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2009中国控制与决策会议

作者： Zhiping Fan~(1,2),Zhong Qin~3,Tiansheng Hong~2,Yufei zhuang~1 1.College of Computer Science & Educational Software,Guangzhou University,Guangzhou,Guangdong,510006,China 2.School of Engineering,South China Agricultural University,Guangzhou,Guangdong,510642,China 3.School of Civil Engineering,Guangzhou University,Guangzhou,Guangzhou,China,510006

＜正＞The load forecasting method usually starts from a single method,we usually improved prediction methods to get the better forecasting accuracy,but this often confined to the application of the method,combination forecasting method can achieve superiority of various methods,the forecast accuracy is higher than single forecasting *** this paper,we used RBF neural network prediction method and support vector machine forecasting *** neural network prediction method is the more popular method in recent years,it has the better generalization ability to the traditional neural network prediction method,It can effectively avoid local minima value and has a very good learning ability;S VM prediction method is transformed into one-dimensional nonlinear prediction of linear space,it has very precise calculation process and can meet the high forecast *** on the combination of the two methods,not only from the Angle of artificial memory model prediction,and using the tight nonlinear model,ultimately meet the purpose of combined *** main innovation in this paper is that assess the result of every kind of prediction method by making the standards of error qualified,using the error rate to determine the weight of combination,finally,we can get the satisfactory results through an empirical analysis.

关键词： Combined weight RBF network SVM method power load

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load Forecasting of the power System: An Investigation Based on the Method of Random Forest Regression

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Energy Engineering 2021年第6期118卷 1703-1712页

作者： Fuyun Zhu Guoqing Wu College of Mechanical and Electrical Engineering Soochow UniversitySuzhou215137China

Accurate power load forecasting plays an important role in the power dispatching and security of *** this paper,a mathematical model for power load forecasting based on the random forest regression(RFR)was *** input parameters of RFR model were determined by means of the grid search *** prediction results for this model were compared with those for several other common machine learning *** was found that the coefficient of determination(R^(2))of test set based on the RFR model was the highest,reaching 0.514 while the corresponding mean absolute error(MAE)and the mean squared error(MSE)were the ***,the impacts of the air conditioning system used in summer on the power load were *** calculation results showed that the introduction of indexes in the field of Heating,Ventilation and Air Conditioning(HVAC)could improve the prediction accuracy of test set.

关键词： Mathematical model machine learning power load HVAC

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Impact of the cold surge event in January 2021 on the power system of China

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Advances in Climate Change Research 2022年第6期13卷 803-810页

作者： Chang-Yi LIU Bo LU Lu JIN Hao CHEN Jie WU Yang XIANG Zhi-Yuan MA Zi-Jian ZHAO Fang YANG Qing LIU Jiang-Tao LI Global Energy Interconnection Development and Cooperation Organization(GEIDCO) Beijing 100031China National Climate Centre China Meteorological AdministrationBeijing 100081China Renmin University of Chin1 Beijing 100872China State Grid Energy Research Institute Co. Ltd.Beijing 102211China

During 6-10 January 2021,a recorded strong cold surge took place in China,with over 800 observational stations reaching their historical *** previous studies that focused on the response of either the power load or generation separately,this study quantitatively revealed the impacts on the balance between the demand and supply sides of the *** the demand side,the sensitivity of power load was found to increase substantially higher in southern China(0.533 GW°C^(−1))than in the northern region(0.139 GW°C^(−1))due to the limited municipal heating *** the supply side,the hourly wind power generation dropped from the highest of 110 GW on 6 January to the lowest of 54 GW on 9 January due to the reduction in wind *** addition,a reduction in solar power generation was observed during 8-10 ***,the balance of the power system was influenced by this cold *** an effective adaptation measure,results further showed that early warning by three weeks ahead can be obtained by an operational climate *** sensitivity of China's power system to such cold surge events may increase remarkably due to the expected increase in the proportion of wind and solar power generation in future new-type power ***,close cooperation between climate scientists and power engineers is needed to build the resilience of the power system to the cold extremes.

关键词： Cold surge event power load Renewable power generation Sub-seasonal prediction Adaptation

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Three-Phase Unbalance Prediction of Electric power Based on Hierarchical Temporal Memory

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Computers, Materials & Continua 2020年第8期64卷 987-1004页

作者： Hui Li Cailin Shi Xin Liu Aziguli Wulamu Alan Yang School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing100083China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083China Amphenol Assemble Tech HoustonTX 77070USA

The difference in electricity and power usage time leads to an unbalanced current among the three phases in the power *** three-phase unbalanced is closely related to power planning and load *** the unbalance occurs,the safe operation of the electrical equipment will be seriously *** paper proposes a Hierarchical Temporal Memory(HTM)-based three-phase unbalance prediction model consisted by the encoder for binary coding,the spatial pooler for frequency pattern learning,the temporal pooler for pattern sequence learning,and the sparse distributed representations classifier for unbalance *** the feasibility of spatial-temporal streaming data analysis,we adopted this brain-liked neural network to a real-time prediction for power *** applied the model in five cities(Tangshan,Langfang,Qinhuangdao,Chengde,Zhangjiakou)of north *** experimented with the proposed model and Long Short-term Memory(LSTM)model and analyzed the predict results and real *** results show that the predictions conform to the reality;compared to LSTM,the HTM-based prediction model shows enhanced accuracy and *** prediction model could serve for the overload warning and the load planning to provide high-quality power grid operation.

关键词： Three-phase unbalance power load prediction model hierarchical temporal memory

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Probe manipulators for Wendelstein 7-X and their interaction with the magnetic topology

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Plasma Science and Technology 2018年第5期20卷 6-17页

作者： M RACK D HOSCHEN D REITER B UNTERBERG J W COENEN S BREZINSEK O NEUBAUER S BOZHENKOV G CZYMEK Y LIANG M HUBENY Ch LINSMEIER the Wendelstein 7-X Team Forschungszentrum Julich GmbH Institut für Energie- und Klimaforschung-Plasmaphysik Partner of the Trilateral Euregio Cluster (TEC) D-52425 Julich Germany JARA-HPC D-52425 Julich Germany Max-Planck-Institut für Plasmaphysik D-17491 Greifswald/D-85748 Garching Germany

Probe manipulators are a versatile addition to typical plasma edge *** with material samples they allow for detailed investigation of plasma–wall interaction processes,such as material erosion,deposition or impurity transport *** combined with electrical probes,a study of scrape-off layer and plasma edge density,temperature and flow profiles as well as magnetic topologies is possible.A mid-plane manipulator is already in operation on Wendelstein 7-X.A system in the divertor region is currently under *** the present paper we discuss the critical issue of heat and power loads,power redistribution and experimental access to the complex magnetic topology of Wendelstein *** the aforementioned aspects are of relevance for the design and operation of a probe manipulator in a device like Wendelstein？7-X.A focus is put on the topological region that is accessible for the different coil current configurations at Wendelstein 7-X and the power load on the manipulator with respect to the resulting different magnetic *** analysis of power loads on plasma-facing components is performed using a numerical tracer particle diffusion tool provided via the Wendelstein 7-X Webservices.

关键词： stellarators Wendelstein 7-X manipulator divertor power load

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Short Term load Forecasting Using Subset Threshold Auto Regressive Model

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Journal of Southeast University(English Edition) 1999年第2期15卷 78-83页

作者：孙海健东南大学电气工程系南京210096

The subset threshold auto regressive (SSTAR) model, which is capable of reproducing the limit cycle behavior of nonlinear time series, is introduced. The algorithm for fitting the sampled data with SSTAR model is proposed and applied to model and forecast power load. Numerical example verifies that desirable accuracy of short term load forecasting can be achieved by using the SSTAR model.

关键词： power load forecasting subset threshold auto regressive model

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