Short-Term Electric Load Forecasting Based Trends Combination Model

In short term load forecasting based on ANN,weather is one of the important factors which impacts on load greatly. In order to capture the effect of weather on load, this paper presents a novel thought based on ANN and trends combination short term load forecasting. Decompose the underlying relationships between load and weather variables into three main trends of weekly, daily and hourly. Three separated ANNs capture each trend. Another ANN to arrive at the final forecast combines the forecasts yielded by individual ANNs. The performances of the proposed model and the traditional model are compared on the basis of one week ahead hourly forecasts. Results indicate that the proposed ANN based model can achieve greater forecasting accuracy than the traditional ANN based model.     

基于特征分量组合的多小水电地区日负荷预测

随着小水电大规模并网,其无序发电对电网造成的冲击越加不可忽视,提高小水电负荷预测的准确率对掌握小水电的发电情况及电网调度有着重要意义。将小水电预测日的负荷解耦为基值和标幺值两部分进行预测,并引入云模型方法对基值的偏差进行拟合。同时,针对标幺化后的负荷曲线,采用集成经验模态获取其特征分量,并通过对相似日和日前负荷曲线的特征向量进行交叉组合以快速获得预测日的标幺曲线。通过对湖南某富小水电地区进行实例分析,验证了本预测方法的有效性。     

基于遗传对角回归神经网络的农机总动力预测

利用遗传算法强全局随机搜索特点,结合DRNN神经网络对非线性数据具有鲁棒性和自学习能力的优点,通过将历年农机总动力数据作为时间序列进行分析,建立DRNN神经网络预测模型对农机总量进行预测。本文采用遗传算法对DRNN神经网络进行训练,可克服基于梯度算法的神经训练算法的缺点,收敛速度快,可达到全局最优。通过与校验用数据的比较证明本文建立的预测模型具有较高的精度。     

灰色系统理论在农业需水量预测中

应用灰色理论建立了GM(1，1)模型，对华北地区某典型区农业用水量进行了预测。所建模型经残差检验证明其模型具有可行性和适应性，在此基础上，通过对典型区农业需水量的预测，为研究区域的水资源综合规划提供了依据。该方法可供相关县级小区域水资源评价与规划中作为参考。     

地区农网变电规模预测研究及软件设计

如何合理预测和研究变电规模的大小以及各量之间的优化配置,是构成一个安全、可靠及经济网架的重要基础,可提高整个电力系统的经济效益和社会效益.为此,首先对传统的中长期电量预测进行改进,以不同供电模式进行用电量分类,针对不同负荷特点选用恰当的数学方法进行电量预测.在目标年电量的基础上,结合其它影响因素配置变电规模,对电网的中长期发展建设提出参考性的建议.     

基于WA-SVM的流域降雨序列预测研

为了提高降雨中长期预测精度，将小波分析和支持向量机回归方法引入水文序列预测领域，给出了两种方法的思路和特点。在此基础上，尝试建立了基于小波分析-支持向量机（WA-SVM）的降雨量序列预测模型。通过小波分解，将原始复杂的降雨序列分解到不同的频率层次，对每层得到的分解序列分别采用支持向量机回归方法进行预测，最后合成原始序列的预测值。将该模型应用于实际流域月降雨量预测，并与单独支持向量机回归方法预测结果进行比较，表明该方法预测精度有明显提高。     

组合预测方法及其在粮食单产分析中的应用

介绍组合预测模型及最优权重的确定，并通过此方法对黑龙江省某农场粮食单产进行了预测。建立了组合预测模型。从预测结果上看，组合预测模型优于单一预测模型，使预测精度有一定的提高。     

基于混合建模方法的水电负荷预测

针对水电企业电力负荷预测的困难，提出了一种将AR模型与T—S模糊神经模型相结合的负荷预测新方法。通过对某水电厂近两年发电量作的预测表明，该方法具有较好的鲁棒性、较高的精度和实用前景。     

Comparison of Time Series Models for Predicting Campylobacteriosis Risk in New Zealand

Predicting campylobacteriosis cases is a matter of considerable concern in New Zealand, after the number of the notified cases was the highest among the developed countries in 2006. Thus, there is a need to develop a model or a tool to predict accurately the number of campylobacteriosis cases as the Microbial Risk Assessment Model used to predict the number of campylobacteriosis cases failed to predict accurately the number of actual cases. We explore the appropriateness of classical time series modelling approaches for predicting campylobacteriosis. Finding the most appropriate time series model for New Zealand data has additional practical considerations given a possible structural change, that is, a specific and sudden change in response to the implemented interventions. A univariate methodological approach was used to predict monthly disease cases using New Zealand surveillance data of campylobacteriosis incidence from 1998 to 2009. The data from the years 1998 to 2008 were used to model the time series with the year 2009 held out of the data set for model validation. The best two models were then fitted to the full 1998–2009 data and used to predict for each month of 2010. The Holt‐Winters (multiplicative) and ARIMA (additive) intervention models were considered the best models for predicting campylobacteriosis in New Zealand. It was noticed that the prediction by an additive ARIMA with intervention was slightly better than the prediction by a Holt‐Winter multiplicative method for the annual total in year 2010, the former predicting only 23 cases less than the actual reported cases. It is confirmed that classical time series techniques such as ARIMA with intervention and Holt‐Winters can provide a good prediction performance for campylobacteriosis risk in New Zealand. The results reported by this study are useful to the New Zealand Health and Safety Authority's efforts in addressing the problem of the campylobacteriosis epidemic.     

灰色理论在土地利用规划中的应用——以河北卢龙县为例

以GDP预测为例,首先建立了卢龙县国内生产总值预测的灰色模型,对2005～2020年卢龙县国内生产总值作了预测;然后.利用灰色关联度分析,分析了卢龙县第一、二、三产业对国内生产总值的影响程度,提出了该县产业结构的调整方向.该研究可为土地利用规划提供科学依据.