ARIMA时间序列分析模型在臭氧浓度中长期预报中的应用

近年来,京津冀地区近地面臭氧浓度呈现上升趋势,臭氧污染超标情况严重.目前由于前体物源排放清单、臭氧生成和扩散的物理和化学过程机制存在不足等原因,导致了数值模型在预报夏季臭氧浓度时仍然存在较大偏差,而时间序列分析方法由于具有建模简单、计算成本低的特点,在臭氧污染预报中具有很好的应用前景.本研究利用华北区域大气本底站上甸子站点和环境监测总站天津和保定站点的臭氧观测数据,采用ARIMA时间序列分析模型开展臭氧浓度中长期预报研究.结果表明,季节性ARIMA模型在预报臭氧长期月均值时,预测值和观测值的相关系数R可达0.951,均方根误差RMSE仅为10.2 μg·m^-3.加入了日最高气温及二次项作为协变量的动态ARIMA模型,对臭氧日最大8 h滑动平均值的预报效果得到了很大提升,预测值和观测值的相关系数由0.296~0.455提升至0.670~0.748,RMSE得到了有效降低.     

SEASONAL ARIMA INFLOW MODELS FOR RESERVOIR SIZING1

ABSTRACT: The reliable sizing of reservoirs is a very important task of hydraulic engineering. Although many reservoirs throughout the world have been designed using Rippl's mass curves with historical inflow volumes at the dam site, this technique is now considered outdated. In this paper, synthetic series of monthly inflows are used as an alternative to historical inflow records. These synthetic series are generated from stochastic SARIMA (Seasonal Autoregressive Integrated Moving Average) models. The analyzed data refer to the planned Almopeos Reservoir on the Almopeos River in Northern Greece with 19‐year monthly inflow series. The analysis of this study demonstrates the ability of SARIMA models, in conjunction with the adequate transformation, to forecast monthly inflows of one or more months ahead and generate synthetic series of monthly inflows that preserve the key statistics of the historical monthly inflows and their persistence Hurst coefficient K. The forecasted monthly inflows would be of help in evaluating the optimal real time reservoir operation policies and the generated synthetic series of monthly inflows can be used to provide a probabilistic framework for reservoir design and to cope with the situation where the design horizon of interest exceeds the length of the historical inflow record.     

最优加权组合模型在管道腐蚀预测中的应用

为构造海底混输管道腐蚀最优加权组合预测模型，针对传统非等间距GM(1,1)管道腐蚀预测模型中初始条件的选取问题，提出了新信息优先原理下的NEGM(1,1,τ)海底管道腐蚀速率预测模型，以充分发挥建模序列中各分量对预测系统的修正作用；引入ARIMA预测模型，在3个不同定权准则下与NEGM(1,1,τ)模型形成管道腐蚀加权组合预测模型，并通过评价指标函数实现组合模型的性能评价。研究结果表明:组合模型2的海底混输管道腐蚀速率预测值与实际值的平均相对误差为0.495 4%，评价指标函数RMSE,AARD和MAPE的值分别为0.1936%,0.1275%和0.595 3%，均优于其他2个准则下的组合模型。建立NEGM(1,1,τ)-ARIMA海底管道腐蚀速率最优加权组合预测模型，从多角度挖掘了管道腐蚀速率序列中的可靠信息，预测结果的可信度更高。     

基于ARIMA模型的区域水生态足迹时间序列分析

20世纪末以来,生态足迹理论应用研究成为生态经济学的一个热点,而生态足迹指标的可预测性一直是研究中争论的一个焦点。通过分析1949—1998年广州市的水生态足迹变化,并对计算结果进行ARIMA模型时间序列预测分析,以揭示广州水资源利用和水产品消耗的发展变化规律,并根据其发展变化规律预测广州市未来水生态足迹的变动。结果表明,ARIMA(2,2,2)模型能较好的拟合广州市1949—1993年期间水生态足迹变化,经1994—1998年实际计算结果验证所建模型,误差在5%左右;应用ARIMA(2,2,2)预测广州市1999—2008年的人均水生态足迹,结果分别为:0.5935,0.656 2,0.714 7,0.779 0,0.840 2,0.901 55,0.964 11,1.025 57,1.087 44,1.149 43 hm2,预测结果表明广州市1999—2008年期间的人均水生态足迹仍呈较快上升趋势,居民对于水产品的需求提高以及城市发展、人口增加等社会经济因素是导致这种趋势产生的主要原因。     

ARIMA模型在苏州河总磷预测中的应用

目前苏州河面临潜在的富营养化危机,氮、磷含量较高的污染源是富营养化形成的直接诱因.选择总磷指标为研究对象,介绍运用时间序列分析法对总磷进行ARIMA建模预测,确定ARIMA(11,0,0)即AR(11)为最终模型,用1986～2003年数据对2004和2005年进行预测,结果得出苏州河总磷在未来2年呈波动下降趋势,最终在0.4～0.6 mg/L之间上下波动,但仍高于地面水Ⅴ类标准.模型适用于苏州河总磷的短期预测,完善苏州河富营养化预测管理系统.     

DROUGHT EPISODES MANAGEMENT: THE ROLE OF PRICE1

ABSTRACT: Municipal water utilities, when faced with drought conditions, typically impose a temporary water use restrictions program to achieve conservation goals. If water is sufficiently price-elastic, however, at least some of the problems associated with restrictions can be avoided by imposing a drought surcharge and allowing users to adjust voluntarily. This paper develops two sources of evidence on price elasticity in Honolulu, Hawaii, with the evidence suggesting that a drought surcharge will induce much of the desired conservation, especially when used with educational publicity.     

污泥干化芦苇床渗滤液中CODCr和TP浓度预测模型

为便于水质管理,在大连开发区污水处理厂设立了1个传统污泥干化床和2个污泥干化芦苇床,根据系统出水ρ(COD_Cr)和ρ(TP)检测数据的平稳系列特点,采用ARIMA时间序列分析模型对出水ρ(COD_Cr)和ρ(TP)进行模拟和预测. 结果表明:ARIMA(1,0,0)模型可较好地模拟和预测出水ρ(COD_Cr)的变化,R2达0.577以上,预测相对误差在-10%~19%之间,实测值和预测值之比处于0.8~1.2之间;ARIMA(6,0,0)模型能较好地模拟和预测污泥处理湿地渗滤液出水ρ(TP)的变化,R2在0.487以上,预测相对误差在-4%~16%之间,实测值和预测值之比处于0.8~1.2之间. 研究显示,所构建的ARIMA模型可用于污泥处理湿地系统运行过程中渗滤液出水ρ(COD_Cr)和ρ(TP)的预测.      

Accuracy improvement of various short-term load forecasting models by a novel and unified statistical data-filtering method

ABSTRACT

Time-series and machine-learning methods are being strongly exploited to improve the accuracy of short-term load forecasting (STLF) results. In developing countries, power consumption behaviors could be suddenly changed by different customers, e.g. industrial customers, residential customers, so the load-demand dataset is often unstable. Therefore, reliability assessment of the load-demand dataset is obviously necessary for STLF models. Hence, this paper proposes a novel and unified statistical data-filtering method with the best confidence interval to eliminate unexpected noises/outliers of the input dataset before performing various short-term load forecasting models. This proposed novel data-filtering method, so-called the data pre-processing method, is also compared to other existing data-filtering methods (e.g. Kalman filter, Density-Based Spatial Clustering of Applications with Noise, Wavelet transform, and Singular Spectrum Analysis). By using an SCADA system?-based database of a typical 22kV distribution network in Vietnam, NYISO database, and PJM-RTO database, case studies of short-term load forecasting have been conducted with a conventional ARIMA model, an ANN forecasting model, an LSTM-RNN model, an LSTM-CNN combined model, a deep auto-encoder (DAE) network, a Wavenet-based model, a Wavenet and LSTM hybrid model, and a Wavelet Neural Network (WNN) model, which are to validate the novel and unified statistical data-filtering method proposed. The achieved numerical results demonstrate which the accuracy of the aforementioned STLF models can be significantly improved due to the proposed statistical data-filtering method with the best confidence interval of the input load dataset. The proposed statistical data-filtering method can considerably outperform the existing data-filtering methods.     

利用ARIMA(自回归移动平均)模型对跑道侵入事件的分析及预测

综合运用具有相当精度的时间序列分析方法,建立美国民航运输安全中的机场跑道侵入的AR IMA(自回归移动平均)模型,克服了样本空间总是有限带来的不足,揭示出民航跑道侵入的动态变化规律,并对未来美国民航跑道侵入事故发生次数进行较准确的预测,为我国民航部门科学地制定飞行计划、人员培训、提高安全管理水平,提供可靠的依据。     

民航安全状况与社会经济指标关联分析

为了分析中国民航的安全趋势,基于中国民航1995-2014年安全生产历史数据和民航安全运行关系密切的26项社会经济指标,利用因子分析、相关系数等数学方法,建立了多元线性回归模型分析民航安全状况与社会经济指标的管理关联性。该模型表明对民航安全具有显著影响的社会经济指标包括国民经济、产业结构和人员素质。通过对比安全生产指标的拟合值和实际值,分析民航安全趋势的变化规律,并利用自回归移动平均模型预测2015-2018年民航安全生产指标值。计算结果显示该时间段内民航安全生产指标将处于历史高位,且呈现缓慢上升趋势。最后给出了针对行业安全监管、安全运行等方面的改进建议。