARCH模型和BP模型进行优选组合预测。为验证模型的适应性,分别以西班牙某风电场2016年8月和2017年1月的风速数据进行建模仿真。仿真结果表明:组合预测模型的预测结果更优,且时变权组合预测精度更高;对于单一模型来说ARMA-ARCH模型的预测精度要高于BP模型,而ARMA模型的预测精度最低。 相似文献
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In this paper, an empirical model is developed for electricity consumption of the Jordanian industrial sector based on multivariate linear regression to identify the main drivers behind electricity consumption. In addition, projection of electricity consumption for the industrial sector based on time series forecasting is presented. It was found that industrial production outputs and capacity utilization are the two most important variables that affect demand on electrical power and the multivariate linear regression model can be used adequately to simulate industrial electricity consumption with very high coefficient of determination. To illustrate the importance of integrating energy efficiency within national energy plans, the impact of implementing high-efficiency motors was investigated and found to be significant. Without such basic energy conservation and management programs, electricity consumption and associated GHG emissions for the industrial sector are predicted to rise by 63% in the year 2019. However, if these measures are implemented on a gradual basis, over the same period, electricity consumption and GHG emissions are forecasted to ascend at a lower rate with low/no cost actions. 相似文献
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建立了一种基于用电量和GDP之间耦合关系的中长期电量预测模型。首先利用协整检验和格兰杰(Granger)因果检验,剖析电能消费和经济发展之间的协整关系和因果关系,并建立中长期电量预测模型。然后采用误差修正方法对预测模型进行短期调节,以提高模型的鲁棒性以及预测精度。以某地区1991—2015年的用电量和GDP数据作为算例输入数据,结果表明:通过构建电能消费和经济发展之间的耦合关系,有助于提高预测模型的解释能力,同时含短期调节的中长期用电量预测模型具有更高的预测精度。 相似文献
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为了提高年径流量预测的精度,将灰色系统理论的弱化缓冲算子和人工神经网络相结合,提出一种新的年径流预测方法——引入灰色弱化缓冲算子的人工神经网络组合预测方法,并以兰州站年径流过程计算为例,验证了该方法的合理性。结果表明,预测结果精度较高,可见将引入灰色弱化缓冲算子的人工神经网络组合预测方法用于年径流预测具有可行性。 相似文献
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Huseyin Avni Es 《Energy Sources, Part A: Recovery, Utilization, and Environmental Effects》2021,43(1):54-69
ABSTRACT Natural gas stands out among fossil fuels because it is relatively cleaner. It is also an important energy source type for several fields such as electricity production, industry, and heating, etc. Due to the poor capacity of Turkey in terms of natural gas sources, the demand is supplied by producer countries. Hence, accurate forecasting for the demand is of critical importance for Turkey, which imports 99% of its natural gas consumption. In the current literature about demand forecasting, most studies were conducted on an annual basis. However, the seasonal effect on the demand for natural gas cannot be foreseen through annual studies. Besides, to deal with some situations such as seasonal balancing, peak shaving, and gas supply shortage in monthly demand, forecasting models that capture the seasonal trend are needed. Therefore, in this study, a new grey seasonal forecast model has been presented and Turkey’s monthly natural gas demand was predicted via the proposed model. Performance of that model was compared with SGM(1,1) and SARIMA (p,d,q) x (P,D,Q)s. The obtained results show the superiority of the proposed model. By using this model, Turkey’s monthly natural gas demand was forecasted up until the year 2025. The proposed model allows us to capture seasonal patterns more successfully. In case this seasonal behavior continues, Turkey’s natural gas demand is expected to increase by %20 until 2025. At this point, the outcomes of the study provide important information to decision-makers to be able to determine reliable and stable energy policies. 相似文献
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Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models
This paper proposes a novel price forecasting method based on wavelet transform combined with ARIMA and GARCH models. By wavelet transform, the historical price series is decomposed and reconstructed into one approximation series and some detail series. Then each subseries can be separately predicted by a suitable time series model. The final forecast is obtained by composing the forecasted results of each subseries. This proposed method is examined on Spanish and PJM electricity markets and compared with some other forecasting methods. 相似文献
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