A novel approach for short-term load forecasting using support vector machines

A support vector machine (SVM) modeling approach for short-term load forecasting is proposed. The SVM learning scheme is applied to the power load data, forcing the network to learn the inherent internal temporal property of power load sequence. We also study the performance when other related input variables such as temperature and humidity are considered. The performance of our proposed SVM modeling approach has been tested and compared with feed-forward neural network and cosine radial basis function neural network approaches. Numerical results show that the SVM approach yields better generalization capability and lower prediction error compared to those neural network approaches.     

Improved Neural Networks with Random Weights for Short-Term Load Forecasting

An effective forecasting model for short-term load plays a significant role in promoting the management efficiency of an electric power system. This paper proposes a new forecasting model based on the improved neural networks with random weights (INNRW). The key is to introduce a weighting technique to the inputs of the model and use a novel neural network to forecast the daily maximum load. Eight factors are selected as the inputs. A mutual information weighting algorithm is then used to allocate different weights to the inputs. The neural networks with random weights and kernels (KNNRW) is applied to approximate the nonlinear function between the selected inputs and the daily maximum load due to the fast learning speed and good generalization performance. In the application of the daily load in Dalian, the result of the proposed INNRW is compared with several previously developed forecasting models. The simulation experiment shows that the proposed model performs the best overall in short-term load forecasting.     

棉铃虫羽化高峰期有效积温预测法的校正

为减小年际间气温变化对昆虫有效积温预测误差的影响,以新疆石河子垦区121团棉铃虫Helicoverpa armigera(Hübner)羽化高峰期为例,利用single sine模型分别计算12年2种有效积温范围(10～30℃和10～35℃)的累计有效积温值,并获得其多年平均值,依此进行棉铃虫羽化高峰期预测;通过当年与12年(有效积温>0日期至羽化高峰日期)平均气温之差,对预测误差进行校正。结果表明:当年平均气温与12年平均值差值越大,预测误差也越大;各代直线回归校正模型均达到显著水平(P<0.05);2种有效积温范围下,校正后各代平均预测误差天数均有所减少,对越冬代误差校正效果最优,校正后各代历史符合率分别为83.33%、100%、100%和100%、100%、93.33%。该校正方法能够显著提高预测准确度,尤其适用于年际间棉铃虫发育期间平均气温变化较大的代别和地区,同时可为多种害虫预测误差校正提供了依据。     

An ensemble data assimilation modeling system for operational outdoor microalgae growth forecasting

Microalgae have received increasing attention as a potential feedstock for biofuel or biobased products. Forecasting the microalgae growth is beneficial for managers in planning pond operations and harvesting decisions. This study proposed a biomass forecasting system comprised of the Huesemann Algae Biomass Growth Model (BGM), the Modular Aquatic Simulation System in Two Dimensions (MASS2), ensemble data assimilation (DA), and numerical weather prediction Global Ensemble Forecast System (GEFS) ensemble meteorological forecasts. The novelty of this study is to seek the use of ensemble DA to improve both BGM and MASS2 model initial conditions with the assimilation of biomass and water temperature measurements and consequently improve short-term biomass forecasting skills. This study introduces the theory behind the proposed integrated biomass forecasting system, with an application undertaken in pseudo-real-time in three outdoor ponds cultured with Chlorella sorokiniana in Delhi, California, United States. Results from all three case studies demonstrate that the biomass forecasting system improved the short-term (i.e., 7-day) biomass forecasting skills by about 60% on average, comparing to forecasts without using the ensemble DA method. Given the satisfactory performances achieved in this study, it is probable that the integrated BGM-MASS2-DA forecasting system can be used operationally to inform managers in making pond operation and harvesting planning decisions.     

麦蚜复合种群动态预测的Fuzzy推理模式及应用

本研究利用了山东省曲阜地区１９８２－１９９４年共１３年的资料，选用了３月下旬至４月上旬平均气温（℃）和４月７上旬温湿系数作为预报因子，麦蚜蚜量始达５００（头／丰株）的日期作为预报对象组建Ｆｕｚｚｙ推理模式。对历史资料进行回代验证，其历史拟合率达１００％。将１９９５年的观测数据作为独立样本进行试报，预测结果与实际一致。为麦蚜复合种群动态预测提供了一种新的研究方法。     

Tea Plantation Frost Damage Early Warning Using a Two-Fold Method for Temperature Prediction

As the source and main producing area of tea in the world, China has formed unique tea culture, and achieved remarkable economic benefits. However, frequent meteorological disasters, particularly low temperature frost damage in late spring has seriously threatened the growth status of tea trees and caused quality and yield reduction of tea industry. Thus, timely and accurate early warning of frost damage occurrence in specific tea garden is very important for tea plantation management and economic values. Aiming at the problems existing in current meteorological disaster forecasting methods, such as difficulty in obtaining massive meteorological data, large amount of calculation for predicted models and incomplete information on frost damage occurrence, this paper proposed a two-fold algorithm for short-term and real-time prediction of temperature using field environmental data, and temperature trend results from a nearest local weather station for accurate frost damage occurrence level determination, so as to achieve a specific tea garden frost damage occurrence prediction in a microclimate. Time-series meteorological data collected from a small weather station was used for testing and parameterization of a two-fold method, and another dataset acquired from Tea Experimental Base of Zhejiang University was further used to validate the capability of a two-fold model for frost damage forecasting. Results showed that compared with the results of autoregressive integrated moving average (ARIMA) and multiple linear regression (MLR), the proposed two-fold method using a second order Furrier fitting model and a K-Nearest Neighbor model (K = 3) with three days historical temperature data exhibited excellent accuracy for frost damage occurrence prediction on consideration of both model accuracy and computation (98.46% forecasted duration of frost damage, and 95.38% for forecasted temperature at the onset time). For field test in a tea garden, the proposed method accurately predicted three times frost damage occurrences, including onset time, duration and occurrence level. These results suggested the newly-proposed two-fold method was suitable for tea plantation frost damage occurrence forecasting.     

Short-term fast forecasting based on family behavior pattern recognition for small-scale users load

Household electricity consumption has been rising gradually with the improvement of living standards. Making short-term load forecasting at the small-scale users plays an increasingly important role in the future power network planning and operation. To meet the efficiency of the dispatching system and the demand of human daily power consumption, an optimal forecasting model Attention-CNN-GRU of small-scale users load at various periods of the day based on family behavior pattern recognition is proposed in this study. The low-level data information (smart meter data) is used to build the high-level model (small-scale users load). Attention mechanism and convolutional neural networks (CNN) can further enhance the prediction accuracy of gated recurrent unit (GRU) and notably shorten its prediction time. The recognition of family behavior patterns can be achieved through the users’ smart meter data, and users are aggregated into K categories. The results of optimal K category prediction under the family behavior model are summarized as the final prediction outcome. This idea framework is tested on real users’ smart meter data, and its performance is comprehensively compared with different benchmarks. The results present strong compatibility in the small-scale users load forecasting model at various periods of the day and swift short-term prediction of users load compared to other prediction models. The time is shortened by 1/4 compared with the GRU/LSTM model. Furthermore, the accuracy is improved to 92.06% (MAPE is 7.94%).

     

New technique for measuring dynamic axonal transport and its application to temperature effects.

A new technique was devised for the dynamic detection of the axoplasmic transport of beta-radioactively labeled materials in which a semiconductor radiation detector was used as the beta-ray counter. The detector element is a silicon p-n junction diode and has a diameter of 2.0 mm. With this detector, the beta-radioactive distribution of axoplasmic transport could be measured in a axon maintained physiologically without cutting nerves. This method makes possible determination of the transport rate using one bundle of peripheral nerves. The rate in the bullfrog was 6.4 mm per hour at 24.0 degrees D. Temperature effects on the bullfrog axoplasmic transport were also observed at different temperatures, ranging from 5.0 to 24.0 degrees C. At these temperatures the rate increased as an exponential function of temperature from 1.1 to 6.4 mm per hour. Within this temperature range, the Q10 is 2.5 and an Arrhenius plot of the natural logarithm of velocity versus the reciprocal of absolute temperature yielded an apparent activation energy of 14.8 Kcal. this technique offers great advantages in permitting direct study of the axoplasmic flow of the axon in a physiological condition.     

A hierarchical spatiotemporal analog forecasting model for count data

Analog forecasting is a mechanism‐free nonlinear method that forecasts a system forward in time by examining how past states deemed similar to the current state moved forward. Previous applications of analog forecasting has been successful at producing robust forecasts for a variety of ecological and physical processes, but it has typically been presented in an empirical or heuristic procedure, rather than as a formal statistical model. The methodology presented here extends the model‐based analog method of McDermott and Wikle (Environmetrics, 27, 2016, 70) by placing analog forecasting within a fully hierarchical statistical framework that can accommodate count observations. Using a Bayesian approach, the hierarchical analog model is able to quantify rigorously the uncertainty associated with forecasts. Forecasting waterfowl settling patterns in the northwestern United States and Canada is conducted by applying the hierarchical analog model to a breeding population survey dataset. Sea surface temperature (SST) in the Pacific Ocean is used to help identify potential analogs for the waterfowl settling patterns.     

物候模式识别在生态动力预报中的应用

以物候资料和数值天气预报模式输出图为基础,应用模式识别和数理逻辑判断的自动化技术,阐述制作生态动力预报的原理、方法和步骤.生态动力预报技术使传统的物候学在气象学和自动化技术支持下,扩展应用到生态预报业务领域,使物候预报从单站预报阶段发展到区域预报阶段,同时促进了农业气象预报方法从定性、统计阶段向动力预报阶段发展.该方法在农作物播种、长势、灌溉与施肥、病虫害防治等方面具有广阔的应用前景.     

樱花始花期预报方法

根据对1981—2016年36年武汉大学樱园日本樱花始花期的记录资料及同期气象资料的研究分析表明:(1)樱花始花期提前,但变化趋势不明显,变率特别大,平均始花期为3月14至15日(闰年为13至14日);(2)为改进始花期预报方程,计算1月1日及2月1日至开花前期2月25日、2月底、3月5日、3月10日、3月15日的活动积温,发现积温与始花期相关性显著,可作为樱花始花期预报方程的因子;(3)分析始花期与1月1日及2月1日至开花前期2月25日、2月底、3月5日、3月10日、3月15日累计日照时数关系,发现始花期与累计日照时数呈负相关;(4)用活动积温作为预报因子改进始花期预报方程预报始花期,有效地提高了预报准确率。     

拓扑预测阀值ξi的确定及广西八角丰歉年预测

本文将灰色系统理论中拓扑预测的阀值ξi从一常量延伸为一变量,使拓扑预测的功能得到拓广,并利用拓广后的方法对广西八角产量的大小年作了预测,并获得较高的精度。     

New technique for measuring dynamic axonal transport and its application to temperature effects

A new technique was devised for the dynamic detection of the axoplasmic transport of β-radioactively labeled materials in which a semiconductor radiation detector was used as the β-ray counter. The detector element is a silicon p-n junction diode and has a diameter of 2.0 mm. With this detector, the β-radioactive distribution of axoplasmic transport could be measured in an axon maintained physiologically without cutting nerves. This method makes possible determination of the transport rate using one bundle of peripheral nerves. The rate in the bullfrog was 6.4 mm per hour at 24.0 °C. Temperature effects on the bullfrog axoplasmic transport were also observed at different temperatures, ranging from 5.0 to 24.0 °C. At these temperatures the rate increased as an exponential function of temperature from 1.1 to 6.4 mm per hour. Within this temperature range, the Q₁₀ is 2.5 and an Arrhenius plot of the natural logarithm of velocity versus the reciprocal of absolute temperature yielded an apparent activation energy of 14.8 Kcal. This technique offers great advantages in permitting direct study of the axoplasmic flow of the axon in a physiological condition.     

GIS支持下青海湖地区草地蝗虫发生与月均温的相关性

为有效地进行草地蝗虫发生的预测预报,必须摸清其生长和繁殖与地理环境特征的关系,在青海湖地区,气温是影响草地蝗虫发生的主要因素,在Arc/Info和ArcView地理信息系统的支持下,选择了环湖地区邻近的16个气象站点,采用综合方法,在小尺度上模拟该区所需月份的月均温,建立了空间分布式气温信息数据库,然后,把野外调查的蝗虫密度在空间数据与相对应的月均温空间数据进行叠加,计算并分析月均温与草地蝗虫发生的关系。结果表明,月均温对草地蝗虫发生的影响是和该区蝗虫优势种的生命史密切相关的,即5、6月（孵化期）和7月（蝗蝻期）的月均温影响当年草地蝗虫发生;8、9月（交尾、产卵期）的月均温则影响次年草地蝗虫的发生,为该区草地蝗虫发生预报模型的建立提供依据。     

Rapid isolation of mesophyll cells from leaves of soybean for photosynthetic studies

Mesophyll cells were rapidly isolated from soybean (Glycine max [L.]) leaves using a combined Macerase enzyme-stirring technique. About 50% to 70% of the leaf cells on a chlorophyll basis from 3 grams of leaves could be isolated in 15 minutes. The cells obtained by this method were capable of high rates of photosynthesis even after storage in the dark for periods of up to 9 hours. The CO₂-saturated rate of photosynthesis increased from 5 μm CO₂/mg Chl·hour at 5 C to 170 μm CO₂/mg Chl·hour at 40 C. At atmospheric CO₂ concentration, the rate varied from 5 to 55 μm CO₂/mg Chl·hour over this temperature range. The reduced temperature response of photosynthesis at low CO₂ concentration was due to an increased Km(CO₂) of the cells with increasing temperature. The products of photosynthesis in the isolated cells were similar to the products of leaf photosynthesis.     

Airborne fungal spores in an industrial area: seasonal and diurnal periodicity

Qualitative and quantitative studies of atmospheric fungal spores at a chloralkali factory, Jayashree Chemicals. were made during 1993 employing culture plate and rotorod methods. A total of 57 sporulating fungal types, including three sterile mycelial forms, were recorded by the culture plate method and 51 spore types, including the hyphal fragments and unidentified spores, were recorded by the rotorod method. As to the seasonal variation, winter was found to be the greatest contributor of fungal spores as compared to the summer and rainy season. Instead, when considering the hour of the day, the peak number of fungal propagules was recorded at noon (12.00 h) followed by evening and morning values, an exception being recorded in winter months, when maximum CFUs ofCladosporium were monitored in the morning. The seasonal variation in fungal concentration and composition was found to be influenced by temperature, rainfall and relative humidity, whereas diurnal incidence was the effect of varying temperature and relative humidity during day time only. Moderate temperature and relative humidity favoured the maximum fungal spore load in the atmosphere.Cladosporium, Nigrospora, Alternaria, Lasiodiplodia, Drechslera, Pestalotia, Curvularia, Epicoccum, Aspergillus, Penicillium andChaetomium were the commonest fungal spores in the factory area.     

A Hierarchical Bayesian Model to Quantify Uncertainty of Stream Water Temperature Forecasts

Providing generic and cost effective modelling approaches to reconstruct and forecast freshwater temperature using predictors as air temperature and water discharge is a prerequisite to understanding ecological processes underlying the impact of water temperature and of global warming on continental aquatic ecosystems. Using air temperature as a simple linear predictor of water temperature can lead to significant bias in forecasts as it does not disentangle seasonality and long term trends in the signal. Here, we develop an alternative approach based on hierarchical Bayesian statistical time series modelling of water temperature, air temperature and water discharge using seasonal sinusoidal periodic signals and time varying means and amplitudes. Fitting and forecasting performances of this approach are compared with that of simple linear regression between water and air temperatures using i) an emotive simulated example, ii) application to three French coastal streams with contrasting bio-geographical conditions and sizes. The time series modelling approach better fit data and does not exhibit forecasting bias in long term trends contrary to the linear regression. This new model also allows for more accurate forecasts of water temperature than linear regression together with a fair assessment of the uncertainty around forecasting. Warming of water temperature forecast by our hierarchical Bayesian model was slower and more uncertain than that expected with the classical regression approach. These new forecasts are in a form that is readily usable in further ecological analyses and will allow weighting of outcomes from different scenarios to manage climate change impacts on freshwater wildlife.     

不同的高温模式对不同密度棉蚜存活和繁殖的影响

为探索趋近自然状态的渐变性高温胁迫对不同密度棉蚜Aphis gossypii Glover的影响,室内研究了4种不同高温模式下,不同密度(5、10、20、40)棉蚜的存活和繁殖。结果表明:随着最高温度值的升高和密度的增加,棉蚜存活率和繁殖率均呈下降趋势。当最高温度值升至40℃以上时,棉蚜存活率和繁殖率均显著下降,不同密度棉蚜存活率和繁殖率均没有差异。即随着温度的升高,密度对棉蚜的作用逐渐减弱。最高温度值为42℃时,棉蚜在3～4 d内全部死亡。研究结果为提高棉蚜种群预测准确性、科学决策防治措施提供依据。     

树木花期预报的花芽形态测量法研究——以大山樱花期预报为例

树木花期预报在林果、养蜂、园林和旅游业等方面有很大的实用价值。该文以大山樱(Prunus sargentii)为例,探讨通过花芽形态测量进行花期预报的新方法。通过1998~2000年对北京玉渊潭公园大山樱进行的数据采集和处理,建立了线性和指数两种预报模型。2002年的试报检验表明,采用3株的观测数据,并利用3日滑动平均的方法,对观测数据进行处理后所作的预报,误差在3 d以内的预报达80%以上;2003年连续测报的平均误差,模型1为1.6 d,模型2为2.1 d。这一树木花期预报的物候学新方法,简便易行、建模周期短、预报精度高,在春季芽膨大后,直至露瓣期之前,可以逐日连续发布预报。     

北京市北环水系富营养化因子分析

以北京市北环水系水体为例,利用聚类分析将研究区分为河流子系统和湖泊子系统.因子分析表明,河流子系统第一主成分富营养元素为总磷(TP)、总氮(TN)和氨氮(NH₄-N),第二主成分为温度(T)和溶解氧(DO);湖泊子系统第一主成分为总氮和氨氮,第二主成分为总磷、酸碱度(pH)、透明度(SD)和温度,第三主成分为溶解氧和叶绿素a(Chla),表明研究区的水体富营养化主要由富营养盐负荷引起.结合逐步回归分析方法,建立富营养水平预测回归模型,根据模型自变量选择证明河流子系统富营养化特征为磷限制型,湖泊子系统为氮限制型.从水量和水质上对营养盐浓度负荷变化分析表明,研究区年最小生态环境用水为4872×10⁴m³,1990～1998年,除1998年外,现实的生态环境需水均不能满足需求.随着流域人口的不断增长,生活污水、城市径流和固体废弃物淋溶液中营养物质进入水体,研究区营养盐浓度负荷有随时间不断增长的趋势,针对这种趋势提出了应对措施.