Cellular automata spatial temporal data model and forecasting approach

The standard cellular automata(CA) model is expanded to meet requests of space time dynamic simulation and forecast under the platform of geographic information system(GIS). Taking power load forecasting of the electric power industry as the specific application, the relations between dynamic model of the land use and power load space are established. The data and attribute data interactive discrete in spatial temporal data management have been solved. The CA theory is practically used to simulate the process of urban land use dynamic development, to forecast future land use types of each small area, to establish spatial load forecasting model. It breaks through the localization of all kinds of forecasting methods of traditional space time separation power prediction. The effectiveness of the prediction method is verified by example.     

Application of Combination Grey Model in Power Load Forecasting

Based on the gray forecast theory, this paper studies the principle and deficiency in power load forecasting by the basic grey model and other improved models, and introduces a new method -the combination grey model to forecast the long-medium power load. Based on an example, the basic grey model, other improved models and combination grey model are used to forecast power load and results of all models are analyzed and compared. The calculation results show that forecasting power load by grey theory is credible and simple. For this type of complex problems such as forecasting the long-medium power load, the combination grey model is specially useful because of it's high precision and facility. The method can be used as one of the tools of forecasting the long-medium power load.     

Intelligent Extracting Method for Feature Mode of Electric Days Load

Generally there have a number of bad data in the electric load data and it affects the precision of load forecasting,so it is necessary for extracting the feature mode of days load data,then cleaning the load data before it is used to forecasting electric load or performing power system analysis.Inspired by soft clustering thought,a intelligent feature mode of days load data extracting method is proposed based on the mutual offset of fuzzy c-means clustering arithmetic and Kohonen self organization feature map neural network.With the merits of not only high extracting precision and convergent speed but also dynamic calculation capability,the method proposed can supply load forecasting or system analysis procedure with due data.Test results using actual data of Chengqu power supply bureau in Chongqing demonstrate the effectivity and feasibility of the method.     

A Fuzzy Regression Method for Long-Term and Medium-Term Load Forecasting

In view of the observation data fuzziness and load pattern fuzziness,a new fuzzy regression prediction method was presented for long-term and medium-term load forecasting. With the established fuzzy regression model, the future load value can be forecasted based on the fuzzy historical observation data. The validity of the proposed method was verified with the numerical example of a practical system.     

Processing Fault Data Method in Load Forecasting Based on Wavelet Analysis

The reliability and reality of load historical data is the foundation of load forecasting.But,the impact load in running power system,and the disturb data in collecting load data through the SCADA may cause much fault data in load historical data. Focusing on solving this problem, a method through adjusting amplitade of its wavele modulus maxima and processing the wavelet decomposed detail signal by soft threshold based on wavelet analysis and singularity theory, then fault date can be eliminated,so that,the real historical imformation and regulation data can be gained by load forecasting.     

Application of Attribute Reduct Method in Spatial Load Forecasting

Spatial load forecasting is a process distributing the total forecasted load to all partitioned area, and involving more spatial information and more factor influencing application of the future small area, which need a great deal of memory space and longer operation time. Rough set is new method of data analysis. It need not be provided with any advanced information except data set. But attribute reduct is its main algorithms. Division matrix approach on rough set used to reduce the attribute related to land - use decision in order to remove redundancy attribute and then the rules of small area land - use decision is distilled. The method obtained better effect and enhanced the total load forecasting efficiency.     

A MULTI -OBJECTIVE FUZZY DECISION -MAKING APPROACH FOR POWER NETWORK PLANNING UNDER UNCERTAINTY

This paper proposes a multi - objective fuzzy decision - making approach for power network planning under uncertainty ,which simutaneously considers not only the least investment cost,the minimum power loss,the maximum reliability,and the least environmental impacts, but also the uncertainty about the future load growth and the capital investment availability. The validity and effectiveness of the proposed approach is verified with numerical examples.     

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.     

Local Nonlinear Forecasting of Short-term Power Load Forecasting

The nearest points in phase space are determined by Euclid distance in chaotic local prediction. The prediction accuracy depends on quality of the nearest points. But the shortest distance does not imply better forecasting effect. While false nearest neighboring point or high embedding dimensions appear evolvement track of some nearest neighboring point should be apart from prediction point. Because it is difficult for Euclid distance to reflect the correlation degree between the nearest points and prediction point. So the idea of combining Euclid distance with correlation degree is put forward. The method is applied to short-term electrical load forecasting. The result of load series forecasting by the presented method is more effective to improve prediction accuracy.     

大型商场建筑夏季冷负荷动态预测模型

夏季建筑冷负荷的正确预测是实现大型复杂中央空调优化运行、节能降耗的关键。笔者探讨了商场建筑冷负荷的主要影响因素,确定了建筑动态冷负荷预测模型的输入,提出了夏季基于新风机组供电频率的商场顾客率间接测量方法,解决了商场内顾客量难以检测的难题。还提出了AFC-HCMAC神经网络预测模型算法,实现了大型商场建筑冷负荷的动态预测。仿真结果表明:顾客率在商场冷负荷预测中占有重要地位,在冷负荷预测模型中增加商场顾客率可显著提高预测精度;AFC-HCMAC神经网络预测算法与传统的HCMAC神经网络算法比较,可有效降低神经网络节点数,提高预测精度。     

几种灰色模型在农村生活垃圾产量预测中的应用

为了能更准确地把握未来生活垃圾量的趋势和发展动态,协助农村环境污染的治理,笔者结合实地考察某些示范村得到的实际数据,通过建立4种不同的灰色模型对数据进行比较分析,确定了一种最适合预测农村人均生活垃圾产量的模型。结果表明,开平方变换法灰色模型更加适合对农村人均生活垃圾产量进行中长期预测,并精确预测出未来5年的农村人均生活垃圾产量,针对预测结果提出了治理农村生活垃圾的建议。     

A New Method for Determining the Typical Daily Load Curves

After analyzing the ten-year load data of a real power system, a novel method, with the help of Edgeworth progression has been developed to determine the representative daily load curve, which is useful in power generation planning. The method is successful to avoid the random and subjective factors in the exiting method of curve-making.     

Optimal Standby Capacity and Its Valuation in Power Pool

The problem of optimal standby capacity and its valuation in Power Pool is very important and complicated. On the basis of researching literatures concerned, this paper constructs an optimal standby capacity model and researches the problem of pricing optimal standby capacity and determining power price on the user-side. The result shows that the optimal standby capacity at some period t is completely determined by the probability distribution density function of power load demand at this period and surplus power loss and insufficient power loss, and the price of optimal standby capacity is determined by real power price of entering power grid and the power price of forecasting load demand, and the expected power price on the user-side consists of the settlement price with grid loss on generation-side and reasonable gains and expected additional loss.     

Application of data mining method in power load bad data intelligent identification and correction

There is a number of bad data in the load database produced, thus the data must be cleaned before it is used to forecasting electric load or performing power system analysis. The WKFCM measures distance by kernel functions instead of the complicated Euclidean distance and this kernel based distance is used as dissimilarity function of target clustering formula which can reduce the calculation complexity. After the clustering, a super circle covering neural network based identification model for load data is proposed, and the bad data is modified. It is proved that the proposed data processing model has good effect.     

Load Forecasting Based on BP ANN for Economical Operation of Electric Boiler with Heat Reservoir Under Time-sharing price

Along with the generalization of DSM and time-sharing price system, the use of electric boiler with heat reservoir becomes more and more extensive. Load forecasting of heat supply system is an important base in the study of economical operation of electric boiler with heat reservoir under time-sharing price. The BP ANN modeling of the load forecasting for a 1200 kW electric boiler is discussed. The result of hourly heat load forecasting accords well with the real heat load.     

自适应加权最小二乘支持向量机的空调负荷预测

为了提高建筑空调负荷的预测精度,在分析空调负荷主要影响因素的基础上提出了一种基于自适应加权最小二乘支持向量机(AWLS-SVM)的建筑空调负荷预测方法。该方法根据预测误差的统计特性,采用基于改进正态分布加权规则,自适应地赋予每个建模样本不同的权值,以克服异常样本点对模型性能的影响。建模过程中采用粒子群优化(PSO)算法对模型参数进行优化,以进一步提高模型预测精度。基于DeST模拟数据将AWLS-SVM方法应用于南方地区某办公建筑的逐时空调负荷预测中,并与径向基神经网络(RBFNN)模型、LS-SVM模型及WLS-SVM模型作比较,其平均预测绝对误差分别降低了51.84 %、13.95 %和3.24 %,并进一步基于实际空调负荷数据将该方法应用于另一办公建筑的逐日空调负荷预测中。预测结果表明：AWLS-SVM预测的累积负荷误差为4.56 MW,亦优于其他3类模型,证明了AWLS-SVM具有较高的预测精度和较好的泛化能力,是建筑空调负荷预测的一种有效方法。     

Application of modified Deep Belief Network in Forecasting Cotton Diseases and Insect Pests

[Objective] The occurrence and development of cotton diseases and insect pests are mainly related to environmental information. Because this environmental information is various, complex and unstable, the study on the prediction methods of cotton diseases and insect pests is a certain challenge. This study aims to establish a forecasting model for the timely and accurate prediction of cotton diseases and insect pests. [Method] A forecasting model of cotton diseases and insect pests is proposed based on environmental information and a modified Deep Belief Network (DBN) that is constructed by a three-layer restricted Boltzmann machine (RBM) and a supervised back-propagation (BP) network. In the method, the RBM is used to transform the original environmental information vectors into a new feature space related to the diseases and pests; the BP network is trained to classify and forecast the features generated by the last RBM layer and two rules of dynamic learning and comparison and dispersion are adopted to accelerate the training process of RBM. The proposed model was validated on a dataset of cotton bollworm, aphids, spider, cotton Verticillium wilt, and Fusarium wilt in a recent six-year period. [Result] Compared with the traditional prediction models of cotton diseases and insect pests, the proposed model can deeply explore the extensive correlation between the occurrence of cotton diseases and pests and environmental information. The results show that the proposed model has a higher accuracy compared with the classical predictive models, and the average forecasting accuracy is above 83%. [Conclusion] The proposed method is an effective crop disease and pest forecasting method that can provide a technical support for preventing cotton disease and insect pests.     

AN ON-LINE ALGORITHM FOR PREDICTING THE TEMPERATURE OF POWER TRANSFORMER WINDINGS

The paper presents an on-line algorithm for predicting the average temperature of power transformer winding according to its load current. The algorithm has accounted for the whole history of load current and variations of ambient temperature. The overload capabilities of transformer can be increased by monitoring and controlling the winding temperature and the necessary durability of the transformer can be ensured. The algorithm can also forecast the future winding temperature and the time of reaching the max, permitted the assumed future load current and oil temperature.     

响应面优化柑桔皮黄色素微波辅助提取工艺

利用微波萃取技术对柑桔皮黄色素的最佳提取工艺进行了研究,采用单因素试验研究乙醇浓度、微波功率、微波处理时间、料液比、粒度对吸光度的影响,在单因素试验基础上,设计三因素三水平的响应面分析方法对微波提取橘皮黄色素工艺进行优化,建立二次多项式回归方程的预测模型,所得微波提取桔皮黄色素的最佳参数为：桔皮粉粒度为80目（0.198 mm）,料液比为1:15（g/mL）,乙醇浓度88%,微波时间500 s,微波功率250 W,在此条件下柑桔皮黄色素得率为14.12%。得到的色素产品为黄色,含有类胡萝卜素和黄酮类物质。     

Data Analysis Method for Neural Network Based on Short-term Load Forecasting

In neural network based short-term load forecasting, complexity and redundancy of input data have a negative effect on network training efficiency and forecasting precision. Focusing on solving this problem, a multiple method of data processing is developed. Firstly a method called input variable contribution analysis is applied, which divides input variables into primary variables and minor variables according to their contribution to network output. Minor variables are tossed out. Then principal component analysis is applied to primary variables to eliminate linear correlation among them, thus reduce the variable dimension. Based on this method, the main components are gotten, and then simplified network structure is designed. The result shows that after data processing, the training time is reduced noticeably and forecasting precision is enhanced.